ACM transactions on evolutionary learning最新文献

Evolutionary Seeding of Diverse Structural Design Solutions via Topology Optimization 通过拓扑优化实现多样化结构设计方案的进化播种

ACM transactions on evolutionary learning Pub Date : 2024-06-05 DOI: 10.1145/3670693

Yue Xie, Josh Pinskier, Xing Wang, David Howard

{"title":"Evolutionary Seeding of Diverse Structural Design Solutions via Topology Optimization","authors":"Yue Xie, Josh Pinskier, Xing Wang, David Howard","doi":"10.1145/3670693","DOIUrl":"https://doi.org/10.1145/3670693","url":null,"abstract":"Topology optimization is a powerful design tool in structural engineering and other engineering problems. The design domain is discretized into elements, and a finite element method model is iteratively solved to find the element that maximizes the structure's performance. Although gradient-based solvers have been used to solve topology optimization problems, they may be susceptible to suboptimal solutions or difficulty obtaining feasible solutions, particularly in non-convex optimization problems. The presence of non-convexities can hinder convergence, leading to challenges in achieving the global optimum. With this in mind, we discuss in this paper the application of the quality diversity approach to topological optimization problems. Quality diversity (QD) algorithms have shown promise in the research field of optimization and have many applications in engineering design, robotics, and games. MAP-Elites is a popular QD algorithm used in robotics. In soft robotics, the MAP-Elites algorithm has been used to optimize the shape and control of soft robots, leading to the discovery of new and efficient motion strategies. This paper introduces an approach based on MAP-Elites to provide diverse designs for structural optimization problems. Three fundamental topology optimization problems are used for experimental testing, and the results demonstrate the ability of the proposed algorithm to generate diverse, high-performance designs for those problems. Furthermore, the proposed algorithm can be a valuable engineering design tool capable of creating novel and efficient designs.","PeriodicalId":497392,"journal":{"name":"ACM transactions on evolutionary learning","volume":"52 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141384171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Influence of Noise on Multi-Parent Crossover for an Island Model Genetic Algorithm 岛屿模型遗传算法中噪声对多亲本交叉的影响

ACM transactions on evolutionary learning Pub Date : 2023-11-09 DOI: 10.1145/3630638

Brahim Aboutaib, Andrew M. Sutton

引用次数: 0

Model-based Gradient Search for Permutation Problems 基于模型的梯度搜索置换问题

ACM transactions on evolutionary learning Pub Date : 2023-10-20 DOI: 10.1145/3628605

Josu Ceberio, Valentino Santucci

引用次数: 0

Exploring the Explainable Aspects and Performance of a Learnable Evolutionary Multiobjective Optimization Method 探索一种可学习的进化多目标优化方法的可解释方面和性能

ACM transactions on evolutionary learning Pub Date : 2023-09-28 DOI: 10.1145/3626104

Giovanni Misitano

{"title":"Exploring the Explainable Aspects and Performance of a Learnable Evolutionary Multiobjective Optimization Method","authors":"Giovanni Misitano","doi":"10.1145/3626104","DOIUrl":"https://doi.org/10.1145/3626104","url":null,"abstract":"Multiobjective optimization problems have multiple conflicting objective functions to be optimized simultaneously. The solutions to these problems are known as Pareto optimal solutions, which are mathematically incomparable. Thus, a decision maker must be employed to provide preferences to find the most preferred solution. However, decision makers often lack support in providing preferences and insights in exploring the solutions available. We explore the combination of learnable evolutionary models with interactive indicator-based evolutionary multiobjective optimization to create a learnable evolutionary multiobjective optimization method. Furthermore, we leverage interpretable machine learning to provide decision makers with potential insights about the problem being solved in the form of rule-based explanations. In fact, we show that a learnable evolutionary multiobjective optimization method can offer advantages in the search for solutions to a multiobjective optimization problem. We also provide an open source software framework for other researchers to implement and explore our ideas in their own works. Our work is a step towards establishing a new paradigm in the field on multiobjective optimization: explainable and learnable multiobjective optimization . We take the first steps towards this new research direction and provide other researchers and practitioners with necessary tools and ideas to further contribute to this field.","PeriodicalId":497392,"journal":{"name":"ACM transactions on evolutionary learning","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135385435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial to the “Evolutionary Reinforcement Learning” Special Issue “进化强化学习”特刊社论

ACM transactions on evolutionary learning Pub Date : 2023-09-26 DOI: 10.1145/3624559

Adam Gaier, Giuseppe Paolo, Antoine Cully

引用次数: 0