ACM Transactions on Evolutionary Learning and Optimization最新文献_第3页

A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies 结合Walsh系数和依赖关系高效学习的代理辅助遗传算法设计新方法

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-07-23 DOI: 10.1145/3453141

A. Dushatskiy, T. Alderliesten, P. Bosman

{"title":"A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies","authors":"A. Dushatskiy, T. Alderliesten, P. Bosman","doi":"10.1145/3453141","DOIUrl":"https://doi.org/10.1145/3453141","url":null,"abstract":"Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the domain of pseudo-Boolean functions in a black-box setting. Moreover, instead of using a surrogate model as an approximation of a fitness function, we propose to precisely learn the coefficients of the Walsh decomposition of a fitness function and use the Walsh decomposition as a surrogate. If the coefficients are learned correctly, then the Walsh decomposition values perfectly match with the fitness function, and, thus, the optimal solution to the problem can be found by optimizing the surrogate without any additional evaluations of the original fitness function. It is known that the Walsh coefficients can be efficiently learned for pseudo-Boolean functions with k-bounded epistasis and known problem structure. We propose to learn dependencies between variables first and, therefore, substantially reduce the number of Walsh coefficients to be calculated. After the accurate Walsh decomposition is obtained, the surrogate model is optimized using GOMEA, which is considered to be a state-of-the-art binary optimization algorithm. We compare the proposed approach with standard GOMEA and two other Walsh decomposition-based algorithms. The benchmark functions in the experiments are well-known trap functions, NK-landscapes, MaxCut, and MAX3SAT problems. The experimental results demonstrate that the proposed approach is scalable at the supposed complexity of O(ℓlogℓ) function evaluations when the number of subfunctions is O(ℓ) and all subfunctions are k-bounded, outperforming all considered algorithms.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131597035","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: 5

Genetic Improvement of Data for Maths Functions 数学函数数据的遗传改进

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-07-23 DOI: 10.1145/3461016

W. Langdon, Oliver Krauss

引用次数: 6

Spatial Coevolution for Generative Adversarial Network Training 生成对抗网络训练的空间协同进化

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-07-23 DOI: 10.1145/3458845

Erik Hemberg, J. Toutouh, Abdullah Al-Dujaili, Tom Schmiedlechner, Una-May O’Reilly

{"title":"Spatial Coevolution for Generative Adversarial Network Training","authors":"Erik Hemberg, J. Toutouh, Abdullah Al-Dujaili, Tom Schmiedlechner, Una-May O’Reilly","doi":"10.1145/3458845","DOIUrl":"https://doi.org/10.1145/3458845","url":null,"abstract":"Generative Adversarial Networks (GANs) are difficult to train because of pathologies such as mode and discriminator collapse. Similar pathologies have been studied and addressed in competitive evolutionary computation by increased diversity. We study a system, Lipizzaner, that combines spatial coevolution with gradient-based learning to improve the robustness and scalability of GAN training. We study different features of Lipizzaner’s evolutionary computation methodology. Our ablation experiments determine that communication, selection, parameter optimization, and ensemble optimization each, as well as in combination, play critical roles. Lipizzaner succumbs less frequently to critical collapses and, as a side benefit, demonstrates improved performance. In addition, we show a GAN-training feature of Lipizzaner: the ability to train simultaneously with different loss functions in the gradient descent parameter learning framework of each GAN at each cell. We use an image generation problem to show that different loss function combinations result in models with better accuracy and more diversity in comparison to other existing evolutionary GAN models. Finally, Lipizzaner with multiple loss function options promotes the best model diversity while requiring a large grid size for adequate accuracy.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115925689","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: 10

Analysis of Evolved Response Thresholds for Decentralized Dynamic Task Allocation 分散动态任务分配的演化响应阈值分析

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-06-10 DOI: 10.1145/3530821

David Mathias, A. Wu, Daniel Dang

{"title":"Analysis of Evolved Response Thresholds for Decentralized Dynamic Task Allocation","authors":"David Mathias, A. Wu, Daniel Dang","doi":"10.1145/3530821","DOIUrl":"https://doi.org/10.1145/3530821","url":null,"abstract":"In this work, we investigate the application of a multi-objective genetic algorithm to the problem of task allocation in a self-organizing, decentralized, threshold-based swarm. We use a multi-objective genetic algorithm to evolve response thresholds for a simulated swarm engaged in dynamic task allocation problems: two-dimensional and three-dimensional collective tracking. We show that evolved thresholds not only outperform uniformly distributed thresholds and dynamic thresholds but achieve nearly optimal performance on a variety of tracking problem instances (target paths). More importantly, we demonstrate that thresholds evolved for some problem instances generalize to all other problem instances, eliminating the need to evolve new thresholds for each problem instance to be solved. We analyze the properties that allow these paths to serve as universal training instances and show that they are quite natural. After a priori evolution, the response thresholds in our system are static. The problem instances solved by the swarms are highly dynamic, with schedules of task demands that change over time with significant differences in rate and magnitude of change. That the swarm is able to achieve nearly optimal results refutes the common assumption that a swarm must be dynamic to perform well in a dynamic environment.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126372778","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

