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Recently, evolutionary computation (EC) has experienced significant advancements due to the integration of machine learning, distributed computing, and big data technologies. These developments have led to new research avenues in EC, such as distributed EC and surrogate-assisted EC. While these advancements have greatly enhanced the performance and applicability of EC, they have also raised concerns regarding privacy leakages, specifically the disclosure of optimal results and surrogate models. Consequently, the combination of evolutionary computation and privacy protection becomes an increasing necessity. However, a comprehensive exploration of privacy concerns in evolutionary computation is currently lacking, particularly in terms of identifying the object, motivation, position, and method of privacy protection. To address this gap, this paper aims to discuss three typical optimization paradigms, namely, centralized optimization, distributed optimization, and data-driven optimization, to characterize optimization modes of evolutionary computation and proposes BOOM (i.e., oBject, mOtivation, pOsition, and Method) to sort out privacy concerns related to evolutionary computation. In particular, the centralized optimization paradigm allows clients to outsource optimization problems to a centralized server and obtain optimization solutions from the server. The distributed optimization paradigm exploits the storage and computational power of distributed devices to solve optimization problems. On the other hand, the data-driven optimization paradigm utilizes historical data to address optimization problems without explicit objective functions. Within each of these paradigms, BOOM is used to characterize the object and motivation of privacy protection. Furthermore, this paper discuss the potential privacy-preserving technologies that strike a balance between optimization performance and privacy guarantees. Finally, this paper outlines several new research directions for privacy-preserving evolutionary computation. [ABSTRACT FROM AUTHOR]

In recent years, multilayer networks have served as effective models for addressing and analyzing real-world systems with multiple relationships. Among these scenarios, the community detection (CD) problem is one of the most prominent research hotspots. Although some research on multilayer network CD (MCD) has been proposed to address this problem, most studies focus only on topological structures. Therefore, their algorithms cannot extract the most out of complementary network information, such as node similarities and low-rank features, which may lead to unsatisfactory accuracy. To tackle this problem, this paper proposes a novel multi-objective evolutionary algorithm based on consensus prior information (MOEA-CPI). The proposed algorithm takes full advantage of prior information to guide the MOEA with respect to topological structures, initializations, and the optimization process. More specifically, this paper first extracts two kinds of prior information, i.e., graph-level and node-level information, based on Node2vec and Jaccard similarity, respectively. Then, the prior layer and a high-quality initial population are constructed on the basis of the graph-level information. During the optimization process, the genetic operator, which integrates the weighting strategy and node-level information, is applied to guide the algorithm to distribute similar nodes into the same community. Extensive experiments are implemented to prove the superior performance of MOEA-CPI over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The papers in this special section provide a series of high quality papers which significantly contribute to new theories, applications, and algorithms about uncertain multicriteria decision making. It not only reports recent significant developments but also highlights potential, growing research directions, and future trends, which will benefit researchers in studying theories, applications, and algorithms about uncertain multicriteria/ multiobjective/multiattribute decision making. [ABSTRACT FROM AUTHOR]

Molecular retrosynthesis is a significant and complex problem in the field of chemistry, however, traditional manual synthesis methods not only need well-trained experts but also are time-consuming. With the development of Big Data and machine learning, artificial intelligence (AI) based retrosynthesis is attracting more attention and has become a valuable tool for molecular retrosynthesis. At present, Monte Carlo tree search is a mainstream search framework employed to address this problem. Nevertheless, its search efficiency is compromised by its large search space. Therefore, this paper proposes a novel approach for retrosynthetic route planning based on evolutionary optimization, marking the first use of Evolutionary Algorithm (EA) in the field of multi-step retrosynthesis. The proposed method involves modeling the retrosynthetic problem into an optimization problem, defining the search space and operators. Additionally, to improve the search efficiency, a parallel strategy is implemented. The new approach is applied to four case products and compared with Monte Carlo tree search. The experimental results show that, in comparison to the Monte Carlo tree search algorithm, EA significantly reduces the number of calling single-step model by an average of 53.9%. The time required to search three solutions decreases by an average of 83.9%, and the number of feasible search routes increases by 1.38 times. The source code is available at https://github.com/ilog-ecnu/EvoRRP. [ABSTRACT FROM AUTHOR]

