Evolutionary Computation最新文献_第8页

Uncrowded Hypervolume-Based Multiobjective Optimization with Gene-Pool Optimal Mixing 基于非拥挤超容量的基因库优化混合多目标优化

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-09-01 DOI: 10.1162/evco_a_00303

S.C. Maree;T. Alderliesten;P.A.N. Bosman

{"title":"Uncrowded Hypervolume-Based Multiobjective Optimization with Gene-Pool Optimal Mixing","authors":"S.C. Maree;T. Alderliesten;P.A.N. Bosman","doi":"10.1162/evco_a_00303","DOIUrl":"10.1162/evco_a_00303","url":null,"abstract":"Domination-based multiobjective (MO) evolutionary algorithms (EAs) are today arguably the most frequently used type of MOEA. These methods, however, stagnate when the majority of the population becomes nondominated, preventing further convergence to the Pareto set. Hypervolume-based MO optimization has shown promising results to overcome this. Direct use of the hypervolume, however, results in no selection pressure for dominated solutions. The recently introduced Sofomore framework overcomes this by solving multiple interleaved single-objective dynamic problems that iteratively improve a single approximation set, based on the uncrowded hypervolume improvement (UHVI). It thereby however loses many advantages of population-based MO optimization, such as handling multimodality. Here, we reformulate the UHVI as a quality measure for approximation sets, called the uncrowded hypervolume (UHV), which can be used to directly solve MO optimization problems with a single-objective optimizer. We use the state-of-the-art gene-pool optimal mixing evolutionary algorithm (GOMEA) that is capable of efficiently exploiting the intrinsically available grey-box properties of this problem. The resulting algorithm, UHV-GOMEA, is compared with Sofomore equipped with GOMEA, and the domination-based MO-GOMEA. In doing so, we investigate in which scenarios either domination-based or hypervolume-based methods are preferred. Finally, we construct a simple hybrid approach that combines MO-GOMEA with UHV-GOMEA and outperforms both.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 3","pages":"329-353"},"PeriodicalIF":6.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39702735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Regret-Based Nash Equilibrium Sorting Genetic Algorithm for Combinatorial Game Theory Problems with Multiple Players 基于回归的Nash均衡排序遗传算法求解多参与者组合博弈论问题

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-09-01 DOI: 10.1162/evco_a_00308

Abdullah Konak;Sadan Kulturel-Konak

引用次数: 2

On the Construction of Pareto-Compliant Combined Indicators 关于帕累托相容组合指标的构建

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-09-01 DOI: 10.1162/evco_a_00307

J. G. Falcón-Cardona;M. T. M. Emmerich;C. A. Coello Coello

{"title":"On the Construction of Pareto-Compliant Combined Indicators","authors":"J. G. Falcón-Cardona;M. T. M. Emmerich;C. A. Coello Coello","doi":"10.1162/evco_a_00307","DOIUrl":"10.1162/evco_a_00307","url":null,"abstract":"The most relevant property that a quality indicator (QI) is expected to have is Pareto compliance, which means that every time an approximation set strictly dominates another in a Pareto sense, the indicator must reflect this. The hypervolume indicator and its variants are the only unary QIs known to be Pareto-compliant but there are many commonly used weakly Pareto-compliant indicators such as R2, IGD+, and ε+. Currently, an open research area is related to finding new Pareto-compliant indicators whose preferences are different from those of the hypervolume indicator. In this article, we propose a theoretical basis to combine existing weakly Pareto-compliant indicators with at least one being Pareto-compliant, such that the resulting combined indicator is Pareto-compliant as well. Most importantly, we show that the combination of Pareto-compliant QIs with weakly Pareto-compliant indicators leads to indicators that inherit properties of the weakly compliant indicators in terms of optimal point distributions. The consequences of these new combined indicators are threefold: (1) to increase the variety of available Pareto-compliant QIs by correcting weakly Pareto-compliant indicators, (2) to introduce a general framework for the combination of QIs, and (3) to generate new selection mechanisms for multiobjective evolutionary algorithms where it is possible to achieve/adjust desired distributions on the Pareto front.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 3","pages":"381-408"},"PeriodicalIF":6.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39934482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Faster Convergence in Multiobjective Optimization Algorithms Based on Decomposition 基于分解的多目标优化算法的快速收敛性

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-09-01 DOI: 10.1162/evco_a_00306

