ACM Transactions on Evolutionary Learning and Optimization最新文献

On the Generalisation Performance of Geometric Semantic Genetic Programming for Boolean Functions: Learning Block Mutations 论布尔函数几何语义遗传编程的泛化性能：学习块突变

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2024-07-16 DOI: 10.1145/3677124

Dogan Corus, P. S. Oliveto

{"title":"On the Generalisation Performance of Geometric Semantic Genetic Programming for Boolean Functions: Learning Block Mutations","authors":"Dogan Corus, P. S. Oliveto","doi":"10.1145/3677124","DOIUrl":"https://doi.org/10.1145/3677124","url":null,"abstract":"In this paper we present the first rigorous theoretical analysis of the generalisation performance of a Geometric Semantic Genetic Programming (GSGP) system. More specifically, we consider a hill-climber using the GSGP Fixed Block Mutation (FBM) operator for the domain of Boolean functions. We prove that the algorithm cannot evolve Boolean conjunctions of arbitrary size that are correct on unseen inputs chosen uniformly at random from the complete truth table i.e., it generalises poorly. Two algorithms based on the Varying Block Mutation (VBM) operator are proposed and analysed to address the issue. We rigorously prove that under the uniform distribution the first one can efficiently evolve any Boolean function of constant size with respect to the number of available variables, while the second one can efficiently evolve general conjunctions or disjunctions of any size without requiring prior knowledge of the target function class. An experimental analysis confirms the theoretical insights for realistic problem sizes and indicates the superiority of the proposed operators also for small parity functions not explicitly covered by the theory.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"1 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141642454","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

Introduction to the “Best of GECCO 2022” Special Issue: Part II 2022 年 GECCO 精选》特刊简介：第二部分

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2024-05-23 DOI: 10.1145/3665797

Jonathan Fieldsend, Markus Wagner

引用次数: 0

A Combinatorial Optimization Framework for Probability-Based Algorithms by Means of Generative Models 通过生成模型实现基于概率算法的组合优化框架

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2024-05-22 DOI: 10.1145/3665650

Mikel Malagón, Ekhine Irurozki, Josu Ceberio

{"title":"A Combinatorial Optimization Framework for Probability-Based Algorithms by Means of Generative Models","authors":"Mikel Malagón, Ekhine Irurozki, Josu Ceberio","doi":"10.1145/3665650","DOIUrl":"https://doi.org/10.1145/3665650","url":null,"abstract":"Probability-based algorithms have proven to be a solid alternative for approaching optimization problems. Nevertheless, in many cases, using probabilistic models that efficiently exploit the characteristics of the problem involves large computational overheads, and therefore, lower complexity models such as those that are univariate are usually employed within approximation algorithms.\u0000 With the motivation to address such an issue, in this paper, we aim to introduce an iterative optimization framework that employs generative models to efficiently estimate the parameters of probability models for optimization problems. This allows the use of complex probabilistic models (or those that are appropriate for each problem) in a way that is feasible to apply them iteratively. Specifically, the framework is composed of three elements: a generative model, a probability model whose probability rule is differentiable, and a loss function. The possibility of modifying any of the three elements of the framework offers the flexibility to design algorithms that best adapt to the problem at hand.\u0000 \u0000 Experiments conducted on two case studies reveal that the presented approach has strong performance in terms of objective value and execution time when compared to other probability-based algorithms. Moreover, the experimental analysis demonstrates that the convergence of the algorithms is controllable by adjusting the components of the framework. For the sake of reproducibility, the source code, results, scripts, figures, and other material related to the manuscript are available at\u0000 https://github.com/mikelma/nnco_lib\u0000 .\u0000","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"43 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141109844","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

Covariance Matrix Adaptation MAP-Annealing: Theory and Experiments 协方差矩阵自适应 MAP 退火法：理论与实验

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2024-05-17 DOI: 10.1145/3665336

Shihan Zhao, Bryon Tjanaka, Matthew C. Fontaine, S. Nikolaidis

{"title":"Covariance Matrix Adaptation MAP-Annealing: Theory and Experiments","authors":"Shihan Zhao, Bryon Tjanaka, Matthew C. Fontaine, S. Nikolaidis","doi":"10.1145/3665336","DOIUrl":"https://doi.org/10.1145/3665336","url":null,"abstract":"Single-objective optimization algorithms search for the single highest-quality solution with respect to an objective. Quality diversity (QD) optimization algorithms, such as Covariance Matrix Adaptation MAP-Elites (CMA-ME), search for a collection of solutions that are both high-quality with respect to an objective and diverse with respect to specified measure functions. However, CMA-ME suffers from three major limitations highlighted by the QD community: prematurely abandoning the objective in favor of exploration, struggling to explore flat objectives, and having poor performance for low-resolution archives. We propose a new quality diversity algorithm, Covariance Matrix Adaptation MAP-Annealing (CMA-MAE), and its differentiable quality diversity variant, Covariance Matrix Adaptation MAP-Annealing via a Gradient Arborescence (CMA-MAEGA), that address all three limitations. We provide theoretical justifications for the new algorithm with respect to each limitation. Our theory informs our experiments, which support the theory and show that CMA-MAE achieves state-of-the-art performance and robustness on standard QD benchmark and reinforcement learning domains.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"58 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140965469","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

