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The aim of this article is to explore the conception of artificial life forms and the interactions we have with them by paying a particular attention to the analogies that characterize them and the mental processes they give rise to. The article adopts a crossed perspective, focusing on the representations conveyed by artificial life but also on the way we deal with the presence of so-called intelligent or social machines. Based on a multi-sited ethnography of design practices and human-machine interaction experiments, this article hypothesizes that robots and AI constitute a symbolic means of addressing problems regarding our understanding of what life could be whether it is biological or social. Starting from the history of automata, this article will first address the modalities by which an "artificial life" is conceived by analogy with vital processes. It will then focus on the way these processes come into play in an experimental interaction situation. [ABSTRACT FROM AUTHOR]

[Display omitted] • Comprehensive analysis on the superiority of Y. lipolytica. • Up-to-date overview of synthetic biology tools in Y. lipolytica. • First review on synthesis of diverse natural products by Y. lipolytica. • In-depth discussion on challenges and future perspectives for the production of natural products. With the rapid development of synthetic biology, researchers can design, modify, or even synthesize microorganisms de novo , and microorganisms endowed with unnatural functions can be considered "artificial life" and facilitate the development of functional products. Based on this concept, researchers can solve critical problems related to the insufficient supply of natural products, such as low yields, long production cycles, and cumbersome procedures. Due to its superior performance and unique physiological and biochemical characteristics, Yarrowia lipolytica is a favorable chassis cell used for green biomanufacturing by numerous researchers. This paper mainly reviews the development of synthetic biology techniques for Y. lipolytica and summarizes the recent research progress on the synthesis of natural products in Y. lipolytica. This review will promote the continued innovative development of Y. lipolytica by providing theoretical guidance for research on the biosynthesis of natural products. [ABSTRACT FROM AUTHOR]

Engineering synthetic minimal cells provide a controllable chassis for studying the biochemical principles of natural life, increasing our understanding of complex biological processes. Recently, synthetic cell engineering has enabled communication between both natural live cells and other synthetic cells. A system such as these enable studying interactions between populations of cells, both natural and artificial, and engineering small molecule cell communication protocols for a variety of basic research and practical applications. In this review, we summarize recent progress in engineering communication between synthetic and natural cells, and we speculate about the possible future directions of this work. [ABSTRACT FROM AUTHOR]

The challenge of robotic reproduction – making of new robots by recombining two existing ones – has been recently cracked and physically evolving robot systems have come within reach. Here we address the next big hurdle: producing an adequate brain for a newborn robot. In particular, we address the task of targeted locomotion which is arguably a fundamental skill in any practical implementation. We introduce a controller architecture and a generic learning method to allow a modular robot with an arbitrary shape to learn to walk towards a target and follow this target if it moves. Our approach is validated on three robots, a spider, a gecko, and their offspring, in three real-world scenarios. [ABSTRACT FROM AUTHOR]

Braitenberg vehicles are simple robotic platforms, equipped with rudimentary sensor and motor components. Such vehicles have typically featured as part of thought experiments that are intended to show how complex behaviours are apt to emerge from the interaction of inner control mechanisms with aspects of bodily structure and features of the wider (extra-agential) environment. The present paper describes a framework for creating Braitenberg-like vehicles, which is built on top of a widely used and freely available game engine, namely, the Unity game engine. The framework can be used to study the behaviour of virtual vehicles within a multiplicity of virtual environments. All aspects of the vehicle's design, as well as the wider virtual environment in which the vehicle is situated, can be modified during the design phase, as well as at runtime. The result is a general-purpose simulation capability that is intended to provide the foundation for studies in so-called computational situated cognition —a field of study whose primary objective is to support the computational modelling of cognitive processes associated with the physically-embodied, environmentally-embedded, and materially-extended mind. [ABSTRACT FROM AUTHOR]

The theory of autopoiesis has been influential in many areas of theoretical biology, especially in the fields of artificial life and origins of life. However, it has not managed to productively connect with mainstream biology, partly for theoretical reasons, but arguably mainly because deriving specific working hypotheses has been challenging. The theory has recently undergone significant conceptual development in the enactive approach to life and mind. Hidden complexity in the original conception of autopoiesis has been explicated in the service of other operationalizable concepts related to self-individuation: precariousness, adaptivity, and agency. Here we advance these developments by highlighting the interplay of these concepts with considerations from thermodynamics: reversibility, irreversibility, and path-dependence. We interpret this interplay in terms of the self-optimization model, and present modeling results that illustrate how these minimal conditions enable a system to re-organize itself such that it tends toward coordinated constraint satisfaction at the system level. Although the model is still very abstract, these results point in a direction where the enactive approach could productively connect with cell biology. [ABSTRACT FROM AUTHOR]

