ACM Transactions on Evolutionary Learning and Optimization最新文献_第2页

Applicability of Neural Combinatorial Optimization: A Critical View 神经组合优化的适用性：批判性视角

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2024-02-09 DOI: 10.1145/3647644

Andoni I. Garmendia, Josu Ceberio, A. Mendiburu

{"title":"Applicability of Neural Combinatorial Optimization: A Critical View","authors":"Andoni I. Garmendia, Josu Ceberio, A. Mendiburu","doi":"10.1145/3647644","DOIUrl":"https://doi.org/10.1145/3647644","url":null,"abstract":"Neural Combinatorial Optimization has emerged as a new paradigm in the optimization area. It attempts to solve optimization problems by means of neural networks and reinforcement learning. In the last few years, due to their novelty and presumably good performance, many research papers have been published introducing new neural architectures for a variety of combinatorial problems. However, the incorporation of such models in the conventional optimization portfolio raises many questions related to their performance compared to other existing methods, such as exact algorithms, heuristics or metaheuristics. This paper aims to present a critical view of these new proposals, discussing their benefits and drawbacks with respect to the tools and algorithms already present in the optimization field. For this purpose, a comprehensive study is carried out to analyse the fundamental aspects of such methods, including performance, computational cost, transferability and reusability of the trained model. Moreover, this discussion is accompanied by the design and validation of a new neural combinatorial optimization algorithm on two well-known combinatorial problems, the Linear Ordering Problem and the Permutation Flowshop Scheduling Problem. Finally, new directions for future work in the area of Neural Combinatorial Optimization algorithms are suggested.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":" 118","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139788871","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

Applicability of Neural Combinatorial Optimization: A Critical View 神经组合优化的适用性：批判性视角

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2024-02-09 DOI: 10.1145/3647644

Andoni I. Garmendia, Josu Ceberio, A. Mendiburu

{"title":"Applicability of Neural Combinatorial Optimization: A Critical View","authors":"Andoni I. Garmendia, Josu Ceberio, A. Mendiburu","doi":"10.1145/3647644","DOIUrl":"https://doi.org/10.1145/3647644","url":null,"abstract":"Neural Combinatorial Optimization has emerged as a new paradigm in the optimization area. It attempts to solve optimization problems by means of neural networks and reinforcement learning. In the last few years, due to their novelty and presumably good performance, many research papers have been published introducing new neural architectures for a variety of combinatorial problems. However, the incorporation of such models in the conventional optimization portfolio raises many questions related to their performance compared to other existing methods, such as exact algorithms, heuristics or metaheuristics. This paper aims to present a critical view of these new proposals, discussing their benefits and drawbacks with respect to the tools and algorithms already present in the optimization field. For this purpose, a comprehensive study is carried out to analyse the fundamental aspects of such methods, including performance, computational cost, transferability and reusability of the trained model. Moreover, this discussion is accompanied by the design and validation of a new neural combinatorial optimization algorithm on two well-known combinatorial problems, the Linear Ordering Problem and the Permutation Flowshop Scheduling Problem. Finally, new directions for future work in the area of Neural Combinatorial Optimization algorithms are suggested.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"197 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848758","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

Code and Data Synthesis for Genetic Improvement in Emergent Software Systems 紧急软件系统遗传改进的代码和数据综合

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2022-08-17 DOI: 10.1145/3542823

P. F. Rainford, Barry Porter

{"title":"Code and Data Synthesis for Genetic Improvement in Emergent Software Systems","authors":"P. F. Rainford, Barry Porter","doi":"10.1145/3542823","DOIUrl":"https://doi.org/10.1145/3542823","url":null,"abstract":"Emergent software systems are assembled from a collection of small code blocks, where some of those blocks have alternative implementation variants; they optimise at run-time by learning which compositions of alternative blocks best suit each deployment environment encountered. In this paper we study the automated synthesis of new implementation variants for a running system using genetic improvement (GI). Typical GI approaches, however, rely on large amounts of data for accurate training and large code bases from which to source genetic material. In emergent systems we have neither asset, with sparsely sampled runtime data and small code volumes in each building block. We therefore examine two approaches to more effective GI under these constraints: the synthesis of data from sparse samples to construct statistically representative larger training corpora; and the synthesis of code to counter the relative lack of genetic material in our starting population members. Our results demonstrate that a mixture of synthesised and existing code is a viable optimisation strategy, and that phases of increased synthesis can make GI more robust to deleterious mutations. On synthesised data, we find that we can produce equivalent optimisation compared to GI methods using larger data sets, and that this optimisation can produce both useful specialists and generalists.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128271476","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

