Evolutionary Computation最新文献_第9页

Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions. 基于模型的小表达式符号回归遗传规划改进。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-06-01 DOI: 10.1162/evco_a_00278

M Virgolin, T Alderliesten, C Witteveen, P A N Bosman

{"title":"Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions.","authors":"M Virgolin, T Alderliesten, C Witteveen, P A N Bosman","doi":"10.1162/evco_a_00278","DOIUrl":"https://doi.org/10.1162/evco_a_00278","url":null,"abstract":"The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts blindly, GOMEA learns a model of interdependencies within the genotype, that is, the linkage, to estimate what patterns to propagate. In this article, we study the role of Linkage Learning (LL) performed by GOMEA in Symbolic Regression (SR). We show that the non-uniformity in the distribution of the genotype in GP populations negatively biases LL, and propose a method to correct for this. We also propose approaches to improve LL when ephemeral random constants are used. Furthermore, we adapt a scheme of interleaving runs to alleviate the burden of tuning the population size, a crucial parameter for LL, to SR. We run experiments on 10 real-world datasets, enforcing a strict limitation on solution size, to enable interpretability. We find that the new LL method outperforms the standard one, and that GOMEA outperforms both traditional and semantic GP. We also find that the small solutions evolved by GOMEA are competitive with tuned decision trees, making GOMEA a promising new approach to SR.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 2","pages":"211-237"},"PeriodicalIF":6.8,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/evco_a_00278","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38075060","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: 49

Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming. 基于语法的遗传规划中的概率上下文和结构依赖学习。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-06-01 DOI: 10.1162/evco_a_00280

Pak-Kan Wong, Man-Leung Wong, Kwong-Sak Leung

{"title":"Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming.","authors":"Pak-Kan Wong, Man-Leung Wong, Kwong-Sak Leung","doi":"10.1162/evco_a_00280","DOIUrl":"https://doi.org/10.1162/evco_a_00280","url":null,"abstract":"Genetic Programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by complex dependencies among components of programs is challenging. It is important because it can misguide Genetic Programming to create suboptimal programs. Besides, a minor modification in the programs may lead to a notable change in the program behaviours and affect the final outputs. This article presents Grammar-Based Genetic Programming with Bayesian Classifiers (GBGPBC) in which the probabilistic dependencies among components of programs are captured using a set of Bayesian network classifiers. Our system was evaluated using a set of benchmark problems (the deceptive maximum problems, the royal tree problems, and the bipolar asymmetric royal tree problems). It was shown to be often more robust and more efficient in searching the best programs than other related Genetic Programming approaches in terms of the total number of fitness evaluation. We studied what factors affect the performance of GBGPBC and discovered that robust variants of GBGPBC were consistently weakly correlated with some complexity measures. Furthermore, our approach has been applied to learn a ranking program on a set of customers in direct marketing. Our suggested solutions help companies to earn significantly more when compared with other solutions produced by several well-known machine learning algorithms, such as neural networks, logistic regression, and Bayesian networks.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 2","pages":"239-268"},"PeriodicalIF":6.8,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38483671","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

A Decomposition-Based Evolutionary Algorithm with Correlative Selection Mechanism for Many-Objective Optimization. 一种基于分解的关联选择机制的多目标优化进化算法。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-06-01 DOI: 10.1162/evco_a_00279

Ruochen Liu, Ruinan Wang, Renyu Bian, Jing Liu, Licheng Jiao

{"title":"A Decomposition-Based Evolutionary Algorithm with Correlative Selection Mechanism for Many-Objective Optimization.","authors":"Ruochen Liu, Ruinan Wang, Renyu Bian, Jing Liu, Licheng Jiao","doi":"10.1162/evco_a_00279","DOIUrl":"https://doi.org/10.1162/evco_a_00279","url":null,"abstract":"Decomposition-based evolutionary algorithms have been quite successful in dealing with multiobjective optimization problems. Recently, more and more researchers attempt to apply the decomposition approach to solve many-objective optimization problems. A many-objective evolutionary algorithm based on decomposition with correlative selection mechanism (MOEA/D-CSM) is also proposed to solve many-objective optimization problems in this article. Since MOEA/D-SCM is based on a decomposition approach which adopts penalty boundary intersection (PBI), a set of reference points must be generated in advance. Thus, a new concept related to the set of reference points is introduced first, namely, the correlation between an individual and a reference point. Thereafter, a new selection mechanism based on the correlation is designed and called correlative selection mechanism. The correlative selection mechanism finds its correlative individuals for each reference point as soon as possible so that the diversity among population members is maintained. However, when a reference point has two or more correlative individuals, the worse correlative individuals may be removed from a population so that the solutions can be ensured to move toward the Pareto-optimal front. In a comprehensive experimental study, we apply MOEA/D-CSM to a number of many-objective test problems with 3 to 15 objectives and make a comparison with three state-of-the-art many-objective evolutionary algorithms, namely, NSGA-III, MOEA/D, and RVEA. Experimental results show that the proposed MOEA/D-CSM can produce competitive results on most of the problems considered in this study.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 2","pages":"269-304"},"PeriodicalIF":6.8,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38483670","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: 7