Body and Brain Quality-Diversity in Robot Swarms 机器人群中的身体和大脑质量多样性

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2024-05-10 DOI: 10.1145/3664656

S. Mkhatshwa, Geoff Nitschke

{"title":"Body and Brain Quality-Diversity in Robot Swarms","authors":"S. Mkhatshwa, Geoff Nitschke","doi":"10.1145/3664656","DOIUrl":"https://doi.org/10.1145/3664656","url":null,"abstract":"In biological societies, complex interactions between the behavior and morphology of evolving organisms and their environment have given rise to a wide range of complex and diverse social structures. Similarly, in artificial counterparts such as swarm-robotics systems, collective behaviors emerge via the interconnected dynamics of robot morphology (sensory-motor configuration), behavior (controller), and environment (task). Various studies have demonstrated morphological and behavioral diversity enables biological groups to exhibit adaptive, robust, and resilient collective behavior across changing environments. However, in artificial (swarm robotic) systems there is little research on the impact of changing environments on morphological and behavioral (body-brain) diversity in emergent collective behavior, and the benefits of such diversity. This study uses evolutionary collective robotics as an experimental platform to investigate the impact of increasing task environment complexity (collective behavior task difficulty) on the evolution and benefits of morphological and behavioral diversity in robotic swarms. Results indicate that body-brain evolution using coupled behavior and morphology diversity maintenance yields higher behavioral and morphological diversity, which is beneficial for collective behavior task performance across task environments. Results also indicate that such behavioral and morphological diversity maintenance coupled with body-brain evolution produces neuro-morpho complexity that does not increase concomitantly with task complexity.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":" 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140993398","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

A Population Initialization Method Based on Similarity and Mutual Information in Evolutionary Algorithm for Bi-objective Feature Selection 双目标特征选择进化算法中基于相似性和互信息的种群初始化方法

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2024-03-19 DOI: 10.1145/3653025

Xu Cai, Yu Xue

{"title":"A Population Initialization Method Based on Similarity and Mutual Information in Evolutionary Algorithm for Bi-objective Feature Selection","authors":"Xu Cai, Yu Xue","doi":"10.1145/3653025","DOIUrl":"https://doi.org/10.1145/3653025","url":null,"abstract":"Feature selection (FS) is an important data pre-processing technique in classification. It aims to remove redundant and irrelevant features from the data, which reduces the dimensionality of data and improves the performance of the classifier. Thus, FS is a bi-objective optimization problem, and evolutionary algorithms (EAs) have been proven to be effective in solving bi-objective FS problems. EA is a population-based metaheuristic algorithm, and the quality of the initial population is an important factor affecting the performance of EA. An improper initial population may negatively affect the convergence speed of the EA and even make the algorithm fall into the local optimum. In this paper, we propose a similarity and mutual information-based initialization method, named SMII, to improve the quality of the initial population. This method determines the distribution of initial solutions based on similarity, and shields features with high correlation to the selected features according to mutual information. In the experiment, we embed SMII, the latest four initialization methods, and a traditional random initialization method into NSGA-II, and compared their performance on 15 public datasets. The experimental results show that SMII performs best on most datasets, and can effectively improve the performance of the algorithm. Moreover, we compare the performance of two other EA before and after embedding SMII on 15 datasets, the results further prove that the proposed method can effectively improve the search capability of the EA for FS.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"35 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229210","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

Introduction to the Special Issue on Explainable AI in Evolutionary Computation 进化计算中的可解释人工智能》特刊简介

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2024-03-13 DOI: 10.1145/3649144

J. Bacardit, Alexander E. I. Brownlee, Stefano Cagnoni, Giovanni Iacca, John A. W. McCall, David Walker

引用次数: 0

Iterated Local Search with Linkage Learning 迭代本地搜索与关联学习

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2024-03-02 DOI: 10.1145/3651165

