Evolutionary Computation最新文献_第2页

Preliminary Analysis of Simple Novelty Search. 简单新奇搜索的初步分析。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-09-03 DOI: 10.1162/evco_a_00340

R Paul Wiegand

{"title":"Preliminary Analysis of Simple Novelty Search.","authors":"R Paul Wiegand","doi":"10.1162/evco_a_00340","DOIUrl":"10.1162/evco_a_00340","url":null,"abstract":"Novelty search is a powerful tool for finding diverse sets of objects in complicated spaces. Recent experiments on simplified versions of novelty search introduce the idea that novelty search happens at the level of the archive space, rather than individual points. The sparseness measure and archive update criterion create a process that is driven by a two measures: (1) spread out to cover the space while trying to remain as efficiently packed as possible, and (2) metrics inspired by k nearest neighbor theory. In this paper, we generalize previous simplifications of novelty search to include traditional population (μ,λ) dynamics for generating new search points, where the population and the archive are updated separately. We provide some theoretical guidance regarding balancing mutation and sparseness criteria and introduce the concept of saturation as a way of talking about fully covered spaces. We show empirically that claims that novelty search is inherently objectiveless are incorrect. We leverage the understanding of novelty search as an optimizer of archive coverage, suggest several ways to improve the search, and demonstrate one simple improvement-generating some new points directly from the archive rather than the parent population.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"249-273"},"PeriodicalIF":4.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9828886","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 Tri-Objective Method for Bi-Objective Feature Selection in Classification. 分类中双目标特征选择的三目标方法

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-09-03 DOI: 10.1162/evco_a_00339

Ruwang Jiao, Bing Xue, Mengjie Zhang

{"title":"A Tri-Objective Method for Bi-Objective Feature Selection in Classification.","authors":"Ruwang Jiao, Bing Xue, Mengjie Zhang","doi":"10.1162/evco_a_00339","DOIUrl":"10.1162/evco_a_00339","url":null,"abstract":"Minimizing the number of selected features and maximizing the classification performance are two main objectives in feature selection, which can be formulated as a bi-objective optimization problem. Due to the complex interactions between features, a solution (i.e., feature subset) with poor objective values does not mean that all the features it selects are useless, as some of them combined with other complementary features can greatly improve the classification performance. Thus, it is necessary to consider not only the performance of feature subsets in the objective space, but also their differences in the search space, to explore more promising feature combinations. To this end, this paper proposes a tri-objective method for bi-objective feature selection in classification, which solves a bi-objective feature selection problem as a tri-objective problem by considering the diversity (differences) between feature subsets in the search space as the third objective. The selection based on the converted tri-objective method can maintain a balance between minimizing the number of selected features, maximizing the classification performance, and exploring more promising feature subsets. Furthermore, a novel initialization strategy and an offspring reproduction operator are proposed to promote the diversity of feature subsets in the objective space and improve the search ability, respectively. The proposed algorithm is compared with five multiobjective-based feature selection methods, six typical feature selection methods, and two peer methods with diversity as a helper objective. Experimental results on 20 real-world classification datasets suggest that the proposed method outperforms the compared methods in most scenarios.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"217-248"},"PeriodicalIF":4.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9822009","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

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics. IOHexperimenter：迭代优化启发法基准测试平台。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-09-03 DOI: 10.1162/evco_a_00342

Jacob de Nobel, Furong Ye, Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck

引用次数: 0

Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. Pflacco：用 Python 对连续和受限优化问题进行基于特征的景观分析

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-09-03 DOI: 10.1162/evco_a_00341

Raphael Patrick Prager, Heike Trautmann

引用次数: 0

Evolutionary Sparsity Regularisation-based Feature Selection for Binary Classification. 基于进化稀疏正则化的二元分类特征选择

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-08-22 DOI: 10.1162/evco_a_00358

Bach Hoai Nguyen, Bing Xue, Mengjie Zhang

{"title":"Evolutionary Sparsity Regularisation-based Feature Selection for Binary Classification.","authors":"Bach Hoai Nguyen, Bing Xue, Mengjie Zhang","doi":"10.1162/evco_a_00358","DOIUrl":"https://doi.org/10.1162/evco_a_00358","url":null,"abstract":"In classification, feature selection is an essential pre-processing step that selects a small subset of features to improve classification performance. Existing feature selection approaches can be divided into three main approaches: wrapper approaches, filter approaches, and embedded approaches. In comparison with two other approaches, embedded approaches usually have better trade-off between classification performance and computation time. One of the most well-known embedded approaches is sparsity regularisation-based feature selection which generates sparse solutions for feature selection. Despite its good performance, sparsity regularisation-based feature selection outputs only a feature ranking which requires the number of selected features to be predefined. More importantly, the ranking mechanism introduces a risk of ignoring feature interactions which leads to the fact that many top-ranked but redundant features are selected. This work addresses the above problems by proposing a new representation that considers the interactions between features and can automatically determine an appropriate number of selected features. The proposed representation is used in a differential evolutionary (DE) algorithm to optimise the feature subset. In addition, a novel initialisation mechanism is proposed to let DE consider various numbers of selected features at the beginning. The proposed algorithm is examined on both synthetic and real-world datasets. The results on the synthetic dataset show that the proposed algorithm can select complementary features while existing sparsity regularisation-based feature selection algorithms are at risk of selecting redundant features. The results on real-world datasets show that the proposed algorithm achieves better classification performance than well-known wrapper, filter, and embedded approaches. The algorithm is also as efficient as filter feature selection approaches.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-33"},"PeriodicalIF":4.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019413","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

Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context. 进化黑箱背景下的代用模型景观分析

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-08-14 DOI: 10.1162/evco_a_00357

Zbyněk Pitra, Jan Koza, Jiří Tumpach, Martin Holeňa

{"title":"Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context.","authors":"Zbyněk Pitra, Jan Koza, Jiří Tumpach, Martin Holeňa","doi":"10.1162/evco_a_00357","DOIUrl":"https://doi.org/10.1162/evco_a_00357","url":null,"abstract":"Surrogate modeling has become a valuable technique for black-box optimization tasks with expensive evaluation of the objective function. In this paper, we investigate the relationships between the predictive accuracy of surrogate models, their settings, and features of the black-box function landscape during evolutionary optimization by the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) state-of-the-art optimizer for expensive continuous black-box tasks. This study aims to establish the foundation for specific rules and automated methods for selecting and tuning surrogate models by exploring relationships between landscape features and model errors, focusing on the behavior of a specific model within each generation in contrast to selecting a specific algorithm at the outset. We perform a feature analysis process, identifying a significant number of non-robust features and clustering similar landscape features, resulting in the selection of 14 features out of 384, varying with input data selection methods. Our analysis explores the error dependencies of four models across 39 settings, utilizing three methods for input data selection, drawn from surrogate-assisted CMA-ES runs on noiseless benchmarks within the Comparing Continuous Optimizers framework.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-29"},"PeriodicalIF":4.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141983781","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

Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables. 针对具有正态分布随机变量的机会约束优化问题的单目标和多目标进化算法的运行时间分析

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-08-02 DOI: 10.1162/evco_a_00355

Frank Neumann, Carsten Witt

{"title":"Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables.","authors":"Frank Neumann, Carsten Witt","doi":"10.1162/evco_a_00355","DOIUrl":"https://doi.org/10.1162/evco_a_00355","url":null,"abstract":"Chance constrained optimization problems allow to model problems where constraints involving stochastic components should only be violated with a small probability. Evolutionary algorithms have been applied to this scenario and shown to achieve high quality results. With this paper, we contribute to the theoretical understanding of evolutionary algorithms for chance constrained optimization. We study the scenario of stochastic components that are independent and normally distributed. Considering the simple single-objective (1+1) EA, we show that imposing an additional uniform constraint already leads to local optima for very restricted scenarios and an exponential optimization time. We therefore introduce a multi-objective formulation of the problem which trades off the expected cost and its variance. We show that multi-objective evolutionary algorithms are highly effective when using this formulation and obtain a set of solutions that contains an optimal solution for any possible confidence level imposed on the constraint. Furthermore, we prove that this approach can also be used to compute a set of optimal solutions for the chance constrained minimum spanning tree problem. In order to deal with potentially exponentially many trade-offs in the multi-objective formulation, we propose and analyze improved convex multi-objective approaches. Experimental investigations on instances of the NP-hard stochastic minimum weight dominating set problem confirm the benefit of the multi-objective and the improved convex multi-objective approach in practice.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-22"},"PeriodicalIF":4.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890746","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

Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks. 使用机器学习方法评估模块化优化框架中的模块性能贡献。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-08-02 DOI: 10.1162/evco_a_00356

Ana Kostovska, Diederick Vermetten, Peter Korošec, Sašo Džeroski, Carola Doerr, Tome Eftimov