Gasoline blending scheduling uses resource allocation and operation sequencing to meet a refinery's production requirements. The presence of nonlinearity, integer constraints, and a large number of decision variables adds complexity to this problem, posing challenges for traditional and evolutionary algorithms. This paper introduces a novel multiobjective optimization approach driven by a diffusion model (named DMO), which is designed specifically for gasoline blending scheduling. To address integer constraints and generate feasible schedules, the diffusion model creates multiple intermediate distributions between Gaussian noise and the feasible domain. Through iterative processes, the solutions transition from Gaussian noise to feasible schedules while optimizing the objectives using the gradient descent method. DMO achieves simultaneous objective optimization and constraint adherence. Comparative tests are conducted to evaluate DMO's performance across various scales. The experimental results demonstrate that DMO surpasses state-of-the-art multiobjective evolutionary algorithms in terms of efficiency when solving gasoline blending scheduling problems. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EAs) have shown their competitiveness in solving the problem of feature selection. However, limited by their encoding scheme, most of them face the challenge of "curse of dimensionality". To address the issue, in this paper, an objective space constraint-based evolutionary algorithm, named OSC-EA, is proposed for high-dimensional feature selection (HDFS). Although the decision space of EAs for HDFS is very huge, its objective space is the same as that of the low-dimensional feature selection. Based on this fact, in the proposed OSC-EA, the HDFS is firstly modeled as a constrained problem, where a constraint of the objective space is introduced and used to partition the whole objective space into the "feasible region" and the "infeasible region". To handle the constrained problem, a two-stage $\varepsilon$ɛ constraint-based evolutionary scheme is designed. In the first stage, the value of $\varepsilon$ɛ is set to be very small, which ensures that the search concentrates on the "feasible region", and the latent high-quality feature subsets can be found quickly. Then, in the second stage, the value of $\varepsilon$ɛ increases gradually, so that more solutions in the "infeasible region" are considered. Until the end of the scheme, $\varepsilon \rightarrow \infty$ɛ→∞; all the solutions in the objective space are considered. By using the search in the second stage, the quality of the obtained feature subsets is further improved. The empirical results on different high-dimensional datasets demonstrate the effectiveness and efficiency of the proposed OSC-EA. [ABSTRACT FROM AUTHOR]

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Cooperative co-evolution (CC) is an evolutionary algorithm that adopts the divide-and-conquer strategy to solve large-scale optimization problems. It is difficult for CC to specify a suitable subpopulation size to solve different subproblems. The population diversity may be insufficient to search for the global optimum during subpopulations' evolution. In this paper, an adaptive method for enhancing population diversity is embedded in a contribution-based CC. In CC, there are two kinds of subpopulation: the convergent or stagnant subpopulations and the non-convergent and non-stagnant subpopulations. A method is proposed in the paper to evaluate the convergent and stagnant subpopulations' contributions to improving the best overall objective value, which is different from the contribution evaluation on the non-convergent and non-stagnant subpopulations. In each co-evolutionary cycle, the new CC adaptively determines to select a subpopulation, which can make a greater contribution to improving the best overall objective value, between the above two kinds of subpopulation to undergo evolution. When a convergent or stagnant subpopulation is selected to undergo evolution, the subpopulation is re-diversified to enhance its global search capability. Our experimental results and analysis suggest that the new CC algorithm can improve the performance of CC and serves as a competitive solver for large-scale optimization problems. [ABSTRACT FROM AUTHOR]