Deep Genetic Programming Trees Are Robust 深度遗传规划树是鲁棒的

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2022-06-01 DOI: 10.1145/3539738

W. Langdon

引用次数: 0

A Survey on High-dimensional Gaussian Process Modeling with Application to Bayesian Optimization 高维高斯过程建模及其在贝叶斯优化中的应用综述

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-11-09 DOI: 10.1145/3545611

M. Binois, Nathan Wycoff

引用次数: 36

Efficient Computation of Probabilistic Dominance in Multi-objective Optimization 多目标优化中概率优势的高效计算

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-10-13 DOI: 10.1145/3469801

Faramarz Khosravi, Alexander Raß, Jürgen Teich

{"title":"Efficient Computation of Probabilistic Dominance in Multi-objective Optimization","authors":"Faramarz Khosravi, Alexander Raß, Jürgen Teich","doi":"10.1145/3469801","DOIUrl":"https://doi.org/10.1145/3469801","url":null,"abstract":"Real-world problems typically require the simultaneous optimization of multiple, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions. To cope with such uncertainties, stochastic and robust optimization techniques are widely studied aiming to distinguish candidate solutions with uncertain objectives specified by confidence intervals, probability distributions, sampled data, or uncertainty sets. In this scope, this article first introduces a novel empirical approach for the comparison of candidate solutions with uncertain objectives that can follow arbitrary distributions. The comparison is performed through accurate and efficient calculations of the probability that one solution dominates the other in terms of each uncertain objective. Second, such an operator can be flexibly used and combined with many existing multi-objective optimization frameworks and techniques by just substituting their standard comparison operator, thus easily enabling the Pareto front optimization of problems with multiple uncertain objectives. Third, a new benchmark for evaluating uncertainty-aware optimization techniques is introduced by incorporating different types of uncertainties into a well-known benchmark for multi-objective optimization problems. Fourth, the new comparison operator and benchmark suite are integrated into an existing multi-objective optimization framework that features a selection of multi-objective optimization problems and algorithms. Fifth, the efficiency in terms of performance and execution time of the proposed comparison operator is evaluated on the introduced uncertainty benchmark. Finally, statistical tests are applied giving evidence of the superiority of the new comparison operator in terms of epsilon-dominance and attainment surfaces in comparison to previously proposed approaches.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131148487","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

Emergent Tangled Program Graphs in Partially Observable Recursive Forecasting and ViZDoom Navigation Tasks 部分可观察递归预测和ViZDoom导航任务中的紧急纠缠程序图

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-08-18 DOI: 10.1145/3468857

Stephen Kelly, Robert J. Smith, M. Heywood, W. Banzhaf

{"title":"Emergent Tangled Program Graphs in Partially Observable Recursive Forecasting and ViZDoom Navigation Tasks","authors":"Stephen Kelly, Robert J. Smith, M. Heywood, W. Banzhaf","doi":"10.1145/3468857","DOIUrl":"https://doi.org/10.1145/3468857","url":null,"abstract":"Modularity represents a recurring theme in the attempt to scale evolution to the design of complex systems. However, modularity rarely forms the central theme of an artificial approach to evolution. In this work, we report on progress with the recently proposed Tangled Program Graph (TPG) framework in which programs are modules. The combination of the TPG representation and its variation operators enable both teams of programs and graphs of teams of programs to appear in an emergent process. The original development of TPG was limited to tasks with, for the most part, complete information. This work details two recent approaches for scaling TPG to tasks that are dominated by partially observable sources of information using different formulations of indexed memory. One formulation emphasizes the incremental construction of memory, again as an emergent process, resulting in a distributed view of state. The second formulation assumes a single global instance of memory and develops it as a communication medium, thus a single global view of state. The resulting empirical evaluation demonstrates that TPG equipped with memory is able to solve multi-task recursive time-series forecasting problems and visual navigation tasks expressed in two levels of a commercial first-person shooter environment.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114052114","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: 6

A Comparison of Learning Classifier Systems’ Rule Compaction Algorithms for Knowledge Visualization 面向知识可视化的学习分类器系统规则压缩算法比较

