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The self-incompatibility locus (S-locus) of flowering plants displays a striking allelic diversity. How such a diversity has emerged remains unclear. In this paper, we performed numerical simulations in a finite island population genetics model to investigate how population subdivision affects the diversification process at a S-locus, given that the two-genes architecture typical of S-loci involves the crossing of a fitness valley. We show that population structure slightly reduces the parameter range allowing for the diversification of self-incompatibility haplotypes (S-haplotypes), but at the same time also increases the number of these haplotypes maintained in the whole metapopulation. This increase is partly due to a higher rate of diversification and replacement of S-haplotypes within and among demes. We also show that the two-genes architecture leads to a higher diversity in structured populations compared with a simpler genetic architecture where new S-haplotypes appear in a single mutation step. Overall, our results suggest that population subdivision can act in two opposite directions: it renders S-haplotypes diversification easier, although it also increases the risk that the self-incompatibility system is lost.

Direct policy search (DPS) is emerging as one of the most effective and widely applied reinforcement learning (RL) methods to design optimal control policies for multiobjective Markov decision processes (MOMDPs). Traditionally, DPS defines the control policy within a preselected functional class and searches its optimal parameterization with respect to a given set of objectives. The functional class should be tailored to the problem at hand and its selection is crucial, as it determines the search space within which solutions can be found. In MOMDPs problems, a different objective tradeoff determines a different fitness landscape, requiring a tradeoff-dynamic functional class selection. Yet, in state-of-the-art applications, the policy class is generally selected a priori and kept constant across the multidimensional objective space. In this work, we present a novel policy search routine called neuro-evolutionary multiobjective DPS (NEMODPS), which extends the DPS problem formulation to conjunctively search the policy functional class and its parameterization in a hyperspace containing policy architectures and coefficients. NEMODPS begins with a population of minimally structured approximating networks and progressively builds more sophisticated architectures by topological and parametrical mutation and crossover, and selection of the fittest individuals concerning multiple objectives. We tested NEMODPS for the problem of designing the control policy of a multipurpose water system. Numerical results show that the tradeoff-dynamic structural and parametrical policy search of NEMODPS is consistent across multiple runs, and outperforms the solutions designed via traditional DPS with predefined policy topologies.

In this paper, we first propose a graph neural network encoding method for multiobjective evolutionary algorithm to handle the community detection problem in complex attribute networks. In the graph neural network encoding method, each edge in an attribute network is associated with a continuous variable. Through non-linear transformation, a continuous valued vector (i.e. a concatenation of the continuous variables associated with the edges) is transferred to a discrete valued community grouping solution. Further, two objective functions for single- and multi-attribute network are proposed to evaluate the attribute homogeneity of the nodes in communities, respectively. Based on the new encoding method and the two objectives, a multiobjective evolutionary algorithm (MOEA) based upon NSGA-II, termed as continuous encoding MOEA, is developed for the transformed community detection problem with continuous decision variables. Experimental results on single- and multi-attribute networks with different types show that the developed algorithm performs significantly better than some well-known evolutionary and non-evolutionary based algorithms. The fitness landscape analysis verifies that the transformed community detection problems have smoother landscapes than those of the original problems, which justifies the effectiveness of the proposed graph neural network encoding method.

One of the key points of machine learning-assisted directed evolution (MLDE) is the accurate learning of the fitness landscape, a conceptual mapping from sequence variants to the desired function. Here, we describe a multi-protein training scheme to leverage the existing deep mutational scanning data from different proteins to help reason about the fitness landscape of a new protein. Proof-of-concept trials are designed to validate this training scheme in three aspects: random and positional extrapolation for single variant effects, zero-shot fitness predictions for new proteins, and extrapolation for higher-order variant effects from single variant effects. Moreover, our study identified previously overlooked strong baselines, and their unexpectedly good performance bring our attention to the pitfalls of MLDE. Overall, these results may improve our understanding of the association between different protein fitness profiles, and shed light on developing better machine learning-assisted approaches to the directed evolution of proteins. Two zipped files were deposited here. "directed_evolution_input_datasets.tar.gz" contains the processed DMS datasets and "zero-shot_parameter.tar.gz" contains our pretrained model parameters for zero-shot prediction.

International audience; Recent work has shown that evaluating functional trait distinctiveness, the average trait distance of a species to other species in a community offers promising insights into biodiversity dynamics and ecosystem functioning. However, the ecological mechanisms underlying the emergence and persistence of functionally distinct species are poorly understood. Here, we address the issue by considering a heterogeneous fitness landscape whereby functional dimensions encompass peaks representing trait combinations yielding positive population growth rates in a community. We identify four ecological cases contributing to the emergence and persistence of functionally distinct species. First, environmental heterogeneity or alternative phenotypic designs can drive positive population growth of functionally distinct species. Second, sink populations with negative population growth can deviate from local fitness peaks and be functionally distinct. Third, species found at the margin of the fitness landscape can persist but be functionally distinct. Fourth, biotic interactions (positive or negative) can dynamically alter the fitness landscape. We offer examples of these four cases and guidelines to distinguish between them. In addition to these deterministic processes, we explore how stochastic dispersal limitation can yield functional distinctiveness. Our framework offers a novel perspective on the relationship between fitness landscape heterogeneity and the functional composition of ecological assemblages.

Evolutionary multitasking (EMT) is a newly emerging research topic in the community of evolutionary computation, which aims to improve the convergence characteristic across multiple distinct optimization tasks simultaneously by triggering knowledge transfer among them. Unfortunately, most of the existing EMT algorithms are only capable of boosting the optimization performance for homogeneous problems which explicitly share the same (or similar) fitness landscapes. Seldom efforts have been devoted to generalize the EMT for solving heterogeneous problems. A few preliminary studies employ domain adaptation techniques to enhance the transferability between two distinct tasks. However, almost all of these methods encounter a severe issue which is the so-called degradation of intertask mapping. Keeping this in mind, a novel rank loss function for acquiring a superior intertask mapping is proposed in this article. In particular, with an evolutionary-path-based representation model for optimization instance, an analytical solution of affine transformation for bridging the gap between two distinct problems is mathematically derived from the proposed rank loss function. It is worth mentioning that the proposed mapping-based transferability enhancement technique can be seamlessly embedded into an EMT paradigm. Finally, the efficacy of our proposed method against several state-of-the-art EMTs is verified experimentally on a number of synthetic multitasking and many-tasking benchmark problems, as well as a practical case study.