A Systematic Literature Review of the Successors of “NeuroEvolution of Augmenting Topologies” “扩充拓扑的神经进化”后续研究的系统文献综述

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-03-02 DOI: 10.1162/evco_a_00282

Evgenia Papavasileiou;Jan Cornelis;Bart Jansen

{"title":"A Systematic Literature Review of the Successors of “NeuroEvolution of Augmenting Topologies”","authors":"Evgenia Papavasileiou;Jan Cornelis;Bart Jansen","doi":"10.1162/evco_a_00282","DOIUrl":"10.1162/evco_a_00282","url":null,"abstract":"<para>NeuroEvolution (NE) refers to a family of methods for optimizing Artificial Neural Networks (ANNs) using Evolutionary Computation (EC) algorithms. NeuroEvolution of Augmenting Topologies (NEAT) is considered one of the most influential algorithms in the field. Eighteen years after its invention, a plethora of methods have been proposed that extend NEAT in different aspects. In this article, we present a systematic literature review (SLR) to list and categorize the methods succeeding NEAT. Our review protocol identified 232 papers by merging the findings of two major electronic databases. Applying criteria that determine the paper's relevance and assess its quality, resulted in 61 methods that are presented in this article. Our review article proposes a new categorization scheme of NEAT's successors into three clusters. NEAT-based methods are categorized based on 1) whether they consider issues specific to the search space or the fitness landscape, 2) whether they combine principles from NE and another domain, or 3) the particular properties of the evolved ANNs. The clustering supports researchers 1) understanding the current state of the art that will enable them, 2) exploring new research directions or 3) benchmarking their proposed method to the state of the art, if they are interested in comparing, and 4) positioning themselves in the domain or 5) selecting a method that is most appropriate for their problem.</para>","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 1","pages":"1-73"},"PeriodicalIF":6.8,"publicationDate":"2021-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38569425","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: 28

Effect of Objective Normalization and Penalty Parameter on Penalty Boundary Intersection Decomposition-Based Evolutionary Many-Objective Optimization Algorithms 目标归一化和惩罚参数对基于惩罚边界交集分解的进化多目标优化算法的影响

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-03-02 DOI: 10.1162/evco_a_00276

Lei Chen;Kalyanmoy Deb;Hai-Lin Liu;Qingfu Zhang

{"title":"Effect of Objective Normalization and Penalty Parameter on Penalty Boundary Intersection Decomposition-Based Evolutionary Many-Objective Optimization Algorithms","authors":"Lei Chen;Kalyanmoy Deb;Hai-Lin Liu;Qingfu Zhang","doi":"10.1162/evco_a_00276","DOIUrl":"10.1162/evco_a_00276","url":null,"abstract":"<para>An objective normalization strategy is essential in any evolutionary multiobjective or many-objective optimization (EMO or EMaO) algorithm, due to the distance calculations between objective vectors required to compute diversity and convergence of population members. For the decomposition-based EMO/EMaO algorithms involving the Penalty Boundary Intersection (PBI) metric, normalization is an important matter due to the computation of two distance metrics. In this article, we make a theoretical analysis of the effect of instabilities in the normalization process on the performance of PBI-based MOEA/D and a proposed PBI-based NSGA-III procedure. Although the effect is well recognized in the literature, few theoretical studies have been done so far to understand its true nature and the choice of a suitable penalty parameter value for an arbitrary problem. The developed theoretical results have been corroborated with extensive experimental results on three to 15-objective convex and non-convex instances of DTLZ and WFG problems. The article, makes important theoretical conclusions on PBI-based decomposition algorithms derived from the study.</para>","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 1","pages":"157-186"},"PeriodicalIF":6.8,"publicationDate":"2021-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/evco_a_00276","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38069842","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: 12

Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations 利用部分评价实现高度可扩展的进化实值优化

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-03-02 DOI: 10.1162/evco_a_00275