Abstract Novelty search is a tool in evolutionary and swarm robotics for maintaining the diversity of population needed for continuous robotic operation. It enables nature-inspired algorithms to evaluate solutions on the basis of the distance to their k -nearest neighbors in the search space. Besides this, the fitness function represents an additional measure for evaluating the solution, with the purpose of preserving the so-named novelty solutions into the next generation. In this study, a differential evolution was hybridized with novelty search. The differential evolution is a well-known algorithm for global optimization, which is applied to improve the results obtained by the other solvers on the CEC-14 benchmark function suite. Furthermore, functions of different dimensions were taken into consideration, and the influence of the various novelty search parameters was analyzed. The results of experiments show a great potential for using novelty search in global optimization. [ABSTRACT FROM AUTHOR]

Graphical abstract Abstract Droplet systems remain the subject of a constant fascination in science and technology. Here we focus on organic droplets floating on the surface of aqueous surfactant solutions. These droplets can exhibit intriguing interactions. Recently we have found independently in two laboratories that we can observe almost the same complex collective behaviour in two different droplet systems. The aim of this paper is to compare both droplets systems, present their differences and show their similar oscillatory behaviour. The first system consists of decanol droplets floating on a sodium decanoate solution. In the second one, the droplets consist of a mixture of ethyl salicylate and liquid paraffin and they are placed on the surface of an aqueous sodium dodecyl sulphate solution. Although the mechanism of these spatio-temporal interactions of droplets is not fully understood yet, we believe that this behaviour is based on the same phenomena. [ABSTRACT FROM AUTHOR]

Abstract The construction of an embryo from a single cell precursor is a highly complex process. Evolutionary emergence of the first embryos is even more complex, and involves both a transition to multicellularity along with the establishment of developmental mechanisms. We propose that embryogenesis relies on a community of cells conforming to a regulatory model of emergent multicellularity. This model draws together multiple threads in the scientific literature, from complexity theory to cybernetics, and from thermodynamic entropy to artificial life. All of these strands come together to inform a model of goal-oriented regulation for emergent structures in early life. This is an important step in the evolution of early life, as well as the emergence of complex life in the earliest habitats. Our model, called the cybernetic embryo, allows for a systems-level view of the embryogenetic process. [ABSTRACT FROM AUTHOR]

Lung cancer is the major cause of death among patients with cancer throughout the world. The main symptom that indicate the lung cancer is the presence of lung nodules. This work proposes a methodology to classify lung nodule and non-nodule using texture features. The state-of-art of the presented work are the adaption of the Artificial Crawlers and Rose Diagram techniques for representing patterns over 3D images. Several information are extracted based on the texture behavior of these methods, allowing the correct classification of lung nodules candidates using Support Vector. Objective: This work proposes a methodology to classify lung nodule candidates and non-nodule candidates based on computed tomography (CT) images. Methodology: The Lung Image Database Consortium (LIDC-IDRI) image database is employed for our tests. Three techniques are employed to extract texture measurements. The first technique is artificial crawlers (ACs), an artificial life algorithm. The second technique uses the rose diagram (RD) to extract directional measurements. The third technique is a hybrid model that combines texture measurements from artificial crawlers and the rose diagram. The support vector machine (SVM) classifier with a radial basis kernel is employed. Results: In the testing stage, we used 833 scans from the LIDC-IDRI database. For the application of the methodology, we decided to divide the whole database into two groups, training and testing. We used partitions of training and testing of 20/80%, 40/60%, 60/40% and 80/20%. The division was repeated 5 times at random. We reached a mean accuracy (mACC) of 94.30%, a mean sensitivity (mSEN) of 91.86%, a mean specificity (mSPC) of 94.78%, a coefficient of accuracy variance (CAv) of 1.61% and a mean area under the receiver operating characteristic (mROC) curves of 0.922. Conclusion: Lung cancer has the highest mortality rate and one of the smallest survival rates after diagnosis. An early diagnosis increases the survival chance of patients. The proposed methodology is a useful tool for specialists in the detection of nodules. We believe we contribute for the expert system field because 1) the adaption of the Artificial Crawlers and Rose Diagram methods as 3D texture descriptors is innovative and contains great potential; 2) we adapted and developed measurements from the 3D texture descriptors; and 3) the simplicity and discriminative power of the methodology can be extended to applications based on images with other contexts. [ABSTRACT FROM AUTHOR]