{"title":"Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks.","authors":"Ana Kostovska, Diederick Vermetten, Peter Korošec, Sašo Džeroski, Carola Doerr, Tome Eftimov","doi":"10.1162/evco_a_00356","DOIUrl":"https://doi.org/10.1162/evco_a_00356","url":null,"abstract":"Modular algorithm frameworks not only allow for combinations never tested in manually selected algorithm portfolios, but they also provide a structured approach to assess which algorithmic ideas are crucial for the observed performance of algorithms. In this study, we propose a methodology for analyzing the impact of the different modules on the overall performance. We consider modular frameworks for two widely used families of derivative-free black-box optimization algorithms, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and differential evolution (DE). More specifically, we use performance data of 324 modCMA-ES and 576 modDE algorithm variants (with each variant corresponding to a specific configuration of modules) obtained on the 24 BBOB problems for 6 different runtime budgets in 2 dimensions. Our analysis of these data reveals that the impact of individual modules on overall algorithm performance varies significantly. Notably, among the examined modules, the elitism module in CMA-ES and the linear population size reduction module in DE exhibit the most significant impact on performance. Furthermore, our exploratory data analysis of problem landscape data suggests that the most relevant landscape features remain consistent regardless of the configuration of individual modules, but the influence that these features have on regression accuracy varies. In addition, we apply classifiers that exploit feature importance with respect to the trained models for performance prediction and performance data, to predict the modular configurations of CMA-ES and DE algorithm variants. The results show that the predicted configurations do not exhibit a statistically significant difference in performance compared to the true configurations, with the percentage varying depending on the setup (from 49.1% to 95.5% for mod-CMA and 21.7% to 77.1% for DE).","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-27"},"PeriodicalIF":4.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890747","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

Hyperparameter Control Using Fuzzy Logic: Evolving Policies for Adaptive Fuzzy Particle Swarm Optimization Algorithm. 使用模糊逻辑的超参数控制：自适应模糊粒子群优化算法的演化策略。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-06-18 DOI: 10.1162/evco_a_00353

Nicolas Roy, Charlotte Beauthier, Alexandre Mayer

{"title":"Hyperparameter Control Using Fuzzy Logic: Evolving Policies for Adaptive Fuzzy Particle Swarm Optimization Algorithm.","authors":"Nicolas Roy, Charlotte Beauthier, Alexandre Mayer","doi":"10.1162/evco_a_00353","DOIUrl":"https://doi.org/10.1162/evco_a_00353","url":null,"abstract":"Heuristic optimization methods such as Particle Swarm Optimization depend on their parameters to achieve optimal performance on a given class of problems. Some modifications of heuristic algorithms aim at adapting those parameters during the optimization process. We present a novel approach to design such adaptation strategies using continuous fuzzy feedback control. Fuzzy feedback provides a simple interface where probes are sampled in the optimization process and parameters are fed back to the optimizer. The probes are turned into parameters by a fuzzy process optimized beforehand to maximize performance on a training benchmark. Utilizing this framework, we systematically established 127 different Fuzzy Particle Swarm Optimization algorithms featuring a maximum of 7 parameters under fuzzy control. These newly devised algorithms exhibit superior performance compared to both traditional PSO and some of its best parameter control variants. The performance is reported in the single-objective bound-constrained numerical optimization competition of CEC 2020. Additionally, two specific controls, highlighted for their efficacy and dependability, demonstrated commendable performance in real-world scenarios from CEC 2011.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-30"},"PeriodicalIF":6.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421719","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

Large-Scale Multiobjective Evolutionary Algorithm Guided by Low-Dimensional Surrogates of Scalarization Functions. 以低维标度化函数替代物为指导的大规模多目标进化算法

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-06-18 DOI: 10.1162/evco_a_00354

Haoran Gu, Handing Wang, Cheng He, Bo Yuan, Yaochu Jin

{"title":"Large-Scale Multiobjective Evolutionary Algorithm Guided by Low-Dimensional Surrogates of Scalarization Functions.","authors":"Haoran Gu, Handing Wang, Cheng He, Bo Yuan, Yaochu Jin","doi":"10.1162/evco_a_00354","DOIUrl":"https://doi.org/10.1162/evco_a_00354","url":null,"abstract":"Recently, computationally intensive multiobjective optimization problems have been efficiently solved by surrogate-assisted multiobjective evolutionary algorithms. However, most of those algorithms can only handle no more than 200 decision variables. As the number of decision variables increases further, unreliable surrogate models will result in a dramatic deterioration of their performance, which makes large-scale expensive multiobjective optimization challenging. To address this challenge, we develop a large-scale multiobjective evolutionary algorithm guided by low-dimensional surrogate models of scalarization functions. The proposed algorithm (termed LDS-AF) reduces the dimension of the original decision space based on principal component analysis, and then directly approximates the scalarization functions in a decompositionbased multiobjective evolutionary algorithm. With the help of a two-stage modeling strategy and convergence control strategy, LDS-AF can keep a good balance between convergence and diversity, and achieve a promising performance without being trapped in a local optimum prematurely. The experimental results on a set of test instances have demonstrated its superiority over eight state-of-the-art algorithms on multiobjective optimization problems with up to 1000 decision variables using only 500 real function evaluations.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-25"},"PeriodicalIF":6.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421720","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