R. Tinós, M. Przewozniczek, Darrell Whitley, Francisco Chicano

{"title":"Iterated Local Search with Linkage Learning","authors":"R. Tinós, M. Przewozniczek, Darrell Whitley, Francisco Chicano","doi":"10.1145/3651165","DOIUrl":"https://doi.org/10.1145/3651165","url":null,"abstract":"In pseudo-Boolean optimization, a variable interaction graph represents variables as vertices, and interactions between pairs of variables as edges. In black-box optimization, the variable interaction graph may be at least partially discovered by using empirical linkage learning techniques. These methods never report false variable interactions, but they are computationally expensive. The recently proposed local search with linkage learning discovers the partial variable interaction graph as a side-effect of iterated local search. However, information about the strength of the interactions is not learned by the algorithm. We propose local search with linkage learning 2, which builds a weighted variable interaction graph that stores information about the strength of the interaction between variables. The weighted variable interaction graph can provide new insights about the optimization problem and behavior of optimizers. Experiments with NK landscapes, knapsack problem, and feature selection show that local search with linkage learning 2 is able to efficiently build weighted variable interaction graphs. In particular, experiments with feature selection show that the weighted variable interaction graphs can be used for visualizing the feature interactions in machine learning. Additionally, new transformation operators that exploit the interactions between variables can be designed. We illustrate this ability by proposing a new perturbation operator for iterated local search.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"27 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140081442","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: 1

Generating Cheap Representative Functions for Expensive Automotive Crashworthiness Optimization 为昂贵的汽车耐撞性优化生成廉价代表函数

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2024-02-13 DOI: 10.1145/3646554

Fu Xing Long, Bas van Stein, M. Frenzel, P. Krause, M. Gitterle, Thomas Bäck

{"title":"Generating Cheap Representative Functions for Expensive Automotive Crashworthiness Optimization","authors":"Fu Xing Long, Bas van Stein, M. Frenzel, P. Krause, M. Gitterle, Thomas Bäck","doi":"10.1145/3646554","DOIUrl":"https://doi.org/10.1145/3646554","url":null,"abstract":"Solving real-world engineering optimization problems, such as automotive crashworthiness optimization, is extremely challenging, because the problem characteristics are oftentimes not well understood. Furthermore, typical hyperparameter optimization approaches that require a large function evaluation budget are computationally hindered, if the function evaluation is expensive, e.g., requires finite element (FE) simulation runs. In this paper, we propose an approach to characterize real-world expensive black-box optimization problems using the exploratory landscape analysis (ELA). Based on these landscape characteristics, we can identify test functions that are fast-to-evaluate and representative for hyperparameter optimization purposes. Focusing on 20 problem instances from automotive crashworthiness optimization, our results reveal that these 20 crashworthiness problems exhibit landscape features different from classical optimization benchmark test suites, such as the widely-used black-box optimization benchmarking (BBOB) problem set. In fact, these 20 problem instances belong to problem classes that are distinct from the BBOB test functions based on the clustering results. Further analysis indicates that, as far as the ELA features concern, they are most similar to problem classes of tree-based test functions. By analyzing the performance of two optimization algorithms with different hyperparameters, namely the covariance matrix adaptation evolutionary strategy (CMA-ES) and Bayesian optimization (BO), we show that the tree-based test functions are indeed representative in terms of predicting the algorithm performances. Following this, such scalable and fast-to-evaluate tree-based test functions have promising potential for automated design of an optimization algorithm for specific real-world problem classes.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"663 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139841157","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

Generating Cheap Representative Functions for Expensive Automotive Crashworthiness Optimization 为昂贵的汽车耐撞性优化生成廉价代表函数

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2024-02-13 DOI: 10.1145/3646554

Fu Xing Long, Bas van Stein, M. Frenzel, P. Krause, M. Gitterle, Thomas Bäck

{"title":"Generating Cheap Representative Functions for Expensive Automotive Crashworthiness Optimization","authors":"Fu Xing Long, Bas van Stein, M. Frenzel, P. Krause, M. Gitterle, Thomas Bäck","doi":"10.1145/3646554","DOIUrl":"https://doi.org/10.1145/3646554","url":null,"abstract":"Solving real-world engineering optimization problems, such as automotive crashworthiness optimization, is extremely challenging, because the problem characteristics are oftentimes not well understood. Furthermore, typical hyperparameter optimization approaches that require a large function evaluation budget are computationally hindered, if the function evaluation is expensive, e.g., requires finite element (FE) simulation runs. In this paper, we propose an approach to characterize real-world expensive black-box optimization problems using the exploratory landscape analysis (ELA). Based on these landscape characteristics, we can identify test functions that are fast-to-evaluate and representative for hyperparameter optimization purposes. Focusing on 20 problem instances from automotive crashworthiness optimization, our results reveal that these 20 crashworthiness problems exhibit landscape features different from classical optimization benchmark test suites, such as the widely-used black-box optimization benchmarking (BBOB) problem set. In fact, these 20 problem instances belong to problem classes that are distinct from the BBOB test functions based on the clustering results. Further analysis indicates that, as far as the ELA features concern, they are most similar to problem classes of tree-based test functions. By analyzing the performance of two optimization algorithms with different hyperparameters, namely the covariance matrix adaptation evolutionary strategy (CMA-ES) and Bayesian optimization (BO), we show that the tree-based test functions are indeed representative in terms of predicting the algorithm performances. Following this, such scalable and fast-to-evaluate tree-based test functions have promising potential for automated design of an optimization algorithm for specific real-world problem classes.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"78 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139781452","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