• A new method of segmentation medical images which combines AI, fusion and artificial life. • A new variant of fusion of the US and the elastography images. • Artificial agents with the ability to find strong edges and close the boundary gaps. • Testing against 85 preceding segmentation methods. • Video demo at http://shorturl.at/htBW9. Segmentation of cancerous tumors in ultrasound (US) images of human organs is one of the critical problems in medical imaging. The US images are characterized by low contrast, irregular shapes, high levels of speckle-noise and acoustic shadows, making it difficult to segment the tumor. Yet, US imaging is considered one of the most inexpensive and safe imaging tests available to detect cancer in its early stages. However, an automatic segmentation method applicable to all types of US imagery does not exist. This paper proposes a novel segmentation method that combines image fusion, artificial life (AL) and a genetic algorithm (GA). The new algorithm has been applied to US images of breast cancer. The method is based on tracing agents (TA), which are artificial organisms with memory and the ability to communicate. They live inside a fusion image generated from the US and the elastography (EL) images. The TA can recognize the patterns of strong edges and boundary gaps allowing to outline the tumor. The new model has been tested against six types of segmentation models, i.e., machine learning, active contours, level set models, superpixel models, edge linking models and selected hybrid methods. The experiments include 16 state-of-the-art methods, which outperform 69 recent and classical segmentation routines. The tests were run on 395 breast cancer images from http://onlinemedicalimages.com and https://www.ultrasoundcases.info/. TA training employs a GA. The model has been verified on "hard" cases (complex shapes, boundary leakage, and noisy edge maps). The proposed algorithm produces more accurate results than the reference methods on high complexity images. A video demo of the algorithm is at http://shorturl.at/htBW9. [ABSTRACT FROM AUTHOR]

The standard view of embryogenesis is one of cooperation driven by the cells' shared genetics and evolutionary interests. However, numerous examples from developmental biology and agriculture reveal a surprising amount of competition among body cells, tissues, and organs for both metabolic and informational resources. To explain the existence of such competition we had hypothesized that evolution uses limiting "reservoirs" of resource molecules as a communication medium - a global scratchpad, to enable tissues across the body to coordinate growth. Here, we test this hypothesis via an evolutionary simulation of embryogeny in silico. Genomes encode state transition rules for cells, such as proliferation, differentiation, and resource use, enabling virtual embryos to develop a specific large-scale morphology. An evolutionary algorithm operates over the genomes, with fitness defined as a function of specific morphological requirements for the final embryo shape. We found that not only does such an algorithm rapidly discover rules for cellular behavior that reliably make embryos with specific anatomical properties, but that it discovers the strategy of using finite resources to coordinate development. Given the option of using finite or infinite reservoirs (which determine cells' ability to carry out specific actions), evolution preferentially uses finite reservoirs, which results in higher fitness and increased consistency (without needing direct selection for morphological invariance). We report aspects of anatomical, physiological/transcriptional, and genomic analysis of evolved virtual embryos that help understand how evolution can use competition among genetically identical subunits within a multicellular body to coordinate reliable, complex morphogenesis. Our results suggest that under some conditions, composite multi-scale systems will promote conflict and artificial scarcity for their components. [ABSTRACT FROM AUTHOR]

In this work we introduce a novel method for creating behaviors in cellular automata: optimizing the topology of the cellular substrate while maintaining a single simple update rule. We study the effect of altering the shape of a 3D cellular automaton and local signaling ability of each of its cells on the ability of that automaton as a whole to give rise to emergent locomotion behavior. This system optimizes for the physically embodied interactions between a cellular automaton with an external physically simulated world, rather than optimizing directly for a computational ability internal to the automaton itself. We give each cell in the automaton the ability to have an internal “excited” state, and also the ability to perform a physical action (volumetric contraction and expansion) as a result of that state. We then employ an evolutionary algorithm to optimize for the locomotion ability of the “robot” resulting from the behavior of this embodied automaton. We demonstrate a number of diverse topologies which lead to effective locomotion behaviors in this paradigm. We believe that creating complex behavior from simple rules in a complex substrate not only opens up questions about cellular automata, but also provides insights towards the study of morphological computation and embodied cognition. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new artificial life model for image enhancement is proposed. [•] The new model is based on the behavior of an intelligent organism. [•] The model is evaluated using synthetic and real images with different levels of noise. [•] Different quality metrics are used to assess the model efficiency. [ABSTRACT FROM AUTHOR]

Highlights: [•] Artificial organisms need not pose greater risks than derived organisms. [•] The creation of artificial life would not support any form of biological reductionism. [•] Artificial life forms might have uncertain functional status. [•] But this is irrelevant to their moral status. [•] The genealogy of an organism makes no difference to its moral status. [Copyright &y& Elsevier]

