Evolutionary Computation Research Research Articles

Extending Genetic Algorithms with Biological Life-Cycle Dynamics.

In this paper, we aim to enhance genetic algorithms (GAs) by integrating a dynamic model based on biological life cycles. This study addresses the challenge of maintaining diversity and adaptability in GAs by incorporating stages of birth, growth, reproduction, and death into the algorithm's framework. We consider an asynchronous execution of life cycle stages to individuals in the population, ensuring a steady-state evolution that preserves high-quality solutions while maintaining diversity. Experimental results demonstrate that the proposed extension outperforms traditional GAs and is as good or better than other well-known and well established algorithms like PSO and EvoSpace in various benchmark problems, particularly regarding convergence speed and solution qu/ality. The study concludes that incorporating biological life-cycle dynamics into GAs enhances their robustness and efficiency, offering a promising direction for future research in evolutionary computation.

Evolutionary computation for feature selection in classification: A comprehensive survey of solutions, applications and challenges

Feature selection (FS), as one of the most significant preprocessing techniques in the fields of machine learning and pattern recognition, has received great attention. In recent years, evolutionary computation has become a popular technique for handling FS problems due to its superior global search performance. In this paper, a comprehensive review of evolutionary computation research on the FS problems is presented. Firstly, a new taxonomy for the basic components of evolutionary feature selection algorithms (EFSs) is proposed, including encoding strategy, population initialization, population updating, local search, multi-FS hybrid and ensemble. Secondly, we summarize the latest research progress of EFSs on some new and complex scenarios, including large-scale high-dimensional data, multi-objective/metric scenario, multi-label data, distributed storage data, multi-task scenario, multi-modal scenario, interpretable FS and stable FS, etc. Moreover, this survey provides also an in-depth analysis of real-world applications of EFSs, such as hyperspectral band selection, bioinformatics gene selection, text classification and fault detection, etc. Finally, several opportunities for future work are pointed out.

A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms

The multi-objective optimization problem is difficult to solve with conventional optimization methods and algorithms because there are conflicts among several optimization objectives and functions. Through the efforts of researchers and experts from different fields for the last 30 years, the research and application of multi-objective evolutionary algorithms (MOEA) have made excellent progress in solving such problems. MOEA has become one of the primary used methods and technologies in the realm of multi-objective optimization. It is also a hotspot in the evolutionary computation research community. This survey provides a comprehensive investigation of MOEA algorithms that have emerged in recent decades and summarizes and classifies the classical MOEAs by evolutionary mechanism from the viewpoint of the search strategy. This paper divides them into three categories considering the search strategy of MOEA, i.e., decomposition-based MOEA algorithms, dominant relation-based MOEA algorithms, and evaluation index-based MOEA algorithms. This paper selects the relevant representative algorithms for a detailed summary and analysis. As a prospective research direction, we propose to combine the chaotic evolution algorithm with these representative search strategies for improving the search capability of multi-objective optimization algorithms. The capability of the new multi-objective evolutionary algorithm has been discussed, which further proposes the future research direction of MOEA. It also lays a foundation for the application and development of MOEA with these prospective works in the future.

Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming

Genetic programming (GP) automatically designs programs. Evolutionary programming (EP) is a real-valued global optimisation method. EP uses a probability distribution as a mutation operator, such as Gaussian, Cauchy, or Lévy distribution. This study proposes a hyper-heuristic approach that employs GP to automatically design different mutation operators for EP. At each generation, the EP algorithm can adaptively explore the search space according to historical information. The experimental results demonstrate that the EP with adaptive mutation operators, designed by the proposed hyper-heuristics, exhibits improved performance over other EP versions (both manually and automatically designed). Many researchers in evolutionary computation advocate adaptive search operators (which do adapt over time) over non-adaptive operators (which do not alter over time). The core motive of this study is that we can automatically design adaptive mutation operators that outperform automatically designed non-adaptive mutation operators.