This paper integrates an estimation of distribution (EoD)-based update operator into decomposition-based multiobjective evolutionary algorithms for binary optimization. The probabilistic model in the update operator is a probability vector, which is adaptively learned from historical information of each subproblem. We show that this update operator can significantly enhance decomposition-based algorithms on a number of benchmark problems. Moreover, we apply the enhanced algorithms to the constrained optimal software product selection (OSPS) problem in the field of search-based software engineering. For this real-world problem, we give its formal definition and then develop a new repair operator based on satisfiability solvers. It is demonstrated by the experimental results that the algorithms equipped with the EoD operator are effective in dealing with this practical problem, particularly for large-scale instances. The interdisciplinary studies in this paper provide a new real-world application scenario for constrained multiobjective binary optimizers and also offer valuable techniques for software engineers in handling the OSPS problem. [ABSTRACT FROM AUTHOR]

Constrained multiobjective optimization problems (CMOPs) are frequently encountered in real-world applications, which usually involve constraints in both the decision and objective spaces. However, current artificial CMOPs never consider constraints in the decision space (i.e., decision constraints) and constraints in the objective space (i.e., objective constraints) at the same time. As a result, they have a limited capability to simulate practical scenes. To remedy this issue, a set of CMOPs, named DOC, is constructed in this paper. It is the first attempt to consider both the decision and objective constraints simultaneously in the design of artificial CMOPs. Specifically, in DOC, various decision constraints (e.g., inequality constraints, equality constraints, linear constraints, and nonlinear constraints) are collected from real-world applications, thus making the feasible region in the decision space have different properties (e.g., nonlinear, extremely small, and multimodal). On the other hand, some simple and controllable objective constraints are devised to reduce the feasible region in the objective space and to make the Pareto front have diverse characteristics (e.g., continuous, discrete, mixed, and degenerate). As a whole, DOC poses a great challenge for a constrained multiobjective evolutionary algorithm (CMOEA) to obtain a set of well-distributed and well-converged feasible solutions. In order to enhance current CMOEAs’ performance on DOC, a simple and efficient two-phase framework, named ToP, is proposed in this paper. In ToP, the first phase is implemented to find the promising feasible area by transforming a CMOP into a constrained single-objective optimization problem. Then in the second phase, a specific CMOEA is executed to obtain the final solutions. ToP is applied to four state-of-the-art CMOEAs, and the experimental results suggest that it is quite effective. [ABSTRACT FROM AUTHOR]

This paper compares three modern and two classical multiobjective optimizers (MOOs) as applied to real-world problems in electromagnetics. The behavior of sophisticated optimizers on simple test functions has been studied exhaustively. In contrast, the algorithms here are tested on practical applications, where the function evaluations are computationally expensive, making the convergence rate a crucial factor. The examples considered include the optimization of a narrowband slot antenna, a mushroom-type electromagnetic bandgap structure, and an ultrawideband Vivaldi antenna. Another popular topic in the literature is in comparing classical MOOs on electromagnetics problems. The modern optimizers chosen in this paper are state of the art and each has a distinct design philosophy. This paper introduces two unique MOOs to the electromagnetics community: BORG, an auto-adaptive genetic algorithm and the Multi-Objective Covariance Matrix Adaptation Evolutionary Strategy (MO-CMA-ES), an extension of the popular single-objective CMA-ES. These algorithms are compared to the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), a Chebysheff scalarization algorithm, and two classical MOOs. This paper will study the behavior of these algorithms on problems in electromagnetics with a limited number of function evaluations using five distinct metrics and will provide useful guidelines and recommended optimizer settings. [ABSTRACT FROM AUTHOR]

This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolved. We analyze the application of NE in games along five different axes, which are the role NE is chosen to play in a game, the different types of neural networks used, the way these networks are evolved, how the fitness is determined and what type of input the network receives. The paper also highlights important open research challenges in the field. [ABSTRACT FROM PUBLISHER]