Members of the Fluc family of membrane channel proteins, found in all three domains of biological classification, allow organisms to prevent the buildup of fluoride ion inside cells and thereby counteract fluoride toxicity. Export through these proteins is passive yet extremely selective for the substrate. Like nearly all membrane proteins, Fluc protomers exhibit internal repeat symmetry, which is thought to result from either the association of structurally similar domains or gene duplication. Gene duplication events are of special significance to the Fluc family because duplication is considered the earliest mechanism of major genetic variation that allowed for topologically distinct Fluc channels to evolve. In prokaryotes, the Fluc channels assemble as proper dimers and may have a dual topology (monomer is equally capable of insertion in either of two orientations with respect to the membrane) or a fixed topology (monomer is biased towards one orientation). Fixed-topology channels are obligate Fluc heterodimers thought to have arisen due to sequence divergence in the two copies of Fluc-encoding gene following a duplication event. In eukaryotes, the homologous FEX family proteins are monomeric, but retain a pseudosymmetry suggestive of a gene fusion event in an ancestor, joining two previously separate copies of Fluc-encoding gene. Accordingly, the dual-topology state is considered the ancestral phenotype; the more subfunctionalized phenotypes evolved later. Multiple independent gene duplications in the Fluc family have been retained over evolutionary time, and the Fluc/FEX proteins as a whole are thought to have undergone a general, three-step evolutionary trajectory: (1) gene duplication, (2) sequence divergence, and (3) gene fusion. With attention to ease of access and other principles of software development, the present work develops computational tools using Python and R for two purposes: evaluating the bias in membrane protein orientation for each member of a diverse set of prokaryotic Fluc homologs and quantifying mutational tolerance in Fluc residues from computed statistical correlations between likely pairs of co-evolving residues. For the latter of these ends, we apply the validated computational framework EVcouplings to Fluc-Bpe, the dual-topology Fluc channel found in Bordetella pertussis whose structure and function have become well characterized in recent years. We generate a single-site substitution matrix that illustrates the effects of amino acid substitution for nearly the entirety of the Fluc-Bpe primary sequence and we describe means by which this result may be validated by experiment. Having estimated prima facie the mutational lability of the Fluc-Bpe sequence space, we consider paralog dynamics to posit a more detailed yet provisional narrative of the evolution of post-duplication Fluc topologies. We stress the importance of further studies in the elaboration and testing of this narrative, including efforts to chart the Fluc evolutionary pathway in phylogenetic terms. Finally, we draw attention to particular limitations of the adaptationist paradigm in evolutionary theory and we advise nuance in the modeling of sequence-fitness relationships using the fitness landscape metaphor that is prominently used throughout the discipline.

The phenotypic consequence of a genetic mutation depends on many factors including the expression level of a gene. However, a comprehensive quantification of this expression effect is still lacking, as is a further general mechanistic understanding of the effect. Here, we measured the fitness effect of almost all (>97.5%) single-nucleotide mutations in GFP, an exogenous gene with no physiological function, and URA3, a conditionally essential gene. Both genes were driven by two promoters whose expression levels differed by around tenfold. The resulting fitness landscapes revealed that the fitness effects of at least 42% of all single-nucleotide mutations within the genes were expression dependent. Although only a small fraction of variation in fitness effects among different mutations can be explained by biophysical properties of the protein and messenger RNA of the gene, our analyses revealed that the avoidance of stochastic molecular errors generally underlies the expression dependency of mutational effects and suggested protein misfolding as the most important type of molecular error among those examined. Our results therefore directly explained the slower evolution of highly expressed genes and highlighted cytotoxicity due to stochastic molecular errors as a non-negligible component for understanding the phenotypic consequence of mutations. Measuring fitness effects of mutations in the same gene expressed at two levels, the authors show expression dependency of fitness effects in at least 42% of all single-nucleotide mutations, probably driven by avoidance of stochastic molecular errors.

On results undertaken studies systematization is carried out and in theory - methodological positions are deep on this basis practical recommendations are offered in relation to basic directions of ecological optimization of the use of earth-resource potential erosive dangerous agrolandscape by working and development of theoretical and applied bases of the adaptive-landscape systems of agriculture, newest systems of land-tenure, aimed at the rational use and protection of agricultural land, differentiated adaptation of technological means to soil-landscape-climatic factors and economic conditions of modern agricultural.The research was aimed at identifying the latest approaches to the organization of modern land use, which will necessitate the development of farming and land use systems on an ecological and landscape basis, given the different levels of intensification and resource provision, which in turn requires the improvement of existing and development of new components and the whole complex of agricultural systems, in particular in the context of climate change, risks of water and wind erosion and desertification.Many years of domestic experience in developing the scientific basis for the organization and management of agricultural land use, taking into account the basic principles of the concept of sustainable development shows that land management design based on landscape-ecological approach to the organization of agricultural land use is quite capable of becoming a link. unified system of theory and practice of organization of ecologically balanced land use at all levels.The principles of designing the organization of the territory of erosively dangerous sloping agrolandscapes for the development of soil protection adaptive-landscape system of agriculture on sloping lands (theoretical and methodological principles of landscape-adaptive land management, methodological approaches to substantiate land management projects on a landscape-ecological basis).

One fundamental problem of protein biochemistry is to predict protein structure from amino acid sequence. The inverse problem, predicting either entire sequences or individual mutations that are consistent with a given protein structure, has received much less attention even though it has important applications in both protein engineering and evolutionary biology. Here, we ask whether 3D convolutional neural networks (3D CNNs) can learn the local fitness landscape of protein structure to reliably predict either the wild-type amino acid or the consensus in a multiple sequence alignment from the local structural context surrounding site of interest. We find that the network can predict wild type with good accuracy, and that network confidence is a reliable measure of whether a given prediction is likely going to be correct or not. Predictions of consensus are less accurate and are primarily driven by whether or not the consensus matches the wild type. Our work suggests that high-confidence mis-predictions of the wild type may identify sites that are primed for mutation and likely targets for protein engineering. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10867-021-09593-6.

Multiple mutations often have non-additive (epistatic) phenotypic effects. Epistasis is of fundamental biological relevance but is not well understood mechanistically. Adaptive evolution, i.e., the evolution of new biochemical activities, is rich in epistatic interactions. To better understand the principles underlying epistasis during genetic adaptation, we studied the evolution of TEM-1 β-lactamase variants exhibiting cefotaxime resistance. We report the collection of a library of 487 observed evolutionary trajectories for TEM-1 and determine the epistasis status based on cefotaxime resistance phenotype for 206 combinations of 2-3 TEM-1 mutations involving 17 positions under adaptive selective pressure. Gain-of-function (GOF) mutations are gatekeepers for adaptation. To see if GOF phenotypes can be inferred based solely on sequence data, we calculated the enrichment of GOF mutations in the different categories of epistatic pairs. Our results suggest that this is possible because GOF mutations are particularly enriched in sign and reciprocal sign epistasis, which leave a major imprint on the sequence space accessible to evolution. We also used FoldX to explore the relationship between thermodynamic stability and epistasis. We found that mutations in observed evolutionary trajectories tend to destabilize the folded structure of the protein, albeit their cumulative effects are consistently below the protein's free energy of folding. The destabilizing effect is stronger for epistatic pairs, suggesting that modest or local alterations in folding stability can modulate catalysis. Finally, we report a significant relationship between epistasis and the degree to which two protein positions are structurally and dynamically coupled, even in the absence of ligand.