Yuri Lavinas;Marcelo Ladeira;Claus Aranha

{"title":"Faster Convergence in Multiobjective Optimization Algorithms Based on Decomposition","authors":"Yuri Lavinas;Marcelo Ladeira;Claus Aranha","doi":"10.1162/evco_a_00306","DOIUrl":"10.1162/evco_a_00306","url":null,"abstract":"The Resource Allocation approach (RA) improves the performance of MOEA/D by maintaining a big population and updating few solutions each generation. However, most of the studies on RA generally focused on the properties of different Resource Allocation metrics. Thus, it is still uncertain what the main factors are that lead to increments in performance of MOEA/D with RA. This study investigates the effects of MOEA/D with the Partial Update Strategy (PS) in an extensive set of MOPs to generate insights into correspondences of MOEA/D with the partial update and MOEA/D with small population size and big population size. Our work undertakes an in-depth analysis of the populational dynamics behaviour considering their final approximation Pareto sets, anytime hypervolume performance, attained regions, and number of unique nondominated solutions. Our results indicate that MOEA/D with partial update progresses with the search as fast as MOEA/D with small population size and explores the search space as MOEA/D with big population size. MOEA/D with partial update can mitigate common problems related to population size choice with better convergence speed in most MOPs, as shown by the results of hypervolume and number of unique nondominated solutions, and as the anytime performance and Empirical Attainment Function indicate.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 3","pages":"355-380"},"PeriodicalIF":6.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39906957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Using Well-Understood Single-Objective Functions in Multiobjective Black-Box Optimization Test Suites 在多目标黑盒优化测试套件中使用理解良好的单目标函数

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-06-01 DOI: 10.1162/evco_a_00298

Dimo Brockhoff;Anne Auger;Nikolaus Hansen;Tea Tušar

{"title":"Using Well-Understood Single-Objective Functions in Multiobjective Black-Box Optimization Test Suites","authors":"Dimo Brockhoff;Anne Auger;Nikolaus Hansen;Tea Tušar","doi":"10.1162/evco_a_00298","DOIUrl":"10.1162/evco_a_00298","url":null,"abstract":"Several test function suites are being used for numerical benchmarking of multiobjective optimization algorithms. While they have some desirable properties, such as well-understood Pareto sets and Pareto fronts of various shapes, most of the currently used functions possess characteristics that are arguably underrepresented in real-world problems such as separability, optima located exactly at the boundary constraints, and the existence of variables that solely control the distance between a solution and the Pareto front. Via the alternative construction of combining existing single-objective problems from the literature, we describe the bbob-biobj test suite with 55 bi-objective functions in continuous domain, and its extended version with 92 bi-objective functions (bbob-biobj-ext). Both test suites have been implemented in the COCO platform for black-box optimization benchmarking and various visualizations of the test functions are shown to reveal their properties. Besides providing details on the construction of these problems and presenting their (known) properties, this article also aims at giving the rationale behind our approach in terms of groups of functions with similar properties, objective space normalization, and problem instances. The latter allows us to easily compare the performance of deterministic and stochastic solvers, which is an often overlooked issue in benchmarking.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 2","pages":"165-193"},"PeriodicalIF":6.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39555350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

Selection Heuristics on Semantic Genetic Programming for Classification Problems 分类问题的语义遗传规划选择启发式算法

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-06-01 DOI: 10.1162/evco_a_00297

Claudia N. Sánchez;Mario Graff

{"title":"Selection Heuristics on Semantic Genetic Programming for Classification Problems","authors":"Claudia N. Sánchez;Mario Graff","doi":"10.1162/evco_a_00297","DOIUrl":"10.1162/evco_a_00297","url":null,"abstract":"Individual semantics have been used for guiding the learning process of Genetic Programming. Novel genetic operators and different ways of performing parent selection have been proposed with the use of semantics. The latter is the focus of this contribution by proposing three heuristics for parent selection that measure the similarity among individuals' semantics for choosing parents that enhance the addition, Naive Bayes, and Nearest Centroid. To the best of our knowledge, this is the first time that functions' properties are used for guiding the learning process. As the heuristics were created based on the properties of these functions, we apply them only when they are used to create offspring. The similarity functions considered are the cosine similarity, Pearson's correlation, and agreement. We analyze these heuristics' performance against random selection, state-of-the-art selection schemes, and 18 classifiers, including auto-machine-learning techniques, on 30 classification problems with a variable number of samples, variables, and classes. The result indicated that the combination of parent selection based on agreement and random selection to replace an individual in the population produces statistically better results than the classical selection and state-of-the-art schemes, and it is competitive with state-of-the-art classifiers. Finally, the code is released as open-source software.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 2","pages":"253-289"},"PeriodicalIF":6.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39555351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Modular Grammatical Evolution for the Generation of Artificial Neural Networks 用于生成人工神经网络的模块语法进化

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-06-01 DOI: 10.1162/evco_a_00302