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When solving constrained optimization problems (COPs) by evolutionary algorithms, the search algorithm plays a crucial role. In general, we expect that the search algorithm has the capability to balance not only diversity and convergence but also constraints and objective function during the evolution. For this purpose, this paper proposes a composite differential evolution (DE) for constrained optimization, which includes three different trial vector generation strategies with distinct advantages. In order to strike a balance between diversity and convergence, one of these three trial vector generation strategies is able to increase diversity, and the other two exhibit the property of convergence. In addition, to accomplish the tradeoff between constraints and objective function, one of the two trial vector generation strategies for convergence is guided by the individual with the least degree of constraint violation in the population, and the other is guided by the individual with the best objective function value in the population. After producing offspring by the proposed composite DE, the feasibility rule and the $\boldsymbol {\varepsilon }$ constrained method are combined elaborately for selection in this paper. Moreover, a restart scheme is proposed to help the population jump out of a local optimum in the infeasible region for some extremely complicated COPs. By assembling the above techniques together, a constrained composite DE is proposed. The experiments on two sets of benchmark test functions with various features, i.e., 24 test functions from IEEE CEC2006 and 18 test functions with 10 dimensions and 30 dimensions from IEEE CEC2010, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The visualization of evolutionary influence graphs is important for performing many real-life tasks such as citation analysis and social influence analysis. The main challenges include how to summarize large-scale, complex, and time-evolving influence graphs, and how to design effective visual metaphors and dynamic representation methods to illustrate influence patterns over time. In this work, we present Eiffel, an integrated visual analytics system that applies triple summarizations on evolutionary influence graphs in the nodal, relational, and temporal dimensions. In numerical experiments, Eiffel summarization results outperformed those of traditional clustering algorithms with respect to the influence-flow-based objective. Moreover, a flow map representation is proposed and adapted to the case of influence graph summarization, which supports two modes of evolutionary visualization (i.e., flip-book and movie) to expedite the analysis of influence graph dynamics. We conducted two controlled user experiments to evaluate our technique on influence graph summarization and visualization respectively. We also showcased the system in the evolutionary influence analysis of two typical scenarios, the citation influence of scientific papers and the social influence of emerging online events. The evaluation results demonstrate the value of Eiffel in the visual analysis of evolutionary influence graphs. [ABSTRACT FROM AUTHOR]

The hardness of fitness functions is an important research topic in the field of evolutionary computation. In theory, this paper can help with understanding the ability of evolutionary algorithms (EAs). In practice, this paper may provide a guideline to the design of benchmarks. The aim of this paper is to answer the following research questions. Given a fitness function class, which functions are the easiest with respect to an EA? Which are the hardest? How are these functions constructed? This paper provides theoretical answers to these questions. The easiest and hardest fitness functions are constructed for an elitist (1 + 1) EA to maximize a class of fitness functions with the same optima. It is demonstrated that the unimodal functions are the easiest and deceptive functions are the hardest in terms of the time-based fitness landscape. This paper also reveals that in a fitness function class, the easiest function to one algorithm may become the hardest to another algorithm, and vice versa. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) considering valve-point effect, multiple fuel options, prohibited operating zones of generation units is a more accurate model compared to a conventional ED model. It is non-smooth and thus evolutionary algorithms (EAs) are so far the only feasible approaches for the model. In Part II of the paper, a new method, the dimensional steepest decline method (DSD), is proposed for the ED with non-smooth objectives. The DSD is based on the local minimum analysis of the ED problem presented in Part I of the paper. The fuel cost's decline rate between each two adjacent singular points is utilized to find the optimal solutions in a serial sequence. The computational complexity of the DSD is analyzed. The DSD has been applied to solve different types of ED problems on different test systems, including large-scale systems. The simulation results show that DSD can obtain more accurate solutions and consume much less time and its advantage is more obvious on large-scale systems, in comparison with the state-of-art EAs. [ABSTRACT FROM PUBLISHER]