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-08-18 DOI: 10.1145/3468166

Yi Liu, Will N. Browne, Bing Xue

{"title":"A Comparison of Learning Classifier Systems’ Rule Compaction Algorithms for Knowledge Visualization","authors":"Yi Liu, Will N. Browne, Bing Xue","doi":"10.1145/3468166","DOIUrl":"https://doi.org/10.1145/3468166","url":null,"abstract":"Learning Classifier Systems (LCSs) are a paradigm of rule-based evolutionary computation (EC). LCSs excel in data-mining tasks regarding helping humans to understand the explored problem, often through visualizing the discovered patterns linking features to classes. Due to the stochastic nature of EC, LCSs unavoidably produce and keep redundant rules, which obscure the patterns. Thus, rule compaction methods are invoked to produce a better population by removing problematic rules. Previously, compaction methods have neither been tested on large-scale problems nor been assessed on the performance of capturing patterns. We review and test the most popular compaction algorithms, finding that across multiple LCSs’ populations for the same task, although the redundant rules can be different, the accurate rules are common. Furthermore, the patterns contained consistently refer to the nature of the explored domain, e.g., the data distribution or the importance of features for determining actions. This extends the [O] set hypothesis proposed by Butz et al. [1], in which an LCS is expected to evolve a minimal number of non-overlapped rules to represent an addressed domain. Two new compaction algorithms are introduced to search at the rule level and the population level by compacting multiple LCSs’ populations. Two visualization methods are employed for verifying the interpretability of these populations. Successful compaction is demonstrated on complex and real problems with clean datasets, e.g., the 11-bits Majority-On problem that requires 924 different interacting rules in the optimal solution to be uniquely identified to enable correct visualization. For the first time, the patterns contained in learned models for the large-scale 70-bits Multiplexer problem are visualized successfully.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123813772","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: 11

Constraint-Objective Cooperative Coevolution for Large-scale Constrained Optimization 大规模约束优化的约束-目标协同进化

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-08-18 DOI: 10.1145/3469036

Peilan Xu, Wenjian Luo, Xin Lin, Jia-jia Zhang, Yingying Qiao, Xuan Wang

{"title":"Constraint-Objective Cooperative Coevolution for Large-scale Constrained Optimization","authors":"Peilan Xu, Wenjian Luo, Xin Lin, Jia-jia Zhang, Yingying Qiao, Xuan Wang","doi":"10.1145/3469036","DOIUrl":"https://doi.org/10.1145/3469036","url":null,"abstract":"Large-scale optimization problems and constrained optimization problems have attracted considerable attention in the swarm and evolutionary intelligence communities and exemplify two common features of real problems, i.e., a large scale and constraint limitations. However, only a little work on solving large-scale continuous constrained optimization problems exists. Moreover, the types of benchmarks proposed for large-scale continuous constrained optimization algorithms are not comprehensive at present. In this article, first, a constraint-objective cooperative coevolution (COCC) framework is proposed for large-scale continuous constrained optimization problems, which is based on the dual nature of the objective and constraint functions: modular and imbalanced components. The COCC framework allocates the computing resources to different components according to the impact of objective values and constraint violations. Second, a benchmark for large-scale continuous constrained optimization is presented, which takes into account the modular nature, as well as both imbalanced and overlapping characteristics of components. Finally, three different evolutionary algorithms are embedded into the COCC framework for experiments, and the experimental results show that COCC performs competitively.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115756356","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: 9

Is Our Archiving Reliable? Multiobjective Archiving Methods on “Simple” Artificial Input Sequences 我们的存档可靠吗?“简单”人工输入序列的多目标归档方法

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-08-18 DOI: 10.1145/3465335

Miqing Li

{"title":"Is Our Archiving Reliable? Multiobjective Archiving Methods on “Simple” Artificial Input Sequences","authors":"Miqing Li","doi":"10.1145/3465335","DOIUrl":"https://doi.org/10.1145/3465335","url":null,"abstract":"In evolutionary multiobjective optimisation (EMO), archiving is a common component that maintains an (external or internal) set during the search process, typically with a fixed size, in order to provide a good representation of high-quality solutions produced. Such an archive set can be used solely to store the final results shown to the decision maker, but in many cases may participate in the process of producing solutions (e.g., as a solution pool where the parental solutions are selected). Over the last three decades, archiving stands as an important issue in EMO, leading to the emergence of various methods such as those based on Pareto, indicator, or decomposition criteria. Such methods have demonstrated their effectiveness in literature and have been believed to be good options to many problems, particularly those having a regular Pareto front shape, e.g., a simplex shape. In this article, we challenge this belief. We do this through artificially constructing several sequences with extremely simple shapes, i.e., 1D/2D simplex Pareto front. We show the struggle of predominantly used archiving methods which have been deemed to well handle such shapes. This reveals that the order of solutions entering the archive matters, and that current EMO algorithms may not be fully capable of maintaining a representative population on problems with linear Pareto fronts even in the case that all of their optimal solutions can be found.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132895696","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: 4