Khabat Soltanian;Ali Ebnenasir;Mohsen Afsharchi

{"title":"Modular Grammatical Evolution for the Generation of Artificial Neural Networks","authors":"Khabat Soltanian;Ali Ebnenasir;Mohsen Afsharchi","doi":"10.1162/evco_a_00302","DOIUrl":"10.1162/evco_a_00302","url":null,"abstract":"This article presents a novel method, called Modular Grammatical Evolution (MGE), toward validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation of smaller and more structured neural networks while providing acceptable (and in some cases superior) accuracy on large data sets. MGE also enhances the state-of-the-art Grammatical Evolution (GE) methods in two directions. First, MGE's representation is modular in that each individual has a set of genes, and each gene is mapped to a neuron by grammatical rules. Second, the proposed representation mitigates two important drawbacks of GE, namely the low scalability and weak locality of representation, toward generating modular and multilayer networks with a high number of neurons. We define and evaluate five different forms of structures with and without modularity using MGE and find single-layer modules with no coupling more productive. Our experiments demonstrate that modularity helps in finding better neural networks faster. We have validated the proposed method using ten well-known classification benchmarks with different sizes, feature counts, and output class counts. Our experimental results indicate that MGE provides superior accuracy with respect to existing NeuroEvolution methods and returns classifiers that are significantly simpler than other machine learning generated classifiers. Finally, we empirically demonstrate that MGE outperforms other GE methods in terms of locality and scalability properties.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 2","pages":"291-327"},"PeriodicalIF":6.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39701853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times 基于迁移学习的协同代理进化双目标优化方法

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-06-01 DOI: 10.1162/evco_a_00300

Xilu Wang;Yaochu Jin;Sebastian Schmitt;Markus Olhofer

引用次数: 11

Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multiobjective Evolutionary Algorithm 基于组合多目标进化算法的多踏脚石进化多模式机器人行为

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-06-01 DOI: 10.1162/evco_a_00301

Joost Huizinga;Jeff Clune

{"title":"Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multiobjective Evolutionary Algorithm","authors":"Joost Huizinga;Jeff Clune","doi":"10.1162/evco_a_00301","DOIUrl":"10.1162/evco_a_00301","url":null,"abstract":"Abstract An important challenge in reinforcement learning is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly, it is often helpful to define a curriculum, which is an ordered set of subtasks that can serve as the stepping stones for solving the overall problem. Unfortunately, choosing an effective ordering for these subtasks is difficult, and a poor ordering can reduce the performance of the learning process. Here, we provide a thorough introduction and investigation of the Combinatorial Multiobjective Evolutionary Algorithm (CMOEA), which allows all combinations of subtasks to be explored simultaneously. We compare CMOEA against three algorithms that can similarly optimize on multiple subtasks simultaneously: NSGA-II, NSGA-III, and ε-Lexicase Selection. The algorithms are tested on a function-optimization problem with two subtasks, a simulated multimodal robot locomotion problem with six subtasks, and a simulated robot maze-navigation problem where a hundred random mazes are treated as subtasks. On these problems, CMOEA either outperforms or is competitive with the controls. As a separate contribution, we show that adding a linear combination over all objectives can improve the ability of the control algorithms to solve these multimodal problems. Lastly, we show that CMOEA can leverage auxiliary objectives more effectively than the controls on the multimodal locomotion task. In general, our experiments suggest that CMOEA is a promising algorithm for solving multimodal problems.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 2","pages":"131-164"},"PeriodicalIF":6.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39744129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

VSD-MOEA: A Dominance-Based Multiobjective Evolutionary Algorithm with Explicit Variable Space Diversity Management VSD-MOEA：一种具有显式可变空间分集管理的基于优势的多目标进化算法

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-06-01 DOI: 10.1162/evco_a_00299

Joel Chacón Castillo;Carlos Segura;Carlos A. Coello Coello

{"title":"VSD-MOEA: A Dominance-Based Multiobjective Evolutionary Algorithm with Explicit Variable Space Diversity Management","authors":"Joel Chacón Castillo;Carlos Segura;Carlos A. Coello Coello","doi":"10.1162/evco_a_00299","DOIUrl":"10.1162/evco_a_00299","url":null,"abstract":"Most state-of-the-art Multiobjective Evolutionary Algorithms (moeas) promote the preservation of diversity of objective function space but neglect the diversity of decision variable space. The aim of this article is to show that explicitly managing the amount of diversity maintained in the decision variable space is useful to increase the quality of moeas when taking into account metrics of the objective space. Our novel Variable Space Diversity-based MOEA (vsd-moea) explicitly considers the diversity of both decision variable and objective function space. This information is used with the aim of properly adapting the balance between exploration and intensification during the optimization process. Particularly, at the initial stages, decisions made by the approach are more biased by the information on the diversity of the variable space, whereas it gradually grants more importance to the diversity of objective function space as the evolution progresses. The latter is achieved through a novel density estimator. The new method is compared with state-of-art moeas using several benchmarks with two and three objectives. This novel proposal yields much better results than state-of-the-art schemes when considering metrics applied on objective function space, exhibiting a more stable and robust behavior.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 2","pages":"195-219"},"PeriodicalIF":6.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39698125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1