Fitness landscapes are often described in terms of ‘peaks’ and ‘valleys’, implying an intuitive low-dimensional landscape of the kind encountered in everyday experience. The space of genotypes, however, is extremely high-dimensional, which results in counter-intuitive properties of genotype-phenotype maps, such as the close proximity of one phenotype to many others. Here we investigate how common structural properties of high-dimensional genotype-phenotype maps, such as the presence of neutral networks, affect the navigability of fitness landscapes. For three biologically realistic genotype-phenotype map models—RNA secondary structure, protein tertiary structure and protein complexes—we find that, even under random fitness assignment, fitness maxima can be reached from almost any other phenotype without passing through a fitness valley. This in turn implies that true fitness valleys are very rare. By considering evolutionary simulations between pairs of real examples of functional RNA sequences, we show that accessible paths are also likely to be utilised under evolutionary dynamics.

Fitness landscapes map genotypes to their corresponding fitness under given environments and allow explaining and predicting evolutionary trajectories. Of particular interest is the landscape ruggedness or the unevenness of the landscape, because it impacts many aspects of evolution such as the likelihood that a population is trapped in a local fitness peak. Although the ruggedness has been inferred from a number of empirically mapped fitness landscapes, it is unclear to what extent this inference is affected by fitness estimation error, which is inevitable in the experimental determination of fitness landscapes. Here we address this question by simulating fitness landscapes under various theoretical models, with or without fitness estimation error. We find that all eight examined measures of landscape ruggedness are overestimated due to imprecise fitness quantification, but different measures are affected to different degrees. We devise a method to use replicate fitness measures to correct this bias and show that our method performs well under realistic conditions. We conclude that previously reported fitness landscape ruggedness is likely upward biased owing to the negligence of fitness estimation error and advise that future fitness landscape mapping should include at least three biological replicates to permit an unbiased inference of the ruggedness. This article is protected by copyright. All rights reserved.

The fitness landscape is a theory applied to the evolutionary dynamics of biological evolution to explain the behavior of evolutionary algorithms in the solution of optimization problems. With the continuous advancement of evolutionary algorithm optimization, a fitness landscape can present more abundant feature information, such as the local fitness, fitness distance correlation, and landscape roughness. These landscape features reflect the optimal solution distribution, quantity, and local unimodal topology of the optimization problem from various angles. This paper expresses the adaptability landscape features of typical optimization problems, engages in a quantitative analysis of the fitness distance correlation information, evaluates the difficulty of solving the problem within the search space, and obtains the correlation degree classification result. The search strategy adapts the mixed mutation and the fitness distance correlation for differential evolution. Empirical studies show that, the fitness distance correlation search strategy for the differential evolution algorithm can avoid falling into the local optimum, improve accuracy and convergence, and solve the single-objective optimization problem in a more comprehensive manner.

Alcohol-forming fatty acyl reductases (FARs) catalyze the reduction of thioesters to alcohols and are key enzymes for microbial production of fatty alcohols. Many metabolic engineering strategies utilize FARs to produce fatty alcohols from intracellular acyl-CoA and acyl-ACP pools; however, enzyme activity, especially on acyl-ACPs, remains a significant bottleneck to high-flux production. Here, we engineer FARs with enhanced activity on acyl-ACP substrates by implementing a machine learning (ML)-driven approach to iteratively search the protein fitness landscape. Over the course of ten design-test-learn rounds, we engineer enzymes that produce over twofold more fatty alcohols than the starting natural sequences. We characterize the top sequence and show that it has an enhanced catalytic rate on palmitoyl-ACP. Finally, we analyze the sequence-function data to identify features, like the net charge near the substrate-binding site, that correlate with in vivo activity. This work demonstrates the power of ML to navigate the fitness landscape of traditionally difficult-to-engineer proteins., Fatty acyl reductases (FARs) are critical enzymes in the biosynthesis of fatty alcohols and have the ability to directly acces acyl-ACP substrates. Here, authors couple machine learning-based protein engineering framework with gene shuffling to optimize a FAR for the activity on acyl-ACP and improve fatty alcohol production.

Traditional approaches to the directed evolution of genes of interest (GOIs) place constraints on the scale of experimentation and depth of evolutionary search reasonably achieved. Engineered genetic systems that dramatically elevate the mutation of target GOIs in vivo relieve these constraints by enabling continuous evolution, affording new strategies in the exploration of sequence space and fitness landscapes for GOIs. We describe various in vivo hypermutation systems for continuous evolution, discuss how different architectures for in vivo hypermutation facilitate evolutionary search scale and depth in their application to problems in protein evolution and engineering, and outline future opportunities for the field.

Despite recent advances in high-throughput combinatorial mutagenesis assays, the number of labeled sequences available to predict molecular functions has remained small for the vastness of the sequence space combined with the ruggedness of many fitness functions. While deep neural networks (DNNs) can capture high-order epistatic interactions among the mutational sites, they tend to overfit to the small number of labeled sequences available for training. Here, we developed Epistatic Net (EN), a method for spectral regularization of DNNs that exploits evidence that epistatic interactions in many fitness functions are sparse. We built a scalable extension of EN, usable for larger sequences, which enables spectral regularization using fast sparse recovery algorithms informed by coding theory. Results on several biological landscapes show that EN consistently improves the prediction accuracy of DNNs and enables them to outperform competing models which assume other priors. EN estimates the higher-order epistatic interactions of DNNs trained on massive sequence spaces-a computational problem that otherwise takes years to solve., Finding a biologically-relevant inductive bias for training DNNs on large fitness landscapes is challenging. Here, the authors propose a method called Epistatic Net that improves DNN prediction accuracy and interpretation speed by integrating the knowledge that higher-order epistatic interactions are usually sparse.

Although how rare species persist in communities is a major ecological question, the critical phenotypic dimension of rarity is broadly overlooked. Recent work has shown that evaluating functional distinctiveness, the average trait distance of a species to other species in a community, offers essential insights into biodiversity dynamics, ecosystem functioning, and biological conservation. However, the ecological mechanisms underlying the persistence of functionally distinct species are poorly understood. Here we propose a heterogeneous fitness landscape framework, whereby functional dimensions encompass peaks representing trait combinations that yield positive intrinsic growth rates in a community. We identify four fundamental causes leading to the persistence of functionally distinct species in a community. First, environmental heterogeneity or alternative phenotypic designs can drive positive population growth of functionally distinct species. Second, sink populations with negative growth can deviate from local fitness peaks and be functionally distinct. Third, species found at the margin of the fitness landscape can persist but be functionally distinct. Fourth, biotic interactions (either positive or negative) can dynamically alter the fitness landscape. We offer examples of these four cases and some guidelines to distinguish among them. In addition to these deterministic processes, we also explore how stochastic dispersal limitation can yield functional distinctiveness.

