Evolutionary Computation最新文献_第4页

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

Neural Architecture Search Using Covariance Matrix Adaptation Evolution Strategy 使用协方差矩阵适应进化策略的神经架构搜索

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-06-03 DOI: 10.1162/evco_a_00331

Nilotpal Sinha;Kuan-Wen Chen

{"title":"Neural Architecture Search Using Covariance Matrix Adaptation Evolution Strategy","authors":"Nilotpal Sinha;Kuan-Wen Chen","doi":"10.1162/evco_a_00331","DOIUrl":"10.1162/evco_a_00331","url":null,"abstract":"Evolution-based neural architecture search methods have shown promising results, but they require high computational resources because these methods involve training each candidate architecture from scratch and then evaluating its fitness, which results in long search time. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) has shown promising results in tuning hyperparameters of neural networks but has not been used for neural architecture search. In this work, we propose a framework called CMANAS which applies the faster convergence property of CMA-ES to the deep neural architecture search problem. Instead of training each individual architecture seperately, we used the accuracy of a trained one shot model (OSM) on the validation data as a prediction of the fitness of the architecture, resulting in reduced search time. We also used an architecture-fitness table (AF table) for keeping a record of the already evaluated architecture, thus further reducing the search time. The architectures are modeled using a normal distribution, which is updated using CMA-ES based on the fitness of the sampled population. Experimentally, CMANAS achieves better results than previous evolution-based methods while reducing the search time significantly. The effectiveness of CMANAS is shown on two different search spaces using four datasets: CIFAR-10, CIFAR-100, ImageNet, and ImageNet16-120. All the results show that CMANAS is a viable alternative to previous evolution-based methods and extends the application of CMA-ES to the deep neural architecture search field.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"32 2","pages":"177-204"},"PeriodicalIF":4.6,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9424655","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

On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem 基于子图的单目标突变用于解决双目标最小生成树问题

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-06-03 DOI: 10.1162/evco_a_00335

Jakob Bossek;Christian Grimme

{"title":"On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem","authors":"Jakob Bossek;Christian Grimme","doi":"10.1162/evco_a_00335","DOIUrl":"10.1162/evco_a_00335","url":null,"abstract":"We contribute to the efficient approximation of the Pareto-set for the classical NP-hard multiobjective minimum spanning tree problem (moMST) adopting evolutionary computation. More precisely, by building upon preliminary work, we analyze the neighborhood structure of Pareto-optimal spanning trees and design several highly biased sub-graph-based mutation operators founded on the gained insights. In a nutshell, these operators replace (un)connected sub-trees of candidate solutions with locally optimal sub-trees. The latter (biased) step is realized by applying Kruskal's single-objective MST algorithm to a weighted sum scalarization of a sub-graph. We prove runtime complexity results for the introduced operators and investigate the desirable Pareto-beneficial property. This property states that mutants cannot be dominated by their parent. Moreover, we perform an extensive experimental benchmark study to showcase the operator's practical suitability. Our results confirm that the sub-graph-based operators beat baseline algorithms from the literature even with severely restricted computational budget in terms of function evaluations on four different classes of complete graphs with different shapes of the Pareto-front.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"32 2","pages":"143-175"},"PeriodicalIF":4.6,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9967379","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

The Role of Morphological Variation in Evolutionary Robotics: Maximizing Performance and Robustness 形态变异在进化机器人学中的作用：最大化性能和鲁棒性

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-06-03 DOI: 10.1162/evco_a_00336

Jonata Tyska Carvalho;Stefano Nolfi

{"title":"The Role of Morphological Variation in Evolutionary Robotics: Maximizing Performance and Robustness","authors":"Jonata Tyska Carvalho;Stefano Nolfi","doi":"10.1162/evco_a_00336","DOIUrl":"10.1162/evco_a_00336","url":null,"abstract":"Exposing an evolutionary algorithm that is used to evolve robot controllers to variable conditions is necessary to obtain solutions which are robust and can cross the reality gap. However, we do not yet have methods for analyzing and understanding the impact of the varying morphological conditions which impact the evolutionary process, and therefore for choosing suitable variation ranges. By morphological conditions, we refer to the starting state of the robot, and to variations in its sensor readings during operation due to noise. In this paper, we introduce a method that permits us to measure the impact of these morphological variations and we analyze the relation between the amplitude of variations, the modality with which they are introduced, and the performance and robustness of evolving agents. Our results demonstrate that (i) the evolutionary algorithm can tolerate morphological variations which have a very high impact, (ii) variations affecting the actions of the agent are tolerated much better than variations affecting the initial state of the agent or of the environment, and (iii) improving the accuracy of the fitness measure through multiple evaluations is not always useful. Moreover, our results show that morphological variations permit generating solutions which perform better both in varying and non-varying conditions.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"32 2","pages":"125-142"},"PeriodicalIF":4.6,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9726876","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

Comparing Robot Controller Optimization Methods on Evolvable Morphologies 比较可进化形态上的机器人控制器优化方法

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-06-03 DOI: 10.1162/evco_a_00334

Fuda van Diggelen;Eliseo Ferrante;A. E. Eiben

{"title":"Comparing Robot Controller Optimization Methods on Evolvable Morphologies","authors":"Fuda van Diggelen;Eliseo Ferrante;A. E. Eiben","doi":"10.1162/evco_a_00334","DOIUrl":"10.1162/evco_a_00334","url":null,"abstract":"In this paper, we compare Bayesian Optimization, Differential Evolution, and an Evolution Strategy employed as a gait-learning algorithm in modular robots. The motivational scenario is the joint evolution of morphologies and controllers, where “newborn” robots also undergo a learning process to optimize their inherited controllers (without changing their bodies). This context raises the question: How do gait-learning algorithms compare when applied to various morphologies that are not known in advance (and thus need to be treated as without priors)? To answer this question, we use a test suite of twenty different robot morphologies to evaluate our gait-learners and compare their efficiency, efficacy, and sensitivity to morphological differences. The results indicate that Bayesian Optimization and Differential Evolution deliver the same solution quality (walking speed for the robot) with fewer evaluations than the Evolution Strategy. Furthermore, the Evolution Strategy is more sensitive for morphological differences (its efficacy varies more between different morphologies) and is more subject to luck (repeated runs on the same morphology show greater variance in the outcomes).","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"32 2","pages":"105-124"},"PeriodicalIF":4.6,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9541798","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

Editorial for the Special Issue on Reproducibility 可重复性特刊编辑。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-03-01 DOI: 10.1162/evco_e_00344

Manuel López-Ibáñez;Luís Paquete;Mike Preuss

引用次数: 0