Highlights : [•] Synthetic biology uses engineering principles to design organisms suited to human ends. [•] This issue examines conceptual, methodological and ethical challenges posed by synthetic life. [•] An overview of these challenges, as well as the contents of the special issue, is provided. [Copyright &y& Elsevier]

Abstract: R-Models are an approach to capturing notions of assistance and abstraction in reproductive systems, based on labelled transition systems and Gibson''s theory of affordances. R-Models incorporate a labelled transition system that describes how a reproductive system changes over the course of reproduction. The actors in the system are represented by a set of entities together with a relation describing the states in which those entities are present, and an affordance-modelling function mapping actions to sets of entities which enable those actions to be performed. We show how R-models can be classified based on whether the reproducer is assisted or unassisted in reproduction, and whether or not the reproducer is active during reproduction. We prove that all assisted and unassisted R-models have a related R-model which has the opposite classification. We discuss the relevance to the field of artificial life, give a potential application to the fields of computer virology, and demonstrate reproduction modelling and classification in action using examples. [Copyright &y& Elsevier]

Abstract: GReaNs is an Artificial Life platform we have built to investigate the general principles that guide evolution of multicellular development and evolution of artificial gene regulatory networks. The embryos develop in GReaNs in a continuous 3-dimensional (3D) space with simple physics. The developmental trajectories are indirectly encoded in linear genomes. The genomes are not limited in size and determine the topology of gene regulatory networks that are not limited in the number of nodes. The expression of the genes is continuous and can be modified by adding environmental noise. In this paper we evolved development of structures with a specific shape (an ellipsoid) and asymmetrical pattering (a 3D pattern inspired by the French flag problem), and investigated emergence of the robustness to damage in development and the emergence of the robustness to noise. Our results indicate that both types of robustness are related, and that including noise during evolution promotes higher robustness to damage. Interestingly, we have observed that some evolved gene regulatory networks rely on noise for proper behaviour. [Copyright &y& Elsevier]

Abstract: We have developed an extension of the NEAT neuroevolution method, called NEATfields, to solve problems with large input and output spaces. The NEATfields method is a multilevel neuroevolution method using externally specified design patterns. Its networks have three levels of architecture. The highest level is a NEAT-like network of neural fields. The intermediate level is a field of identical subnetworks, called field elements, with a two-dimensional topology. The lowest level is a NEAT-like subnetwork of neurons. The topology and connection weights of these networks are evolved with methods derived from the NEAT method. Evolution is provided with further design patterns to enable information flow between field elements, to dehomogenize neural fields, and to enable detection of local features. We show that the NEATfields method can solve a number of high dimensional pattern recognition and control problems, provide conceptual and empirical comparison with the state of the art HyperNEAT method, and evaluate the benefits of different design patterns. [Copyright &y& Elsevier]

Abstract: This paper proposes a way to generate a robot genome that contributes to defining the personality of a software robot or an artificial life in a mobile phone. The personality should be both complex and feature-rich, but still plausible by human standards for an emotional life form. However, it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robot''s personality while manually initializing values for the individual genes. To overcome this difficulty, this paper proposes a neural network algorithm for a genetic robot''s personality (NNGRP) and an upgraded version of a previously introduced evolutionary algorithm for a genetic robot''s personality (EAGRP). The robot genomes for heterogeneous personalities are demonstrably generated via the NNGRP and the EAGRP and compared. The implementation is embedded into genetic robots in a mobile phone to verify the feasibility and effectiveness of each algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, we describe a digital scenario where we simulated the emergence of self-organized symbol-based communication among artificial creatures inhabiting a virtual world of unpredictable predatory events. In our experiment, creatures are autonomous agents that learn symbolic relations in an unsupervised manner, with no explicit feedback, and are able to engage in dynamical and autonomous communicative interactions with other creatures, even simultaneously. In order to synthesize a behavioral ecology and infer the minimum organizational constraints for the design of our creatures, we examined the well-studied case of communication in vervet monkeys. Our results show that the creatures, assuming the role of sign users and learners, behave collectively as a complex adaptive system, where self-organized communicative interactions play a major role in the emergence of symbol-based communication. We also strive in this paper for a careful use of the theoretical concepts involved, including the concepts of symbol and emergence, and we make use of a multi-level model for explaining the emergence of symbols in semiotic systems as a basis for the interpretation of inter-level relationships in the semiotic processes we are studying. [Copyright &y& Elsevier]