Evolution is the continuous adaptation to ever changing environments and pressure for survival of all living species. Directed evolution attempts to mimic this process of natural selection of biological molecules and systems towards a specific function that would otherwise remain untapped by nature. For this purpose, it uses iterative rounds of mutation and artificial selection to improve existing or generate entirely new biomolecules. However, a series of stepwise improvements during the selection process can lead to local entrapments in the respective sequences and fitness, and thus abolish access to further phenotypic diversity. Multivalency is a strategy commonly applied by nature that has the potential to increase the strength of interaction and consequently the recovery and fitness of variants. As a result, this strategy may be able to support and tolerate mutations that are negative in one context but can become beneficial in others during the course of evolution. This thesis aims to characterize and investigate the effects of multivalency on the outcomes and intermediate states of evolution campaigns. To test the hypothesis of increased survival of variants by introduction of multivalency during the selection process, a new in vitro multivalent selection system was developed, that allowed for the display of proteins of interest under defined valencies. Using this system, different valency effects could be quantified and mapped. The observations of vastly improved recoveries and enrichments of variants by multivalent display suggests that the introduction of multivalency in a selection system can substantially alter the stringency of selection pressure on variants, while maintaining positive enrichment of functional variants during the course of a selection campaign. Based on this confirmed hypothesis, two main questions were addressed: Do identical naïve libraries, subjected to different evolutionary stringencies by multivalency, evolve a common set of amino acids and do these naïve libraries, submitted to different evolutionary stringencies, evolve different fitness optima? To address these questions, a new phage display system was developed that enabled the display of variants under a range of defined valencies and allowed for the creation of large (>109) naïve phage-libraries of Designed Ankyrin Repeat Proteins (DARPins). The resulting libraries were subjected to evolution campaigns and the evolved variants emerging from selection rounds, as well as intermediate states, analyzed to address the questions of whether differences in trajectories in sequence space towards fitness optima can be identified. A third section of this thesis focused on the effects of high selection pressure on the evolution of weak promiscuous activities. For these investigations, I evolved and characterized proteins with binding specificities towards a range of pathogenic human coronaviruses. As such, insight into the potential of occupied sequence space with overlapping fitness optima towards different distantly related targets for the subsequent evolution of broadly specific interactions could be obtained. The tools established and insights obtained in this thesis can provide an improved understanding of the effects of multivalency and selection stringency on the outcomes and intermediate states of evolution campaigns. As such, these novel insights contribute to a more comprehensive understanding of the underlying principles, that may govern the outcome of directed evolution campaigns of proteins., European Commission Horizon 2020 (H2020), Marie Skłodowska-Curie Actions (722610)

Biological populations may survive lethal environmental stress through evolutionary rescue. The rescued populations typically suffer a reduction in growth performance and harbor very low genetic diversity compared with their parental populations. The present study addresses how population size and within‐population diversity may recover through compensatory evolution, using the experimental adaptive radiation of bacterium Pseudomonas fluorescens. We exposed bacterial populations to an antibiotic treatment and then imposed a one‐individual‐size population bottleneck on those surviving the antibiotic stress. During the subsequent compensatory evolution, population size increased and leveled off very rapidly. The increase of diversity was of slower paces and persisted longer. In the very early stage of compensatory evolution, populations of large sizes had a greater chance to diversify; however, this productivity–diversification relationship was not observed in later stages. Population size and diversity from the end of the compensatory evolution was not contingent on initial population growth performance. We discussed the possibility that our results be explained by the emergence of a “holey” fitness landscape under the antibiotic stress., Bacterial populations lost diversity and showed reduced growth performance after an evolutionary rescue from environmental stress. During the subsequent compensatory adaptation, improvement of growth performance preceded ecological diversification, and the final population size and diversity were not contingent on initial population growth performance. The mode and pace of adaptation and diversification may be explained by a holey fitness landscape under environmental stress.

International audience; Understanding the landscape underlying NK models is of fundamental interest. Different representations have been proposed to better understand how the ruggedness of the landscape is influenced by the model parameters, such as the problem dimension, the degree of non-linearity and the structure of variable interactions. In this paper, we propose to use neural embedding, that is a continuous vectorial representation obtained as a result of applying a neural network to a prediction task, in order to investigate the characteristics of NK landscapes. The main assumption is that neural embeddings are able to capture important features that reflect the difficulty of the landscape. We propose a method for constructing NK embeddings, together with metrics for evaluating to what extent this embedding space encodes valuable information from the original NK landscape. Furthermore, we study how the embedding dimensionality and the parameters of the NK model influence the characteristics of the NK embedding space. Finally, we evaluate the performance of optimizers that solve the continuous representations of NK models by searching for solutions in the embedding space.

During the struggle for survival, populations occasionally evolve new functions that give them access to untapped ecological opportunities. Theory suggests that coevolution between species can promote the evolution of such innovations by deforming fitness landscapes in ways that open new adaptive pathways. We directly tested this idea by using high-throughput gene editing-phenotyping technology (MAGE-Seq) to measure the fitness landscape of a virus, bacteriophage λ, as it coevolved with its host, the bacterium Escherichia coli. An analysis of the empirical fitness landscape revealed mutation-by-mutation-by-host-genotype interactions that demonstrate coevolution modified the contours of λ’s landscape. Computer simulations of λ’s evolution on a static versus shifting fitness landscape showed that the changes in contours increased λ’s chances of evolving the ability to use a new host receptor. By coupling sequencing and pairwise competition experiments, we demonstrated that the first mutation λ evolved en route to the innovation would only evolve in the presence of the ancestral host, whereas later steps in λ’s evolution required the shift to a resistant host. When time-shift replays of the coevolution experiment were run where host evolution was artificially accelerated, λ did not innovate to use the new receptor. This study provides direct evidence for the role of coevolution in driving evolutionary novelty and provides a quantitative framework for predicting evolution in coevolving ecological communities.

The “RNA World Hypothesis” posits that extant life was likely preceded by a simpler primordial biology lacking DNA and proteins, relying instead on RNA for both heredity and catalysis. An essential component of an RNA world scenario would be an RNA “replicase” – a ribozyme capable of self-replication as well as copying other RNA sequences. While such a replicase has not been found in nature, past work in our laboratory identified artificial ribozymes that are promising candidates for further engineering towards self-replication. One such example is the t5+1 RNA polymerase ribozyme, which polymerises triplets to produce the complementary strand of a given RNA template; while incapable of self-synthesis in its entirety, t5+1 can copy the sequence of its catalytic subunit if provided in 5 separate segments. In Chapter 2 of this thesis, I describe in vitro selection methods to further evolve the t5+1 RNA polymerase ribozyme, identifying mutations that improve its catalytic activity, while also mapping the comprehensive fitness landscape of the ribozyme. In Chapter 3, I explore the potential of appending novel domains to the ribozyme in order to increase its contacts upstream of the triplet ligation junction. Using directed evolution, I identify new 5’ domains which when grafted onto t5+1 alter its triplet substrate preference, leading to improved polymerisation activity on some RNA templates that were previously difficult to copy. The invention of translation paved the transition away from the RNA world, providing a method for the coded-synthesis of proteins. In modern cells, a key player in translation is the ribosome, a large macromolecular machine that reads messenger RNAs in a triplet register, converting each codon into the correct amino acid. In Escherichia coli, RNA comprises two-thirds of the total mass of the ribosome, in the form of the 16S, 23S and 5S ribosomal RNAs (rRNAs). Despite the importance of the bacterial ribosome in biotechnological applications and as a target for antibiotics, most rRNA mutations have not been characterised for their effects on translation. In Chapter 4, I develop a novel system for the high-throughput quantification of mutational effects in the 23S rRNA of E. coli; I apply this system to map the higher-order fitness landscape of a short rRNA segment that can be read by short-read Next Generation Sequencing (NGS). In Chapter 5, I describe a new cloning method that enables the identification of mutations anywhere in the 23S rRNA through sequencing of a short 20nt barcode. Using this method, I map the fitness of all 23S rRNA point mutants., Herchel Smith Fund