A survey on evolutionary computation for complex continuous optimization

Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

A biological perspective on evolutionary computation

Evolutionary computation is inspired by the mechanisms of biological evolution. With algorithmic improvements and increasing computing resources, evolutionary computation has discovered creative and innovative solutions to challenging practical problems. This paper evaluates how today’s evolutionary computation compares to biological evolution and how it may fall short. A small number of well-accepted characteristics of biological evolution are considered: openendedness, major transitions in organizational structure, neutrality and genetic drift, multi-objectivity, complex genotype-to-phenotype mappings and co-evolution. Evolutionary computation exhibits many of these to some extent but more can be achieved by scaling up with available computing and by emulating biology more carefully. In particular, evolutionary computation diverges from biological evolution in three key respects: it is based on small populations and strong selection; it typically uses direct genotype-to-phenotype mappings; and it does not achieve major organizational transitions. These shortcomings suggest a roadmap for future evolutionary computation research, and point to gaps in our understanding of how biology discovers major transitions. Advances in these areas can lead to evolutionary computation that approaches the complexity and flexibility of biology, and can serve as an executable model of biological processes. Evolutionary computation is inspired by biological evolution and exhibits characteristics familiar from biology such as openendedness, multi-objectivity and co-evolution. This Perspective highlights where major differences still exist, and where the field of evolutionary computation could attempt to approach features from biological evolution more closely, namely neutrality and random drift, complex genotype-to-phenotype mappings with rich environmental interactions and major organizational transitions.

The General Combinatorial Optimization Problem: Towards Automated Algorithm Design

This paper defines a new combinatorial optimization problem, namely General Combinatorial Optimization Problem (GCOP), whose decision variables are a set of parametric algorithmic components, i.e. algorithm design decisions. The solutions of GCOP, i.e. compositions of algorithmic components, thus represent different generic search algorithms. The objective of GCOP is to find the optimal algorithmic compositions for solving the given optimization problems. Solving the GCOP is thus equivalent to automatically designing the best algorithms for optimization problems. Despite recent advances, the evolutionary computation and optimization research communities are yet to embrace formal standards that underpin automated algorithm design. In this position paper, we establish GCOP as a new standard to define different search algorithms within one unified model. We demonstrate the new GCOP model to standardize various search algorithms as well as selection hyperheuristics. A taxonomy is defined to distinguish several widely used terminologies in automated algorithm design, namely automated algorithm composition, configuration and selection. We would like to encourage a new line of exciting research directions addressing several challenging research issues including algorithm generality, algorithm reusability, and automated algorithm design.

Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons

For solving constrained multiobjective optimization problems (CMOPs), many algorithms have been proposed in the evolutionary computation research community for the past two decades. Generally, the effectiveness of an algorithm for CMOPs is evaluated by artificial test problems. However, after a brief review of current artificial test problems, we have found that they are not well-designed and fail to reflect the characteristics of real-world applications (e.g., small feasibility ratio). Thus, in this paper, we first propose a new constraint construction method to facilitate the systematic design of test problems. Then, on the basis of this method, we design a new test suite consisting of 14 instances, which covers diverse characteristics extracted from real-world CMOPs and can be divided into four types. Considering that the comprehensive performance comparisons among the constraint-handling techniques (CHTs) remain scarce, we choose several representative CHTs and compare their performance on our test suite. The performance comparisons identify the strengths and weaknesses of different CHTs on different types of CMOPs and provide guidelines on how to select/design a CHT in a specific scenario.

Genetic programming in the twenty-first century: a bibliometric and content-based analysis from both sides of the fence

In this work we present an extensive bibliometric and content-based analysis of the scientific literature about genetic programming in the twenty-first century. Our work has two key peculiarities. First, we revealed the topics emerging from the literature based on an unsupervised analysis of the textual content of titles and abstracts. Second, we executed all of our analyses twice, once on the papers published in the venues that are typical of the evolutionary computation research community and once on those published in all the other venues. This view from “both sides of the fence” allows us to gain broader and deeper insights into the actual contributions of our community.