In the last two decades, a variety of different types of multiobjective optimization problems (MOPs) have been extensively investigated in the evolutionary computation community. However, most existing evolutionary algorithms encounter difficulties in dealing with MOPs whose Pareto optimal solutions are sparse (i.e., most decision variables of the optimal solutions are zero), especially when the number of decision variables is large. Such large-scale sparse MOPs exist in a wide range of applications, for example, feature selection that aims to find a small subset of features from a large number of candidate features, or structure optimization of neural networks whose connections are sparse to alleviate overfitting. This paper proposes an evolutionary algorithm for solving large-scale sparse MOPs. The proposed algorithm suggests a new population initialization strategy and genetic operators by taking the sparse nature of the Pareto optimal solutions into consideration, to ensure the sparsity of the generated solutions. Moreover, this paper also designs a test suite to assess the performance of the proposed algorithm for large-scale sparse MOPs. The experimental results on the proposed test suite and four application examples demonstrate the superiority of the proposed algorithm over seven existing algorithms in solving large-scale sparse MOPs. [ABSTRACT FROM AUTHOR]

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Generative adversarial networks (GANs) have been effective for learning generative models for real-world data. However, accompanied with the generative tasks becoming more and more challenging, existing GANs (GAN and its variants) tend to suffer from different training problems such as instability and mode collapse. In this paper, we propose a novel GAN framework called evolutionary GANs (E-GANs) for stable GAN training and improved generative performance. Unlike existing GANs, which employ a predefined adversarial objective function alternately training a generator and a discriminator, we evolve a population of generators to play the adversarial game with the discriminator. Different adversarial training objectives are employed as mutation operations and each individual (i.e., generator candidature) are updated based on these mutations. Then, we devise an evaluation mechanism to measure the quality and diversity of generated samples, such that only well-performing generator(s) are preserved and used for further training. In this way, E-GAN overcomes the limitations of an individual adversarial training objective and always preserves the well-performing offspring, contributing to progress in, and the success of GANs. Experiments on several datasets demonstrate that E-GAN achieves convincing generative performance and reduces the training problems inherent in existing GANs. [ABSTRACT FROM AUTHOR]

As an important research topic in intelligent teaching systems, personalized recommendation services of learning resources can effectively solve the “information overload” problem and provide effective learning. However, the traditional learning resource recommendation technology mainly aims to improve recommendation accuracy and cannot effectively ensure the diversity and novelty of recommendation results. In this paper, the learning resource recommendation task is modelled with a multi-objective optimization problem. This paper proposes the Multi-Objective Evolutionary Algorithm-based online learning Resource Recommendation Model to balance the system’s accuracy, novelty, and diversity. The proposed model includes the following four steps: learning clustering, optimization goal setting individual representation, and genetic operator. According to the experimental results, this algorithm can improve the recommendation performance of online learning resources. Compared with the existing recommendation algorithms, more accurate, diverse, and novel learning resource recommendation results can be obtained with the proposed algorithm. [ABSTRACT FROM AUTHOR]

The prior domain knowledge, i.e., the regularity property of continuous multiobjective optimization problems (MOPs), could be learned to guide the search for evolutionary multiobjective optimization. This paper proposes a learning-to-guide strategy (LGS) for assisting the search for multiobjective optimization algorithms in dealing with MOPs. The main idea behind LGS is to capture the regularity via learning techniques to guide the evolutionary search to generate promising offspring solutions. To achieve this, a generative model called the generative topographic mapping (GTM) is adopted to capture the manifold distribution of a population. A set of regular grid points in the latent space are mapped into the decision space within some manifold structures to guide the search for mating with some parents for offspring generation. Following this idea, three alternative LGS-based generation operators are developed and investigated, which combine the local and global information in the offspring generation. To learn the regularity more efficiently in an algorithm, the proposed LGS is embedded in an efficient evolutionary algorithm (called LGSEA). The LGSEA includes an incremental training procedure aimed at reducing the computational cost of GTM training by reusing the built GTM model. The developed algorithm is compared with some newly developed or classical learning-based algorithms on several benchmark problems. The results demonstrate the advantages of LGSEA over other approaches, showcasing its potential for solving complex MOPs. [ABSTRACT FROM AUTHOR]