Abstract: 3-D object segmentation is an important and challenging topic in computer vision that could be tackled with artificial life models. A Channeler Ant Model (CAM), based on the natural ant capabilities of dealing with 3-D environments through self-organization and emergent behaviours, is proposed. Ant colonies, defined in terms of moving, pheromone laying, reproduction, death and deviating behaviours rules, is able to segment artificially generated objects of different shape, intensity, background. The model depends on few parameters and provides an elegant solution for the segmentation of 3-D structures in noisy environments with unknown range of image intensities: even when there is a partial overlap between the intensity and noise range, it provides a complete segmentation with negligible contamination (i.e., fraction of segmented voxels that do not belong to the object). The CAM is already in use for the automated detection of nodules in lung Computed Tomographies. [Copyright &y& Elsevier]

Abstract: Advances in computing hardware coupled with its software counterparts have, for the past decades, influenced to a greater extent both the workflow of archaeologists and their interpretation of archaeological data. On the leisurely periphery, the synergy that arises between entertainment demands and commercial driven developments of interactive 3D (i3D) computer games has pushed these technologies beyond the expectations of the Virtual Reality (VR) community. This phenomenal growth in useable technology and its proportionate decrease in price have benefited the applicability of VR which in turn, have made it more accessible for researchers wishing to harness its benefits. The last 10 years have seen a steady increase in the use of VR technology to restore, preserve, reconstruct, recreate, and visualise ancient sites, monuments and artefacts. But, is technology ready for virtual time travel (VTT)? This article examines the possibility of experiential archaeology in voltage and silicon with the aim of formulating a strategy for VTT. [Copyright &y& Elsevier]

Abstract: This paper surveys fitness functions used in the field of evolutionary robotics (ER). Evolutionary robotics is a field of research that applies artificial evolution to generate control systems for autonomous robots. During evolution, robots attempt to perform a given task in a given environment. The controllers in the better performing robots are selected, altered and propagated to perform the task again in an iterative process that mimics some aspects of natural evolution. A key component of this process–one might argue, the key component–is the measurement of fitness in the evolving controllers. ER is one of a host of machine learning methods that rely on interaction with, and feedback from, a complex dynamic environment to drive synthesis of controllers for autonomous agents. These methods have the potential to lead to the development of robots that can adapt to uncharacterized environments and which may be able to perform tasks that human designers do not completely understand. In order to achieve this, issues regarding fitness evaluation must be addressed. In this paper we survey current ER research and focus on work that involved real robots. The surveyed research is organized according to the degree of a priori knowledge used to formulate the various fitness functions employed during evolution. The underlying motivation for this is to identify methods that allow the development of the greatest degree of novel control, while requiring the minimum amount of a priori task knowledge from the designer. [Copyright &y& Elsevier]

Abstract: In future astrobiology, like in modern astrophysics, the numerical simulations can be a very important tool for proving theories. In this paper, I propose a simple lattice model of a multi-species ecosystem suitable for the study of emergent properties of macroevolution. Unlike the majority of ecological models, the number of species is not fixed—they emerge by “mutation” of existing species, then survive or go extinct depending on the balance between local ecological interactions. The Monte-Carlo numerical simulations show that this model is able to qualitatively reproduce phenomena that have been empirically observed, like the dependence between size of the isolated area and the number of species inhabiting there, primary production and species-diversity. The model allows also studying the causes of mass extinctions and more generally, repeatability, and the role of pure chance in macroevolution. [Copyright &y& Elsevier]

Abstract: Artificial Genetic Regulatory Networks (GRNs) are interesting control models through their simplicity and versatility. They can be easily implemented, evolved and modified, and their similarity to their biological counterparts makes them interesting for simulations of life-like systems as well. These aspects suggest they may be perfect control systems for distributed computing in diverse situations, but to be usable for such applications the computational power and evolvability of GRNs need to be studied. In this research we propose a simple distributed system implementing GRNs to solve the well known NP-complete graph colouring problem. Every node (cell) of the graph to be coloured is controlled by an instance of the same GRN. All the cells communicate directly with their immediate neighbours in the graph so as to set up a good colouring. The quality of this colouring directs the evolution of the GRNs using a genetic algorithm. We then observe the quality of the colouring for two different graphs according to different communication protocols and the number of different proteins in the cell (a measure for the possible complexity of a GRN). Those two points, being the main scalability issues that any computational paradigm raises, will then be discussed. [Copyright &y& Elsevier]