Anton Bouter;Tanja Alderliesten;Peter A.N. Bosman

{"title":"Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations","authors":"Anton Bouter;Tanja Alderliesten;Peter A.N. Bosman","doi":"10.1162/evco_a_00275","DOIUrl":"10.1162/evco_a_00275","url":null,"abstract":"<para>It is known that to achieve efficient scalability of an Evolutionary Algorithm (EA), dependencies (also known as linkage) must be properly taken into account during variation. In a Gray-Box Optimization (GBO) setting, exploiting prior knowledge regarding these dependencies can greatly benefit optimization. We specifically consider the setting where partial evaluations are possible, meaning that the partial modification of a solution can be efficiently evaluated. Such problems are potentially very difficult, for example, non-separable, multimodal, and multiobjective. The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) can effectively exploit partial evaluations, leading to a substantial improvement in performance and scalability. GOMEA was recently shown to be extendable to real-valued optimization through a combination with the real-valued estimation of distribution algorithm AMaLGaM. In this article, we definitively introduce the Real-Valued GOMEA (RV-GOMEA), and introduce a new variant, constructed by combining GOMEA with what is arguably the best-known real-valued EA, the Covariance Matrix Adaptation Evolution Strategies (CMA-ES). Both variants of GOMEA are compared to L-BFGS and the Limited Memory CMA-ES (LM-CMA-ES). We show that both variants of RV-GOMEA achieve excellent performance and scalability in a GBO setting, which can be orders of magnitude better than that of EAs unable to efficiently exploit the GBO setting.</para>","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 1","pages":"129-155"},"PeriodicalIF":6.8,"publicationDate":"2021-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/evco_a_00275","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38055587","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: 10

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling 具有延迟路由的遗传规划在多目标动态柔性车间调度中的应用

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-03-02 DOI: 10.1162/evco_a_00273

Binzi Xu;Yi Mei;Yan Wang;Zhicheng Ji;Mengjie Zhang

{"title":"Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling","authors":"Binzi Xu;Yi Mei;Yan Wang;Zhicheng Ji;Mengjie Zhang","doi":"10.1162/evco_a_00273","DOIUrl":"10.1162/evco_a_00273","url":null,"abstract":"<para>Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e., the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this article, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and most accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multiobjective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.</para>","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 1","pages":"75-105"},"PeriodicalIF":6.8,"publicationDate":"2021-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/evco_a_00273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37907438","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: 21

Feature-Based Diversity Optimization for Problem Instance Classification 基于特征的问题实例分类多样性优化

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-03-02 DOI: 10.1162/evco_a_00274

Wanru Gao;Samadhi Nallaperuma;Frank Neumann

引用次数: 47

High-Order Entropy-Based Population Diversity Measures in the Traveling Salesman Problem 旅行商问题中基于高阶熵的群体多样性测度

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2020-12-02 DOI: 10.1162/evco_a_00268

Yuichi Nagata

{"title":"High-Order Entropy-Based Population Diversity Measures in the Traveling Salesman Problem","authors":"Yuichi Nagata","doi":"10.1162/evco_a_00268","DOIUrl":"10.1162/evco_a_00268","url":null,"abstract":"<para>To maintain the population diversity of genetic algorithms (GAs), we are required to employ an appropriate population diversity measure. However, commonly used population diversity measures designed for permutation problems do not consider the dependencies between the variables of the individuals in the population. We propose three types of population diversity measures that address high-order dependencies between the variables to investigate the effectiveness of considering high-order dependencies. The first is formulated as the entropy of the probability distribution of individuals estimated from the population based on an <inline-formula><mml:math><mml:mi>m</mml:mi></mml:math></inline-formula>-th--order Markov model. The second is an extension of the first. The third is similar to the first, but it is based on a variable order Markov model. The proposed population diversity measures are incorporated into the evaluation function of a GA for the traveling salesman problem to maintain population diversity. Experimental results demonstrate the effectiveness of the three types of high-order entropy-based population diversity measures against the commonly used population diversity measures.</para>","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"28 4","pages":"595-619"},"PeriodicalIF":6.8,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/evco_a_00268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37639325","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

Inferring Future Landscapes: Sampling the Local Optima Level 推断未来景观：采样局部最优水平

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2020-12-02 DOI: 10.1162/evco_a_00271

Sarah L. Thomson;Gabriela Ochoa;Sébastien Verel;Nadarajen Veerapen

{"title":"Inferring Future Landscapes: Sampling the Local Optima Level","authors":"Sarah L. Thomson;Gabriela Ochoa;Sébastien Verel;Nadarajen Veerapen","doi":"10.1162/evco_a_00271","DOIUrl":"10.1162/evco_a_00271","url":null,"abstract":"<para>Connection patterns among <italic>Local Optima Networks (LONs) can inform heuristic design for optimisation. LON research has predominantly required complete enumeration of a fitness landscape, thereby restricting analysis to problems diminutive in size compared to real-life situations. LON <italic>sampling algorithms are therefore important. In this article, we study LON construction algorithms for the Quadratic Assignment Problem (QAP). Using machine learning, we use estimated LON features to predict search performance for competitive heuristics used in the QAP domain. The results show that by using random forest regression, LON construction algorithms produce fitness landscape features which can explain almost all search variance. We find that LON samples better relate to search than enumerated LONs do. The importance of fitness levels of sampled LONs in search predictions is crystallised. Features from LONs produced by different algorithms are combined in predictions for the first time, with promising results for this “super-sampling”: a model to predict tabu search success explained 99% of variance. Arguments are made for the use-case of each LON algorithm and for combining the exploitative process of one with the exploratory optimisation of the other.</para>","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"28 4","pages":"621-641"},"PeriodicalIF":6.8,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/evco_a_00271","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37680158","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: 11