Adaptive landscapes are a common way of conceptualizing the phenotypic evolution of lineages across deep time. Although multiple approaches exist to implement this concept into operational models of trait evolution, inferring adaptive landscapes from comparative datasets remains challenging. Here, I explore the macroevolutionary dynamics of echinoid body size using data from over 5000 specimens and a phylogenetic framework incorporating a dense fossil sampling and spanning approximately 270 million years. Furthermore, I implement a novel approach of exploring alternative parameterizations of adaptive landscapes that succeeds in finding simpler, yet better-fitting models. Echinoid body size has been constrained to evolve within a single adaptive optimum for much of the clade's history. However, most of the morphological disparity of echinoids was generated by multiple regime shifts that drove the repeated evolution of miniaturized and gigantic forms. Events of body size innovation occurred predominantly in the Late Cretaceous and were followed by a drastic slowdown following the Cretaceous-Paleogene mass extinction. The discovery of these patterns is contingent upon directly sampling fossil taxa. The macroevolution of echinoid body size is therefore characterized by a late increase in disparity (likely linked to an expansion of ecospace), generated by active processes driving lineages toward extreme morphologies.

The substantiation of an adaptive planting system for the natural frame of Voronezh is presented. A forest-park green belt has been identified, the components of which make up a single natural ecological system. It is indicated that the oak forests of the urban district of Voronezh occupy an area of 4500 hectares. Using the indicators of the normal head water level, average width, average depth, length of the coastline, regulation coefficient, the morphometric characteristics and standard head water levels of the Voronezh reservoir were determined. On the basis of functional zoning in the Voronezh reservoir, three zones are identified and their characteristics are given. It was identified that the forest cover of the Central Chernozem region is 9 %, which is lower than the normative indicators of forest cover for creating the natural frame in Voronezh. It is shown that the scientific objects of the Educational and Experimental Forestry Enterprise of the Voronezh State Forestry University (VGLTU) for the creation of forest cultures of the main forest-forming species in the Central Chernozem Region confirm the concept of creating adaptive landscaping systems for the natural frame of the city of Voronezh. 140 species in the genus of birch have been identified, which can be used for adaptive landscaping systems in the area under consideration. The stated description of the Great Voronezh Ecological Trail (BVET) along the entire route, characterized by the presence of unique natural landscape, botanical, geological, garden and park objects. An upward trend in the indicator of recreation of adaptive landscaping systems from 31,904.3 hectares was recorded up to 32421.2 hectares in 2020. It is noted that the climatic and natural conditions of this territory are favorable for growing an expanded range of tree species and shrubs, primarily the main forest-forming species.

BackgroundAsexually reproducing populations of single cells evolve through mutation, natural selection, and genetic drift. Environmental conditions in which the evolution takes place define the emergent fitness landscapes. In this work, we used Avida—a digital evolution framework—to uncover a hitherto unexplored interaction between mutation rates, population size, and the relative abundance of metabolizable resources, and its effect on evolutionary outcomes in small populations of digital organisms.ResultsOver each simulation, the population evolved to one of several states, each associated with a single dominant phenotype with its associated fitness and genotype. For a low mutation rate, acquisition of fitness by organisms was accompanied with, and dependent on, an increase in rate of genomic replication. At an increased mutation rate, phenotypes with high fitness values were similarly achieved through enhanced genome replication rates. In addition, we also observed the frequent emergence of suboptimal fitness phenotype, wherein neighboring organisms signaled to each other information relevant to performing metabolic tasks. This metabolic signaling was vital to fitness acquisition and was correlated with greater genotypic and phenotypic heterogeneity in the population. The frequency of appearance of signaling populations increased with population size and with resource abundance.ConclusionsOur results reveal a minimal set of environment–genotype interactions that lead to the emergence of metabolic signaling within evolving populations.

For real parameter single objective optimization, Differential Evolution (DE) performs better than other types of population-based metaheuristic. Nevertheless, in the field of DE, it is impossible for a given combination of operators to perform well in all fitness landscapes. Practice has proved that assembling more than one combinations of operators may lead to improvement in solution. However, some DE algorithms with good performance, e.g., one of the best performers in competitions of real parameter single objective optimization among population-based metaheuristics held by the series of IEEE Congress on Evolutionary Computation - L-SHADE-EpSin, are still not involved in ensemble. Furthermore, ensemble based on similar DE algorithms is rarely reported. Based on such a background, we propose L-SHADE-E, ensemble of L-SHADE-EpSin and L-SHADE-RSP in this paper. The two constituent algorithms are both variants of L-SHADE and belong to similar DE algorithms. In our algorithm, L-SHADE-EpSin or L-SHADE-RSP is selected randomly to run at the beginning. If progress on fitness is low for generations, the other constituent algorithm takes over population immediately. Our experiments are based on the CEC 2014 and CEC 2017 benchmark test suites. Altogether, our algorithm is compared with nine DE algorithms and three population-based metaheuristics other than DE. Experimental results show that our algorithm is very competitive.

The evolution of resistance has been seen across all major classes of xenobiotics, including antimicrobial drugs and agricultural pesticides. This repeated emergence of resistance is a case of phenotypic parallel evolution, but often the parallelism extends to the molecular level too, with multiple species gaining the same mutation in response to the same chemical treatment. We review the degree of repeatability in target‐site resistance mutations affecting different classes of site‐specific agricultural fungicides used in crop protection, comparing the extent to which resistance in different pathogen species has evolved via the same or different mutations. For all major fungicide target sites, substantial levels of molecular parallel evolution can be seen, with at least one mutation recurring in over 50% of species. Target‐site mutations appear to be most repeatable in cytochrome b, target site of quinone‐outside inhibitor fungicides, and least predictable for CYP51, target site of the azoles. Intermediate levels of repeatability are seen for the MBC target site β‐tubulin, and the SDHI target site succinate dehydrogenase. Repeatability may be lower where there are selective trade‐offs between resistance and pleiotropic fitness penalties, or differing levels of cross‐resistance across members of a fungicide class; or where single mutations confer only partial resistance, and epistatic interactions between multiple mutations result in a rugged fitness landscape. This affects the predictive power of in vitro mutation studies, and has practical implications for resistance monitoring strategies and diagnostic methods.

Animals alter their habitat use in response to the energetic demands of movement ('energy landscapes') and the risk of predation ('the landscape of fear'). Recent research suggests that animals also select habitats and move in ways that minimise their chance of temporarily losing control of movement and thereby suffering slips, falls, collisions or other accidents, particularly when the consequences are likely to be severe (resulting in injury or death). We propose that animals respond to the costs of an 'accident landscape' in conjunction with predation risk and energetic costs when deciding when, where, and how to move in their daily lives. We develop a novel theoretical framework describing how features of physical landscapes interact with animal size, morphology, and behaviour to affect the risk and severity of accidents, and predict how accident risk might interact with predation risk and energetic costs to dictate movement decisions across the physical landscape. Future research should focus on testing the hypotheses presented here for different real-world systems to gain insight into the relative importance of theorised effects in the field.