Foreword

Automated algorithm selection and configuration are key enabling approaches for improving the state of the art in solving a broad range of important problems, by exploiting performance complementarity between multiple algorithms for the same problem (selection) and by realising the latent performance potential in parameterised algorithms (configuration). Compared to traditional, manual approaches, these techniques are not only more efficient and rely less on human expertise, but also provide a more principled basis for algorithm selection and configuration, enable fairer comparisons between algorithms, and facilitate new insights into which algorithmic techniques and components work best and under which circumstances.Work on automated algorithm selection, configuration, and related areas spans multiple, weakly connected communities, including artificial intelligence, evolutionary computation, mathematical optimisation and operations research. This special issue follows a Dagstuhl seminar on the same topic, held in October 2016, and is intended as a further step toward creating synergy between those communities.For this special issue, we selected, from a substantial number of submissions, seven papers that jointly cover a broad range of topics and approaches, including various methods for algorithm selection and configuration, search landscape analysis, software engineering aspects, as well as applications to prominent discrete and continuous, single- and multiobjective optimisation problems.The survey paper by Kerschke et al. provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Unlike earlier surveys, it covers applications to discrete and continuous problems; it also situates algorithm selection in the context of a wide spectrum of conceptually related approaches, such as algorithm configuration, scheduling, and portfolio selection, and discusses open challenges.Alyaha and Rowe present a study of landscape characteristics for three NP-hard combinatorial optimisation problems: number partitioning and two variants of knapsack problems. A comparative analysis of landscapes induced by different neighbourhood operators, penalty functions, and problem parameters led to improved problem understanding and to a heuristic for selecting the most appropriate local search operator.The paper by Saalem et al. introduces an approach for assessing the effectiveness of exploratory landscape features with respect to their impact on the performance of algorithm selection methods. A model-based framework for continuous black-box problem comparison using Gaussian process (GP) regression is presented, leading to a flexible surrogate model for problem landscapes. The GP substantially facilitates problem comparison while efficiently measuring model quality.Kerschke et al. present an automated algorithm selection method for single-objective continuous black-box optimisation problems, based on sophisticated exploratory landscape analysis and machine learning techniques. The efficiency of the selector is illustrated on the Black-Box Optimisation Benchmark (BBOB), by improving the performance over the single best solver from a representative set of solvers by a factor of two on average.In the paper by Wessing et al., an improved initialisation procedure for a well-known automated configuration procedure, irace, is shown to outperform classical uniform sampling of algorithm configurations. Techniques from the design and analysis of computer experiments are applied, that is, several Latin hypercube sampling methods that are able to handle categorical and numerical parameters that may be conditional (nested) on the value of other (branching) parameters.Blot et al. investigate the automatic configuration of multiobjective local search algorithms for permutation problems—specifically, for the bi-objective permutation flowshop and travelling salesman problems. They consider two performance metrics as configuration objectives and study several approaches for automated configuration according to these two objectives, presenting clear evidence that multiobjective algorithms are best configured using a multiobjective configurator.Finally, Swan et al. introduce a new approach to the automation of the design of metaheuristics, the so-called Automated Open-Closed Principle (AOCP), which addresses the problem of state dependencies between configurable components in flexible algorithm frameworks. AOCP extends current approaches to automated algorithm configuration by offering a principled software engineering approach to ensure the automated assembly of algorithms from an extensible palette of components.By now, automated algorithm selection and configuration are mature research areas, as witnessed not only by the sophistication of the methods and the success of their many applications, but also by a large and fast-growing body of literature. Still, we see much room for further work, spanning the gamut from theoretical to empirical studies, from new methodology to applications. We hope that this special issue will inspire interest and future work in these dynamic and exciting research areas.

AnD: A many-objective evolutionary algorithm with angle-based selection and shift-based density estimation

Evolutionary many-objective optimization has been gaining increasing attention from the evolutionary computation research community. Much effort has been devoted to addressing this issue by improving the scalability of multiobjective evolutionary algorithms, such as Pareto-based, decomposition-based, and indicator-based approaches. Different from current work, we propose an alternative algorithm in this paper called AnD, which consists of an angle-based selection strategy and a shift-based density estimation strategy. These two strategies are employed in the environmental selection to delete poor individuals one by one. Specifically, the former is devised to find a pair of individuals with the minimum vector angle, which means that these two individuals have the most similar search directions. The latter, which takes both diversity and convergence into account, is adopted to compare these two individuals and to delete the worse one. AnD has a simple structure, few parameters, and no complicated operators. The performance of AnD is compared with that of seven state-of-the-art many-objective evolutionary algorithms on a variety of benchmark test problems with up to 15 objectives. The results suggest that AnD can achieve highly competitive performance. In addition, we also verify that AnD can be readily extended to solve constrained many-objective optimization problems.

A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications

Bilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as a constraint. These problems have received significant attention from the mathematical programming community. Only limited work exists on bilevel problems using evolutionary computation techniques; however, recently there has been an increasing interest due to the proliferation of practical applications and the potential of evolutionary algorithms in tackling these problems. This paper provides a comprehensive review on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary. A number of potential application problems are also discussed. To offer the readers insights on the prominent developments in the field of bilevel optimization, we have performed an automated text-analysis of an extended list of papers published on bilevel optimization to date. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities.

Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This article is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

Just because it works: a response to comments on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms”

This response examines the context and implications of the comments to "On the Mapping of Genotype to Phenotype in Evolutionary Algorithms" that appears in this journal. The notion of metaphor is first considered and then the general themes of the commentaries addressed. The response subsequently focuses on representation and operators, noting that many of the comments support our basic premise. The main conclusion is that Sterelny's conditions do form a suitable basis for representation and operator design and that the collection of responses form an excellent basis for further discussion and research in evolutionary computation.

$${\lambda }$$ λ -LGP: an improved version of linear genetic programming evaluated in the Ant Trail problem

The Ant Trail problem has been widely investigated as a benchmark for automatic design of algorithms. One must design the program of a virtual ant to collect all pieces of food located in different places of a map, which may have obstacles, in a predefined limit of steps. This is a challenging problem, but several evolutionary computation (EC) researchers have reported methods with good results. In this paper, we propose an EC method called \({\lambda }\)-linear genetic programming (\({\lambda }\)-LGP), a variation of the well-known linear genetic programming (LGP) algorithm. Starting with an LGP based only on effective macro- and micro-mutations, the \({\lambda }\)-LGP proposed in this work consists in extending how the individuals are chosen for reproduction. In this model, a number (\({\lambda }\)) of mutations is applied to each individual, trying to explore its neighboring fitness regions; such individual might be replaced by one of its children according to different criteria. Several configurations were tested over three different trails: the Santa Fe, the Los Altos Hill, and the John Muir. Results show a very significant improvement over LGP by using this proposed variation. Also, \({\lambda }\)-LGP outperformed not only LGP, but also other state-of-the-art methods from the literature.

Improving experimental methods on success rates in evolutionary computation

Due to the complexity of theoretical approaches in evolutionary computation (EC), research has being largely performed on experimental basis. One popular measure used by the EC community is the success rate (SR), which is used alone or as part of more complex measures such as Koza’s computational effort in genetic programming. A common practice in EC is to report just a punctual estimation of the SR, without additional information about its associated uncertainty. We aim to motivate EC researchers to adopt more rigorous practices when working with SRs. In particular, we introduce the importance of correctly reporting this measure and highlight its binomial nature. Unfortunately, this fact is usually overlooked in the literature. Considering the binomiality of the SR opens the whole corpus of binomial statistics to EA research and practice. In particular, we focus on studying several methods to compute SR confidence intervals, the factors that determine their quality in terms of coverage probability and interval length. Due to its practical interest, we also briefly discuss the number of required runs to build confidence intervals with a certain quality, providing a sound method to set the number of runs, one of the most important experimental settings in EC. Evidence suggests that Wilson is, on average, a reliable and simple method to bound an estimation of SR with confidence intervals, while the standard method, which is quite popular because of its conceptual simplicity, should be avoided in any case. However, other methods can also be of interest under certain circumstances. We encourage to report the number of trials and successes, as well as the interval, to ease further comparability of the results.

Multiobjective Multifactorial Optimization in Evolutionary Multitasking

In recent decades, the field of multiobjective optimization has attracted considerable interest among evolutionary computation researchers. One of the main features that makes evolutionary methods particularly appealing for multiobjective problems is the implicit parallelism offered by a population, which enables simultaneous convergence toward the entire Pareto front. While a plethora of related algorithms have been proposed till date, a common attribute among them is that they focus on efficiently solving only a single optimization problem at a time. Despite the known power of implicit parallelism, seldom has an attempt been made to multitask, i.e., to solve multiple optimization problems simultaneously. It is contended that the notion of evolutionary multitasking leads to the possibility of automated transfer of information across different optimization exercises that may share underlying similarities, thereby facilitating improved convergence characteristics. In particular, the potential for automated transfer is deemed invaluable from the standpoint of engineering design exercises where manual knowledge adaptation and reuse are routine. Accordingly, in this paper, we present a realization of the evolutionary multitasking paradigm within the domain of multiobjective optimization. The efficacy of the associated evolutionary algorithm is demonstrated on some benchmark test functions as well as on a real-world manufacturing process design problem from the composites industry.

The training set and generalization in grammatical evolution for autonomous agent navigation

Over recent years, evolutionary computation research has begun to emphasize the issue of generalization. Instead of evolving solutions that are optimized for a particular problem instance, the goal is to evolve solutions that can generalize to various different scenarios. This paper compares objective-based search and novelty search on a set of generalization oriented experiments for a navigation task using grammatical evolution (GE). In particular, this paper studies the impact that the training set has on the generalization of evolved solutions, considering: (1) the training set size; (2) the manner in which the training set is chosen (random or manual); and (3) if the training set is fixed throughout the run or dynamically changed every generation. Experimental results suggest that novelty search outperforms objective-based search in terms of evolving navigation behaviors that are able to cope with different initial conditions. The traditional objective-based search requires larger training sets and its performance degrades when the training set is not fixed. On the other hand, novelty search seems to be robust to different training sets, finding general solutions in almost all of the studied conditions with almost perfect generalization in many scenarios.

Evolutionary design assistants for architecture

Evolutionary design assistants for architecture

Evolutionary design assistants for architecture

Evolutionary design assistants for architecture