Abstract: We propose a novel approach to self-regenerating continuously-operating systems. Such systems provide best-case solutions in security surveillance or decision making centers. We introduce HADES, a self-regenerating system whose agents acknowledge their “citizenship” or faithfulness to the good of the system and are able to monitor their environment. When agents of HADES find irregularity in themselves they first try to repair, and will self-kill if repair fails. When an agent senses that there are persistent malfunctioning agents in its environment, it sends messages to entice them to self-kill. The neighbors then proceed to generate new healthy agents to replace the killed agent. We experiment with HADES on various impairments including the most difficult one of excessive regeneration of irregular aggressive agents. These agents may use all of the system''s resources and thus take over the system, reminiscent of biologically grown tumors. We study how irregular growth may occur and then develop protocols of killing these agents to optimize the system''s longevity. While some of the inspiration is from the immune system and tumor therapy, we contribute to the field of AI by introducing protocols for system robustness via the notion of active citizenship and the fundamental property of programmed death. [Copyright &y& Elsevier]

Abstract: Knowing that human judgment can be fallible, we propose to distinguish the subjective ascription of a property, such as autonomy, from the genuine fact that an entity is characterised by a certain property, i.e., it is autonomous. In this paper, we take a closer look at this distinction and what it is grounded on, taking a constructivist stance that sees the scientist as an observing subject. We arrive at a notion of fortified ascription, in which knowledge and scientific study of generative mechanisms play an important role, and look at some models of autonomy in the light of this distinction. [Copyright &y& Elsevier]

Abstract: Anticipation allows a system to adapt to conditions that have not yet come to be, either externally to the system or internally. Autonomous systems actively control their own conditions so as to increase their functionality (they self-regulate). Living systems self-regulate in order to increase their own viability. These increasingly stronger conditions, anticipation, autonomy and viability, can give an insight into progressively stronger classes of models of autonomy. I will argue that stronger forms are the relevant ones for Artificial Life. This has consequences for the design of and accurate simulation of living systems. [Copyright &y& Elsevier]

Abstract: Fuzzy logic is a suitable approach for linguistic knowledge representation and Artificial Life (A-Life) is a field of study dedicated to studying life and synthesis of creatures like those of real world. Thus, as our understanding about life is more in linguistic form than exact numbers, fuzzy logic seems to be a suitable logic for artificial creatures. In this paper, we have tried to apply fuzzy logic to an A-life environment and have studied the changes in capabilities of its creatures when using fuzzy minds. To do this, we have selected an A-life model called Zamin, which has creatures with case-based reasoner brains who must learn their environmental rules, search for food, avoid predators and keep up their species. Then we have replaced their brains with fuzzy self-organizing controllers and have compared the results of this replacement with former creatures. The comparisons are done both in separate environments, where measures of successful life are recorded and compared, and in common arenas, where they need to compete for resources and escape predators. [Copyright &y& Elsevier]

Abstract: This paper presents an optimum design of high-speed short journal bearing using an enhanced artificial life algorithm (EALA) to compute the solutions of optimization problem. The proposed hybrid EALA algorithm is a synthesis of an artificial life algorithm (ALA) and the random tabu search method (R-tabu method) to solve some demerits of the ALA. The emergence is the most important feature of the artificial life which is the result of dynamic interaction among the individuals consisting of the system and is not found in an individual. The artificial life optimization algorithm is a stochastic searching algorithm using the feature of artificial life. The feature of R-tabu method, which prevents converging to the local minimum, is combined with the ALA. One of the features of the R-tabu method is to divide any given searching region into several sub-steps. As the result of the combination of the two methods, the EALA not only converges faster than the ALA, but also can lead to a more accurate solution. In addition, this algorithm can also find all global optimum solutions. We applied the hybrid algorithm to the optimum design of a short journal bearing. The optimized results were compared with those of ALA and successive quadratic programming, and identified the reliability and usefulness of the hybrid algorithm. [Copyright &y& Elsevier]

Abstract: In this paper we investigate population dynamics, genealogy and complexity-increase of locally interacting populations of cellular automata-based evolving self-replicating loops (evoloops). We outline experiments indicating that the evolutionary growth in complexity, known to be achievable in principle given the complete genetic accessibility granted by universal construction, may be achievable in practice using much simpler replicating structures. By introducing evoloop populations to hostile environments, we demonstrate that selection pressures toward smaller species can be mediated to enable evolutionary accessibility to larger species, which themselves roam a much more vast portion of genetic state-space. We show that this growth in size results from intrinsically biased genealogy inherent in the rules of the evoloop CA, normally suppressed by selection pressures from direct competition favouring the smallest species. This shows that, in populations of simple self-replicating structures, a limited form of complexity-increase may result from a process which is driven by biased genealogical connectivity —a purely emergent property arising out of bottom-up evolutionary dynamics—and not just by adaptation . Implications of this result are discussed and contrasted with other self-replication studies in Artificial Life and Biology. [Copyright &y& Elsevier]