Developing the wind energy industry (WEI) has been a long-term strategy in China for responding to the energy crisis and greenhouse gas emissions. However, China’s WEI faces various barriers despite its favorable prospects. This paper aims to propose a path analysis approach for exploring the best methods and timing for overcoming diverse barriers in developing WEI systematically, especially in terms of revealing in which phases these barriers should take priority. A multidimensional barrier set categorized by institutional, economic and financial, social, technical, and market factors is identified via the integration of a literature review and empirical interviews. A path analysis approach based on the grey group DEMATEL-NK model is constructed, where the causalities and intensities among barriers are extracted to design a path simulation algorithm with heuristics. The optimal path for overcoming barriers in developing the WEI is generated through a simulation of the search and optimization process used to climb to the fitness landscape peak. The results show that the different barriers to completing the legal and regulatory framework should be prioritized to reach adequate financial incentives and coordination among stakeholders, and that an efficient talent cultivation system should be developed and industry-academic cooperation should be strengthened to increase technical and R&D capabilities in the short term. The optimal path presents a strategic instrument for managers to use to better develop sustainable and clean wind energy systems from the novel insights of prioritizing to overcome barriers.

In recent years, many different differential evolution (DE) variants have been proposed to solve real-world optimization problems. However, the performance of them is largely determined by the selection of the mutation strategy, an approach to choose favorable mutation strategy when solving various optimization problems has attracted increasing attention recently. In this paper, we propose a DE with an adaptive mutation operator based on fitness landscape (FLDE). The application of fitness landscape to DE requires three stages. First, we analyzed the fitness landscape features of each benchmark training function, a total of 45 benchmark functions are taken from CEC2014 and 2015. Then, the relationship between three mutation strategies and fitness landscape features is trained by random forest (RF) offline. Finally, the trained RF is used to predict which mutation strategy should be utilized to perform mutation operator for each problem during the evolutionary process. Besides, a historical memory parameter adaption mechanism and population size linear reduction are applied to the FLDE. The CEC2017 benchmark set is utilized to perform the experiments, and five well-known DE variant algorithms are compared with the FLDE algorithm. The experimental results attest that the proposed FLDE algorithm is highly competitive with the other five DE algorithms.

Self-cleaving ribozymes are genetic elements found in all domains of life, but their evolution remains poorly understood. A ribozyme located in the second intron of the cytoplasmic polyadenylation binding protein 3 gene (CPEB3) shows high sequence conservation in mammals, but little is known about the functional conservation of self-cleaving ribozyme activity across the mammalian tree of life or during the course of mammalian evolution. Here, we use a phylogenetic approach to design a mutational library and a deep sequencing assay to evaluate the in vitro self-cleavage activity of numerous extant and resurrected CPEB3 ribozymes that span over 100 My of mammalian evolution. We found that the predicted sequence at the divergence of placentals and marsupials is highly active, and this activity has been conserved in most lineages. A reduction in ribozyme activity appears to have occurred multiple different times throughout the mammalian tree of life. The in vitro activity data allow an evaluation of the predicted mutational pathways leading to extant ribozyme as well as the mutational landscape surrounding these ribozymes. The results demonstrate that in addition to sequence conservation, the self-cleavage activity of the CPEB3 ribozyme has persisted over millions of years of mammalian evolution.

NeuroEvolution (NE) refers to a family of methods for optimizing Artificial Neural Networks (ANNs) using Evolutionary Computation (EC) algorithms. NeuroEvolution of Augmenting Topologies (NEAT) is considered one of the most influential algorithms in the field. Eighteen years after its invention, a plethora of methods have been proposed that extend NEAT in different aspects. In this article, we present a systematic literature review (SLR) to list and categorize the methods succeeding NEAT. Our review protocol identified 232 papers by merging the findings of two major electronic databases. Applying criteria that determine the paper's relevance and assess its quality, resulted in 61 methods that are presented in this article. Our review article proposes a new categorization scheme of NEAT's successors into three clusters. NEAT-based methods are categorized based on 1) whether they consider issues specific to the search space or the fitness landscape, 2) whether they combine principles from NE and another domain, or 3) the particular properties of the evolved ANNs. The clustering supports researchers 1) understanding the current state of the art that will enable them, 2) exploring new research directions or 3) benchmarking their proposed method to the state of the art, if they are interested in comparing, and 4) positioning themselves in the domain or 5) selecting a method that is most appropriate for their problem.

The inherent difficulty of solving a continuous, static, bound-constrained and single-objective black-box optimization problem depends on the characteristics of the problem’s fitness landscape and the algorithm being used. Exploratory landscape analysis (ELA) uses numerical features generated via a sampling process of the search space to describe such characteristics. Despite their success in a number of applications, these features have limitations related with the computational costs associated with generating accurate results. Consequently, only approximations are available in practice which may be unreliable, leading to systemic errors. The overarching aim of this paper is to evaluate the reliability of five well-known ELA feature sets across multiple dimensions and sample sizes. For this purpose, we propose a comprehensive experimental methodology combining exploratory and statistical validation stages, which uses resampling techniques to minimize the sampling cost, and statistical significance tests to identify strengths and weaknesses of individual features. The data resulting from the methodology is collected and made available in the LEarning and OPtimization Archive of Research Data v1.0. The results show that instances of the same function can have feature values that are significantly different; hence, non-generalizable across instances, due to the effects produced by the boundary constraints. In addition, some landscape features under evaluation are highly volatile, and strongly susceptible to changes in sample size. Finally, the results show evidence of a curse of modality, meaning that the sample size should increase with the number of local optima.

Driven by host–pathogen coevolution, cell surface antigens are often the fastest evolving parts of a microbial pathogen. The persistent evolutionary impetus for novel antigen variants suggests the utility of novelty-seeking algorithms in predicting antigen diversification in microbial pathogens. In contrast to traditional genetic algorithms maximizing variant fitness, novelty-seeking algorithms optimize variant novelty. Here, we designed and implemented three evolutionary algorithms (fitness-seeking, novelty-seeking, and hybrid) and evaluated their performances in 10 simulated and 2 empirically derived antigen fitness landscapes. The hybrid walks combining fitness- and novelty-seeking strategies overcame the limitations of each algorithm alone, and consistently reached global fitness peaks. Thus, hybrid walks provide a model for microbial pathogens escaping host immunity without compromising variant fitness. Biological processes facilitating novelty-seeking evolution in natural pathogen populations include hypermutability, recombination, wide dispersal, and immune-compromised hosts. The high efficiency of the hybrid algorithm improves the evolutionary predictability of novel antigen variants. We propose the design of escape-proof vaccines based on high-fitness variants covering a majority of the basins of attraction on the fitness landscape representing all potential variants of a microbial antigen.