Building energy management usually involves a number of objectives, such as investment costs, thermal comfort, system resilience, battery life, and many others. However, most existing studies merely consider optimizing less than three objectives since it becomes increasingly difficult as the number of objectives increases. In addition, the optimization of building energy management relies heavily on time-consuming energy component simulators, posing great challenges for conventional evolutionary algorithms that typically require a large number of real function evaluations. To address the above-mentioned issues, this paper formulates a building energy management scenario as a 10-objective optimization problem, aiming to find optimal configurations of power supply components. To solve this expensive many-objective optimization problem, six state-of-the-art multi-objective evolutionary algorithms, five of which are assisted by surrogate models, are compared. The experimental results show that the adaptive reference vector assisted algorithm is proven to be the most competitive one among the six compared algorithms; the five evolutionary algorithms with surrogate assistance always outperform their counterpart without the surrogate, although the kriging-assisted reference vector assisted evolutionary algorithm only performs slightly better than the algorithm without surrogate assistance in dealing with the 10-objective building energy management problem. By analyzing the non-dominated solutions obtained by the six algorithms, an optimal configuration of power supply components can be obtained within an affordable period of time, providing decision makers with new insights into the building energy management problem. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

This paper examines evolutionary nonlinear projection (NLP), a form of multidimensional scaling (MDS) performed with an evolutionary algorithm. MDS is a family of techniques for producing a low dimensional data set whose points have a one-to-one correspondence with the points of a higher dimensional data set with the added property that distances or dissimilarities in the higher dimensional space are preserved as much as possible in the lower dimensional space. The goal is typically visualization but may also be clustering or other forms of analysis. In this paper, we review current methods of NLP and go on to characterize NLP as an evolutionary computation problem, gaining insight into MDS as an optimization problem. Two different mutation operators, one introduced in this paper, are compared and parameter studies are performed on mutation rate and population size. The new mutation operator is found to be superior. NLP is found to be a problem where small population sizes exhibit superior performance. It is demonstrated experimentally that NLP is a multimodal optimization problem. Two broad classes of projection problems are identified, one of which yields consistent high-quality results and the other of which has many optima, all of low quality. A number of applications of the technique are presented, including projections of feature vectors for polyominos, of vectors that are members of an error correcting code, of behavioral assessments of a collection of agents, and of features derived from DNA sequences. [ABSTRACT FROM AUTHOR]

Author Names in Native Languages IEEE supports the publication of author names in the native language alongside the English versions of the names in the author list of an article. An IEEE LaTeX style file or Word Template can also be obtained at URL: http://www.ieee.org/publications standards/publications/authors/ author templates.html. For manuscript preparation guidelines, visit the Preparation of Papers for IEEE TRANSACTIONS AND JOURNALS at the following URL: http://www.ieee.org/publications standards/ publications/authors/author templates.html. [Extracted from the article]

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Due to natural disasters or system failures, the facility has the risk of disruption, and thus improving the location reliability under uncertainty of facilities becomes an important issue. In this paper, we propose a multi-objective facility location problem under uncertainty of facilities, where two objectives on reliability are constructed and multiple coverage with variable radius is imposed to reduce the influence caused by the facility disruption. A dual-population based evolutionary algorithm is also suggested to address this problem, where one population is for the location optimization and the other population is for the radius optimization. These two populations iteratively exchange the information obtained from elite solutions during the evolution to collaboratively search for the optimal solutions of the problem. The location population provides the high-quality location schemes for radius population in evaluating the quality of radii of each location, whereas the radius population equips the proper radii for location population in determining the good location schemes. Experimental results indicate that the proposed model can effectively improve the location reliability and the proposed method can obtain higher quality optimal solutions in comparison with four state-of-the-art algorithms. Moreover, the proposed method is applied to a real-world facility location with uncertainty of express cabinets in Tianjin, China, and it produces satisfactory location schemes. [ABSTRACT FROM AUTHOR]