This paper presents a model which is based on biological research using the movable finite automata (MFA) on a self-assembly of T4 phage, and exhibits the results of artificial life simulation. In the previous work, Thompson and Goel [Artificial Life, Addison Weley, 1989, pp. 317–340; Biosystems 18 (1985) 23; J. Theor. Biol. 131 (1988) 351] presented the movable finite automata (MFA) which has a capability of moving on finite automata, and simulated on a computer. They were represented individual rectangular boxes, however, the results of simulation was different from real T4 phage. We propose the sphere model as a protein structure, and simulate the self-assembly of the entire structure of the T4 phage on a computer. [Copyright &y& Elsevier]

The Baldwin effect is known as an possible interaction between learning and evolution, where individual lifetime learning can influence the course of evolution without using any Lamarckian mechanism. Our concern is to consider the Baldwin effect in dynamic environments, especially when there is no explicit optimal solution through generations and this solution depends only on interactions among agents. We adopted the iterated Prisoner’s Dilemma as a dynamic environment, introduced phenotypic plasticity into its strategies, and conducted computational experiments, in which phenotypic plasticity is allowed to evolve. The Baldwin effect was observed in the experiments as follows: First, strategies with enough plasticity spread, which caused a shift from defect-oriented populations to cooperative populations. Second, these strategies were replaced by a strategy with a modest amount of plasticity generated by interactions between learning and evolution. By making three kinds of analysis, we have shown that this strategy provides outstanding performance in comparison with other deterministic strategies. Further experiments towards open-ended evolution have also been conducted so as to generalize our results. [Copyright &y& Elsevier]

Abstract: The problem of demarcating neural network space is formidable. A simple fully connected recurrent network of five units (binary activations, synaptic weight resolution of 10) has 3.2∗1026 possible initial states. The problem increases drastically with scaling. Here we consider three complementary approaches to help direct the exploration to distinguish epileptic from healthy networks. {1} First, we perform a gross mapping of the space of five-unit continuous recurrent networks using randomized weights and initial activations. The majority of weight patterns (>70%) were found to result in neural assemblies exhibiting periodic limit-cycle oscillatory behavior. {2} Next we examine the activation space of non-periodic networks demonstrating that the emergence of paroxysmal activity does not require changes in connectivity. {3} The next challenge is to focus the search of network space to identify networks with more complex dynamics. Here we rely on a major available indicator critical to clinical assessment but largely ignored by epilepsy modelers, namely: behavioral states. To this end, we connected the above network layout to an external robot in which interactive states were evolved. The first random generation showed a distribution in line with approach {1}. That is, the predominate phenotypes were fixed-point or oscillatory with seizure-like motor output. As evolution progressed the profile changed markedly. Within 20 generations the entire population was able to navigate a simple environment with all individuals exhibiting multiply-stable behaviors with no cases of default locked limit-cycle oscillatory motor behavior. The resultant population may thus afford us a view of the architectural principles demarcating healthy biological networks from the pathological. The approach has an advantage over other epilepsy modeling techniques in providing a way to clarify whether observed dynamics or suggested therapies are pointing to computational viability or dead space. [Copyright &y& Elsevier]

An individual-based model (IBM) was developed to examine the effects of intraspecific competition and spatial structuring of food on life history traits of grasshoppers inhabiting temperate-zone grasslands. Each individual carried real-valued genes which determined size of offspring and size at maturity, and which were passed on to its offspring. Size at maturity was a plastic trait, depending on an individual’s growth rate, whereas size of offspring was a fixed trait. Individuals with more successful combinations of traits produced more offspring that eventually came to dominate the population. Populations were food limited and intraspecific competition was either exploitative or size-based interference. Growth rates and fecundity depended on food quality, which declined within season as a function of day of year and the proportion consumed by grasshoppers. Three different spatial distributions of food quality were examined: uniform, random, and clumped. Optimal egg size was larger under interference competition and spatially clumped resources. Reaction norms of size at maturity were strongly affected by type of competition, and, by spatial distribution of resources within exploitative competition, but not under interference competition. The IBM shows promise as a means of analyzing life history evolution in grasshoppers in relation to processes that arise from localized interactions between individuals. [Copyright &y& Elsevier]