Cancer treatment effectiveness could be improved if it were possible to accurately anticipate the response of the tumor to treatment. Writing in Nature, Salehi et al. combine single-cell genomics and mathematical modeling to measure cancer subclone fitness and use these measurements to accurately predict the future trajectory of cancer evolution.

The basis of adaptive landscape diagnostics and assessment of the structure of the soil cover of the reclaimed territories is based on the basin principle, since the drainage basin is the prevailing geosystem that forms the landscape everywhere, the fundamental concept of which is based on the idea of the interrelation and interdependence of all natural phenomena of the earth’s surface. The key geosystem unit for mapping the soil cover structure and its diagnostics in the catchment area is the transect-catena. In the transect-catena, as a natural body, not torn out of the space and time of regional development of the territory and being its full-scale model, and an information key, the areal «geometric shape» (cartography) of the soil cover is combined with catenary (geochemical conjugation) and with structural functionally organization of geosystems.

In the light of recent developments, the use of biological techniques in crop cultivation technologies needs to be expanded. The role of crop rotations with a high proportion of legume grasses, green manure crops and intermediate crops is increasing. The purpose of the research is to study soil fertility dynamics in terms of the main nutritional elements and determine their balance for the development of the adaptive landscape farming system in the North-Eastern region of the European part of Russia. Experiments were carried out on the experimental plots of the FARC of the North-East in 2002–2009. Soil – sod-podzolic middle loamy. We analyzed data of a long-term stationary experiment in four grain-fallow-grass field crop rotations. Meteorological conditions during the years of research were close to the average long-term values and favorable for crop cultivation. The introduction of low doses of fertilizers (N45P45K45 as mineral, 11.5–22.5 t/ha as green manure) increased the content of mobile phosphorus in the soil by 11–14 mg/kg (LSD05 = 9.5) in crop rotations with occupied and green manure fallows. An increase in exchangeable potassium by 15 mg/kg (LSD05 = 14.1) was noted in crop rotation with two clover fields for green manure. A positive balance of nitrogen and phosphorus was obtained in crop rotation with occupied and green manure fallows. The nitrogen balance was with a higher intake of +157.8 – +483.5 kg/ha and balance intensity – 115–140 %. The balance of phosphorus has developed at +6.0…+49.6 kg/ha. The positive balance of potassium was only in crop rotations with the introduction of green manure fertilizers and intermediate crop (+69.9…+175.8 kg/ha). The intensity of the balance of these elements was 101–118 %. In the control crop rotation with bare fallow at a minimum intake of nutrients, the negative balance of nitrogen, phosphorus and potassium was obtained; the intensity was below 100 %. Productivity of crop rotation was at the level of 4.90–5.41 thousand fodder units. According to the optimal combination of productivity and the balance of nutritional elements, crop rotation with two clover fields for green manure and an intermediate crop stood out.

Search trajectory networks (STNs) were proposed as a tool to analyze the behavior of metaheuristics in relation to their exploration ability and the search space regions they traverse. The technique derives from the study of fitness landscapes using local optima networks (LONs). STNs are related to LONs in that both are built as graphs, modelling the transitions among solutions or group of solutions in the search space. The key difference is that STN nodes can represent solutions or groups of solutions that are not necessarily locally optimal. This work presents an STN-based study for a particular combinatorial optimization problem, the cyclic bandwidth sum minimization. STNs were employed to analyze the two leading algorithms for this problem: a memetic algorithm and a hyperheuristic memetic algorithm. We also propose a novel grouping method for STNs that can be generally applied to both continuous and combinatorial spaces.

PSO is a simple and yet powerful metaheuristic search algorithm widely used to solve various optimization problems. Nevertheless, conventional PSO tends to lose its population diversity drastically and suffer with compromised performance when encountering the optimization problems with complex fitness landscapes. Extensive studies suggest the needs of preserving high population diversity for PSO to escape from the local optima in order to solve complex optimization problems effectively. Inspired by these ideas, a hovering swarm PSO (HSPSO) is proposed in this paper, where a computationally efficient diversity preservation scheme is first introduced to divide the population of HSPSO into a main swarm and a hovering swarm. An exemplar construction scheme is subsequently proposed in the main swarm of HSPSO to generate a universal exemplar by considering the promising directional information contributed by the other non-fittest particles. The proposed universal exemplar is envisioned to suppress the negative impacts of global best particle, while remain effective to guide all particles of main swarm converging towards the promising solution regions. While hovering around the main swarm, an intelligent scheme is introduced to dynamically adjust inertia weights of all hovering swarm members to achieve proper balancing of exploration and exploitation searches at swarm levels. Extensive performance analyses are conducted by using the proposed HSPSO to solve 30 benchmark functions of CEC 2014 and five real-world engineering applications. Simulation results reveal that the HSPSO is able outperform the state-of-art optimizers when solving most tested functions due to its excellent diversity preservation capability.

Sexual selection shapes the adaptive landscape in complex ways that lead to trait integration. Much of our understanding of selection comes from studies of morphological traits. However, few studies attempt to quantify the form and direction of selection on performance even though it is predicted to be a more direct target of selection in nature. We measured sexual selection on performance traits (bite force, sprint speed and endurance) in an Australian lizard, the Eastern water skink (Eulamprus quoyii). We first staged 123 contests between size-matched males to investigate whether performance traits were important in determining contest outcome. In a second experiment, we established six breeding populations in large replicate semi-natural enclosures to estimate whether performance traits predicted reproductive success. Our results show that none of the performance measures were important in predicting contest outcome and were not generally strong predictors of reproductive success. However, our analyses suggest a complex fitness landscape driven by males adopting different alternative reproductive tactics (ARTs). We provide a rare test of the role performance plays in sexual selection and highlight the need to test common assumptions regarding the link between maximal performance and fitness. Our results suggest that performance traits may not necessarily be direct targets of sexual selection, but rather indirect targets through their integration with morphological and/or behavioural traits, highlighting a need for more explicit tests of the predicted links between performance and fitness.

In vitro selection is a powerful tool that can be used to understand basic principles of molecular evolution. We used in vitro selection to understand how changes in length and the accumulation of point mutations enable the evolution of functional RNAs. Using RNA populations of various lengths, we performed a series of in vitro experiments to select for ribozymes with RNA ligase activity. We identified a core ribozyme structure that was robust to changes in RNA length, high levels of mutagenesis, and increased selection pressure. Elaboration on this core structure resulted in improved activity which we show is consistent with a larger trend among functional RNAs in which increasing motif size can lead to an exponential improvement in fitness. We conclude that elaboration on conserved core structures is a preferred mechanism in RNA evolution. This conclusion, drawn from selections of RNAs from random sequences, is consistent with proposed evolutionary histories of specific biological RNAs. More generally, our results indicate that modern RNA structures can be used to infer ancestral structures. Our observations also suggest a mechanism by which structural outcomes of early RNA evolution would be largely reproducible even though RNA fitness landscapes consist of disconnected clusters of functional sequences.