RNA inverse folding problem is a bioinformatics problem where the objective is to find an RNA sequence that folds into a given target secondary structure. In this paper, we use evolutionary computation to solve a new and innovative multiobjective definition of this problem. In this new multiobjective definition of the problem, we have considered the similarity between target and predicted structures as a constraint, and three objective functions: 1) partition function (free energy of the ensemble); 2) ensemble diversity; and 3) nucleotides composition. The multiobjective metaheuristic to design RNA sequences (m2dRNAs) proposed in this paper is compared against other RNA inverse folding methods published in the literature, such as RNAinverse, RNA secondary structure designer, inverse folding of RNA, MODENA, NUPACK, fRNAkenstein, dynamics in sequence space optimization, RNAiFOLD, antaRNA, evolutionary RNA design, and Eterna players. After a comprehensive comparative study on two well-known benchmarks (Rfam and Eterna100), we conclude that m2dRNAs is capable of obtaining very promising results in terms of both quality of RNA designs and required runtime. The source code of m2dRNAs is available at http://arco.unex.es/arl/m2dRNAs-source_code.zip. [ABSTRACT FROM AUTHOR]

Conventional evolutionary algorithms (EAs) are not well suited for solving expensive optimization problems due to the fact that they often require a large number of fitness evaluations to obtain acceptable solutions. To alleviate the difficulty, this paper presents a multitasking evolutionary optimization framework for solving computationally expensive problems. In the framework, knowledge is transferred from a number of computationally cheap optimization problems to help the solution of the expensive problem on the basis of the recently proposed multifactorial EA (MFEA), leading to a faster convergence of the expensive problem. However, existing MFEAs do not work well in solving multitasking problems whose optimums do not lie in the same location or when the dimensions of the decision space are not the same. To address the above issues, the existing MFEA is generalized by proposing two strategies, one for decision variable translation and the other for decision variable shuffling, to facilitate knowledge transfer between optimization problems having different locations of the optimums and different numbers of decision variables. To assess the effectiveness of the generalized MFEA (G-MFEA), empirical studies have been conducted on eight multitasking instances and eight test problems for expensive optimization. The experimental results demonstrate that the proposed G-MFEA works more efficiently for multitasking optimization and successfully accelerates the convergence of expensive optimization problems compared to single-task optimization. [ABSTRACT FROM AUTHOR]

Solutions to most practical numerical optimization problems must be evaluated for their performance over a number of different loading or operating conditions, which we refer here as scenarios. Therefore, a meaningful and resilient optimal solution must be such that it remains feasible under all scenarios and performs close to an individual optimal solution corresponding to each scenario. Despite its practical importance, multiscenario consideration has received a lukewarm attention, particularly in the context of multiobjective optimization. The usual practice is to optimize for the worst-case scenario. In this paper, we review existing methodologies in this direction and set our goal to suggest a new and potential population-based method for handling multiple scenarios by defining scenario-wise domination principle and scenario-wise diversity-preserving operators. To evaluate, the proposed method is applied to a number of numerical test problems and engineering design problems with a detail explanation of the obtained results and compared with an existing method. This first systematic evolutionary-based multiscenario, multiobjective optimization study on numerical problems indicates that multiple scenarios can be handled in an integrated manner using an evolutionary multiobjective optimization framework to find a well-balanced compromise set of solutions to multiple scenarios and maintain a tradeoff among multiple objectives. In comparison to an existing serial multiple optimization approach, the proposed approach finds a set of compromised tradeoff solutions simultaneously. An achievement of multiobjective tradeoff and multiscenario tradeoff is algorithmically challenging, but due to its practical appeal, further research and application must be spent. [ABSTRACT FROM AUTHOR]

We propose an evolution strategy algorithm using a sparse plus low rank model for large-scale optimization in this paper. We first develop a rank one evolution strategy using a single principal search direction. It is of linear complexity. Then we extend it to multiple search directions, and develop a rank- ${m}$ evolution strategy. We illustrate that the principal search direction accumulates the natural gradients with respect to the distribution mean, and acts as a momentum term. Further, we analyze the optimal low rank approximation to the covariance matrix, and experimentally show that the principal search direction can effectively learn the long valley of the function with predominant search direction. Then we investigate the effects of Hessian on the algorithm performance. We conduct experiments on a class of test problems and the CEC’2010 LSGO benchmarks. The experimental results validate the effectiveness of our proposed algorithms. [ABSTRACT FROM AUTHOR]