The modern vat-brain debate is an epistemological one, and it focuses on the point of view of a putatively deceived subject. Semantic externalists argue that we cannot coherently wonder whether we are brains in vats. This paper examines a new experimental paradigm for cognitive neuroscience—the neurally-controlled animat (NCA) paradigm—that seems to have a great deal in common with the vat-brain scenario. Neural cells are provided with a simulated body within an artificial world in order to study the brain both in vitro and in vivo. Given the similarity between the NCA scenario and the vat-brain scenario, semantic externalism seems to undermine the utility of the NCA methodology. Three initial responses to the externalist challenge are offered. A fourth response clarifies the distinctive theoretical background to the NCA in ‘artificial life’ and, in doing so, we uncover an anti-representationalist conception of the NCA. This distances the NCA paradigm from externalist objections and casts cognitive neuroscience, and the vat-brain debate, in a new light. [Copyright &y& Elsevier]

The extent to which extraterrestrial life questions can be confidently addressed rests in large measure on the extent to which terrestrial life is representative of life in general since we will have to draw from terrestrial life knowledge. This paper outlines a long-term research program that could inform the extent to which terrestrial life is representative of life more generally, which might then help inform our level of confidence in applying terrestrial life knowledge to extraterrestrial life issues. The approach involves appealing to the relatively new field of Artificial Life to: (1) use minimal characterizations of life in (2) a large number of open-ended Artificial Life computer experiments to generate “life possibility spaces” (3) the results of which can be examined for their plausibility within the context of relevant constraining knowledge, so that (4) the remaining results can be examined for variability relative to terrestrial life, where low variability might suggest that terrestrial life is typical of life in general, and high variability could be interpreted to suggest that terrestrial life might be atypical. [Copyright &y& Elsevier]

Artificial life attempts to understand the essential general properties of living systems by synthesizing life-like behavior in software, hardware and biochemicals. As many of the essential abstract properties of living systems (e.g. autonomous adaptive and intelligent behavior) are also studied by cognitive science, artificial life and cognitive science have an essential overlap. This review highlights the state of the art in artificial life with respect to dynamical hierarchies, molecular self-organization, evolutionary robotics, the evolution of complexity and language, and other practical applications. It also speculates about future connections between artificial life and cognitive science. [Copyright &y& Elsevier]

We introduce a new approach to medical image analysis that combines deformable model methodologies with concepts from the field of artificial life. In particular, we propose ‘deformable organisms’, autonomous agents whose task is the automatic segmentation, labeling, and quantitative analysis of anatomical structures in medical images. Analogous to natural organisms capable of voluntary movement, our artificial organisms possess deformable bodies with distributed sensors, as well as (rudimentary) brains with motor, perception, behavior, and cognition centers. Deformable organisms are perceptually aware of the image analysis process. Their behaviors, which manifest themselves in voluntary movement and alteration of body shape, are based upon sensed image features, pre-stored anatomical knowledge, and a deliberate cognitive plan. We demonstrate several prototype deformable organisms based on a multiscale axisymmetric body morphology, including a ‘corpus callosum worm’ that can overcome noise, incomplete edges, considerable anatomical variation, and interference from collateral structures to segment and label the corpus callosum in 2D mid-sagittal MR brain images. [Copyright &y& Elsevier]

Artificial Life is the study of all phenomena of the living world through their reproduction in artificial systems. We argue that Artificial Life models of evolution and development offer a new set of theoretical and methodological tools for investigating Piaget’s ideas. The concept of an Artificial Life Neural Network (ALNN) is first introduced, and contrasted with the study of other recent approaches to modeling development. We then illustrate how several key elements of Piaget’s theory of cognitive development (e.g., sensorimotor schemata, perception-action integration) can be investigated within the Artificial Life framework. We conclude by discussing possible new directions of Artificial Life research that will help to elaborate and extend Piaget’s developmental framework. [Copyright &y& Elsevier]

The virtual ant introduced by Langton [Physica D 22 (1986) 120] has an interesting behavior, which has been studied in several contexts. Here we give a construction to calculate any boolean circuit with the trajectory of a single ant. This proves the P-hardness of the system and implies, through the simulation of one-dimensional cellular automata and Turing machines, the universality of the ant and the undecidability of some problems associated to it. [Copyright &y& Elsevier]

Describes models that incorporate biomimetic mechanisms into the control architecture of an animat that has to survive in a changing environment. Comparison of the adaptive capacities of the mechanisms to engineering solutions; Remedying of the encountered shortcomings with biologists taking advantage of the bottom-up and synthetic point of view.

Artificial life provides important theoretical and methodological tools for the investigation of Piaget''s developmental theory. This new method uses artificial neural networks to simulate living phenomena in a computer. A recent study by Parisi and Schlesinger suggests that artificial life might reinvigorate the Piagetian framework. We contrast artificial life with traditional cognitivist approaches, discuss the role of innateness in development, and examine the relation between physiological and psychological explanations of intelligent behaviour. [Copyright &y& Elsevier]