A local optima network (LON) encodes local optima connectivity in the fitness landscape of a combinatorial optimisation problem. Recently, LONs have been studied for their fractal dimension. Fractal dimension is a complexity index where a non-integer dimension can be assigned to a pattern. This paper investigates the fractal nature of LONs and how that nature relates to metaheuristic performance on the underlying problem. We use visual analysis, correlation analysis, and machine learning techniques to demonstrate that relationships exist and that fractal features of LONs can contribute to explaining and predicting algorithm performance. The results show that the extent of multifractality and high fractal dimensions in the LON can contribute in this way when placed in regression models with other predictors. Features are also individually correlated with search performance, and visual analysis of LONs shows insight into this relationship.

Article publié dans la revue Evolution doi: 10.1111/evo.14129. Voir hal-03375711.; International audience; The evolution of mate preferences may depend on natural selection acting on the mating cues and on the underlying genetic architecture. While the evolution of assortative mating with respect to locally adapted traits has been well-characterized, the evolution of disassortative mating is poorly characterized. Here we aim at understanding the evolution of disassortative mating for traits under strong balancing selection, by focusing on polymorphic mimicry as an illustrative example. Positive frequency-dependent selection exerted by predators generates local selection on wing patterns acting against rare variants and promoting local monomorphism. This acts across species boundaries, favouring Mullerian mimicry among defended species. In this well-characterized adaptive landscape, polymorphic mimicry is rare but is observed in a butterfly species, associated with polymorphic chromosomal inversions. Because inversions are often associated with recessive deleterious mutations, we hypothesize they may induce heterozygote advantage at the color pattern locus, putatively favoring the evolution of disassortative mating. To explore the conditions underlying the emergence of disassortative mating, we modeled both a trait locus (colour pattern for instance), subject to mutational load, and a preference locus. We confirm that heterozygote advantage favors the evolution of disassortative mating and show that disassortative mating is more likely to emerge if at least one allele at the trait locus is free from any recessive deleterious mutations. We modelled different possible genetic architectures underlying mate choice behaviour, such as self referencing alleles, or specific preference or rejection alleles. Our results showed that self referencing or rejection alleles linked to the color pattern locus can be under positive selection and enable the emergence of disassortative mating. However rejection alleles allow the emergence of disassortative mating only when the color pattern and preference loci are tightly linked. Our results therefore provide relevant predictions on both the selection regimes and the genetic architecture favoring the emergence of disassortative mating and a theoretical framework in which to interprete empirical data on mate preferences in wild populations.

Local search is widely used to solve combinatorial optimisation problems and to model biological evolution, but the performance of local search algorithms on different kinds of fitness landscapes is poorly understood. Here we consider how fitness landscapes can be represented using valued constraints, and investigate what the structure of such representations reveals about the complexity of local search. First, we show that for fitness landscapes representable by binary Boolean valued constraints there is a minimal necessary constraint graph that can be easily computed. Second, we consider landscapes as equivalent if they allow the same (improving) local search moves; we show that a minimal constraint graph still exists, but is NP-hard to compute. We then develop several techniques to bound the length of any sequence of local search moves. We show that such a bound can be obtained from the numerical values of the constraints in the representation, and show how this bound may be tightened by considering equivalent representations. In the binary Boolean case, we prove that a degree 2 or tree-structured constraint graph gives a quadratic bound on the number of improving moves made by any local search; hence, any landscape that can be represented by such a model will be tractable for any form of local search. Finally, we build two families of examples to show that the conditions in our tractability results are essential. With domain size three, even just a path of binary constraints can model a landscape with an exponentially long sequence of improving moves. With a treewidth-two constraint graph, even with a maximum degree of three, binary Boolean constraints can model a landscape with an exponentially long sequence of improving moves., 26 pages, 9 figures. Extended journal version to appear in Journal of Artificial Intelligence Research; conference version appeared in CP2019

The acquisition of terrestrial, limb-based locomotion during tetrapod evolution has remained a subject of debate for more than a century1,2. Our current understanding of the locomotor transition from water to land is largely based on a few exemplar fossils such as Tiktaalik3, Acanthostega4, Ichthyostega5 and Pederpes6. However, isolated bony elements may reveal hidden functional diversity, providing a more comprehensive evolutionary perspective7. Here we analyse 40 three-dimensionally preserved humeri from extinct tetrapodomorphs that span the fin-to-limb transition and use functionally informed ecological adaptive landscapes8–10 to reconstruct the evolution of terrestrial locomotion. We show that evolutionary changes in the shape of the humerus are driven by ecology and phylogeny and are associated with functional trade-offs related to locomotor performance. Two divergent adaptive landscapes are recovered for aquatic fishes and terrestrial crown tetrapods, each of which is defined by a different combination of functional specializations. Humeri of stem tetrapods share a unique suite of functional adaptations, but do not conform to their own predicted adaptive peak. Instead, humeri of stem tetrapods fall at the base of the crown tetrapod landscape, indicating that the capacity for terrestrial locomotion occurred with the origin of limbs. Our results suggest that stem tetrapods may have used transitional gaits5,11 during the initial stages of land exploration, stabilized by the opposing selective pressures of their amphibious habits. Effective limb-based locomotion did not arise until loss of the ancestral ‘L-shaped’ humerus in the crown group, setting the stage for the diversification of terrestrial tetrapods and the establishment of modern ecological niches12,13. Analysis of humeri from fossils that span the fin-to-limb transition reveal that the change in the humerus shape is driven by both ecology and phylogeny, and is associated with functional trade-offs related to locomotor performance.

A major issue while conceiving or parameterizing an optimization heuristic is to ensure an appropriate balance between exploitation and exploration of the search. Evolution strategies and neighborhood-based metaheuristics constitute relevant high-level frameworks, which ease the problem solving but are often complex to configure. Moreover, their effective behavior, according to the particularities of the search landscapes, remains difficult to grasp. In this paper, we deeply investigate the sampled walk search algorithm, which is a local search equivalent of the $$(1,\lambda )$$ -evolution strategy, considering that the neighborhood relation describes mutation possibilities. We specifically designed experiments to better understand the behavior of such a strategy offering a fine way to deal with the exploration versus exploitation dilemma. The main contribution is the analysis of search trajectories by evaluating and visualizing both their width (exploration) and their height (exploitation). More generally, we aim at bringing insights about the behavior of the $$(1,\lambda )$$ -ES in a discrete optimization context and within a fitness landscape perspective.

International audience

The performance of differential evolution (DE) algorithm highly depends on the selection of mutation strategy. However, there are six commonly used mutation strategies in DE. Therefore, it is a challenging task to choose an appropriate mutation strategy for a specific optimization problem. For a better tackle this problem, in this paper, a novel DE algorithm based on local fitness landscape called LFLDE is proposed, in which the local fitness landscape information of the problem is investigated to guide the selection of the mutation strategy for each given problem at each generation. In addition, a novel control parameter adaptive mechanism is used to improve the proposed algorithm. In the experiments, a total of 29 test functions originated from CEC2017 single-objective test function suite which are utilized to evaluate the performance of the proposed algorithm. The Wilcoxon rank-sum test and Friedman rank test results reveal that the performance of the proposed algorithm is better than the other five representative DE algorithms.