For many real-world optimization problems, evaluating a solution involves running a computationally expensive simulation model. This makes it challenging to use evolutionary algorithms that usually have to evaluate thousands of solutions before converging. On the other hand, in many cases, even a prematurely stopped run of the simulation may serve as a cheaper, albeit less accurate (low fidelity), estimate of the true fitness value. For evolutionary optimization, this opens up the opportunity to decide about the simulation run length for each individual. In this paper, we propose a mechanism that is capable of learning the appropriate simulation run length for each solution. To test our approach, we propose two new benchmark problems, one simple artificial benchmark function and one benchmark based on a computational fluid dynamics (CFDs) simulation scenario to design a toy submarine. As we demonstrate, our proposed algorithm finds good solutions much more quickly than always using the full CFDs simulation and provides much better solution quality than a strategy of progressively increasing the fidelity level over the course of optimization. [ABSTRACT FROM PUBLISHER]

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The complex network has attracted increasing attention and shown effectiveness in modeling multifarious systems. Focusing on selecting members with good spreading ability, the influence maximization problem is of great significance in network-based information diffusion tasks. Plenty of attention has been paid to simulating the diffusion process and choosing influential seeds. However, errors and attacks typically threaten the normal function of networked systems, and few studies have considered the influence maximization problem under structural failures. Therefore, a quantitative measure with a changeable parameter is first developed in this paper to tackle the unpredictable destruction percentage on networks. Further, limitations on the existing methods are shown experimentally. To address these limitations, the evolutionary multitasking paradigm is employed, and several problem-specific operators are developed. On top of these developments, a multi-factorial evolutionary algorithm is devised to find seeds with robust influence ability, termed MFEARIM, where the genetic information for both myopia and holistic areas is considered to improve the search ability. Additionally, an asynchronous strategy is designed to efficiently tackle tasks with distinct costs, and the convergence of the search process can thus be accelerated. Experiments on several synthetic and real-world networks validate the competitive performance of MFEARIM over the existing methods. [ABSTRACT FROM AUTHOR]

Learning to optimize the area under the receiver operating characteristics curve (AUC) performance for imbalanced data has attracted much attention in recent years. Although there have been several methods of AUC optimization, scaling up AUC optimization is still an open issue due to its pairwise learning style. Maximizing AUC in the large-scale dataset can be considered as a non-convex and expensive problem. Inspired by the characteristic of pairwise learning, the cheap AUC optimization task with a small-scale dataset sampled from the large-scale dataset is constructed to promote the AUC accuracy of the original, large-scale, and expensive AUC optimization task. This paper develops an evolutionary multitasking framework (termed EMTAUC) to make full use of information among the constructed cheap and expensive tasks to obtain higher performance. In EMTAUC, one mission is to optimize AUC from the sampled dataset, and the other is to maximize AUC from the original dataset. Moreover, due to the cheap task containing limited knowledge, a strategy for dynamically adjusting the data structure of inexpensive tasks is proposed to introduce more knowledge into the multitasking AUC optimization environment. The performance of the proposed method is evaluated on a series of binary classification datasets. The experimental results demonstrate that EMTAUC is highly competitive to single task methods and online methods. Supplementary materials and source code implementation of EMTAUC can be accessed at https://github.com/xiaofangxd/EMTAUC. [ABSTRACT FROM AUTHOR]

This paper aims to provide an introduction to genetic algorithms and their three main components, i.e., the representation of solutions and their modification through mutation and crossover operators. It has been specifically designed as introduction for newcomers to this exciting research area. This short paper represents a summary of the full paper found online in IEEE Xplore. The latter provides interactive components for a hands-on exploration of the covered material. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]