Evolution of Activation Functions: An Empirical Investigation 激活函数的演化:一个实证研究

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-05-30 DOI: 10.1145/3464384

Andrew Nader, D. Azar

{"title":"Evolution of Activation Functions: An Empirical Investigation","authors":"Andrew Nader, D. Azar","doi":"10.1145/3464384","DOIUrl":"https://doi.org/10.1145/3464384","url":null,"abstract":"The hyper-parameters of a neural network are traditionally designed through a time-consuming process of trial and error that requires substantial expert knowledge. Neural Architecture Search algorithms aim to take the human out of the loop by automatically finding a good set of hyper-parameters for the problem at hand. These algorithms have mostly focused on hyper-parameters such as the architectural configurations of the hidden layers and the connectivity of the hidden neurons, but there has been relatively little work on automating the search for completely new activation functions, which are one of the most crucial hyperparameters to choose. There are some widely used activation functions nowadays that are simple and work well, but nonetheless, there has been some interest in finding better activation functions. The work in the literature has mostly focused on designing new activation functions by hand or choosing from a set of predefined functions while this work presents an evolutionary algorithm to automate the search for completely new activation functions. We compare these new evolved activation functions to other existing and commonly used activation functions. The results are favorable and are obtained from averaging the performance of the activation functions found over 30 runs, with experiments being conducted on 10 different datasets and architectures to ensure the statistical robustness of the study.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123130422","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

Saddle Point Optimization with Approximate Minimization Oracle and Its Application to Robust Berthing Control 鞍点近似最小化优化及其在鲁棒靠泊控制中的应用

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-05-25 DOI: 10.1145/3510425

Youhei Akimoto, Yoshiki Miyauchi, A. Maki

引用次数: 10

Feature Construction for Meta-heuristic Algorithm Recommendation of Capacitated Vehicle Routing Problems 有能力车辆路径问题元启发式算法推荐的特征构建

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-04-26 DOI: 10.1145/3447540

Hao Jiang, Yuhang Wang, Ye Tian, Xing-yi Zhang, Jian-hua Xiao

{"title":"Feature Construction for Meta-heuristic Algorithm Recommendation of Capacitated Vehicle Routing Problems","authors":"Hao Jiang, Yuhang Wang, Ye Tian, Xing-yi Zhang, Jian-hua Xiao","doi":"10.1145/3447540","DOIUrl":"https://doi.org/10.1145/3447540","url":null,"abstract":"The algorithm recommendation is attracting increasing attention in solving real-world capacitated vehicle routing problems (CVRPs), due to the fact that existing meta-heuristic algorithms often show different performances on different CVRPs. To effectively perform algorithm recommendation for CVRPs, it becomes vital to extract suitable features to characterize the CVRPs accurately. To this end, in this article three groups of penetrating features are proposed to capture the characteristics of CVRPs. The first group consists of some basic features of CVRPs, where several features are suggested to capture the distribution of customer demand, the relationship between customer demand and vehicle capacity, besides some common attributes widely used in CVRPs. The second group is composed of the features extracted from some CVRP solutions generated by local search, where in addition to the feasible and better solutions, the worse solutions and the distribution of travel cost are also used to measure the sensitivity of CVRPs to local search operations. The third group is made up of image features obtained by depicting CVRP instances through images, which is first introduced by us to enhance the generalization of algorithm recommendation. Furthermore, based on the three groups of features, an algorithm recommendation method called ARM-I is built on the basis of a KNN classifier to recommend suitable algorithm for CVRPs. Experimental results on several selected benchmarks demonstrate the effectiveness of the designed features. More interestingly, the proposed ARM-I shows high generalization on real-world instances.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114890149","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

On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best 论稳态进化算法和选择压力:为什么基于逆秩的生殖试验分配是最好的

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-03-18 DOI: 10.1145/3427474

Dogan Corus, Andrei Lissovoi, P. S. Oliveto, C. Witt

{"title":"On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best","authors":"Dogan Corus, Andrei Lissovoi, P. S. Oliveto, C. Witt","doi":"10.1145/3427474","DOIUrl":"https://doi.org/10.1145/3427474","url":null,"abstract":"We analyse the impact of the selective pressure for the global optimisation capabilities of steady-state evolutionary algorithms (EAs). For the standard bimodal benchmark function TwoMax, we rigorously prove that using uniform parent selection leads to exponential runtimes with high probability to locate both optima for the standard ( +1) EA and ( +1) RLS with any polynomial population sizes. However, we prove that selecting the worst individual as parent leads to efficient global optimisation with overwhelming probability for reasonable population sizes. Since always selecting the worst individual may have detrimental effects for escaping from local optima, we consider the performance of stochastic parent selection operators with low selective pressure for a function class called TruncatedTwoMax, where one slope is shorter than the other. An experimental analysis shows that the EAs equipped with inverse tournament selection, where the loser is selected for reproduction and small tournament sizes, globally optimise TwoMax efficiently and effectively escape from local optima of TruncatedTwoMax with high probability. Thus, they identify both optima efficiently while uniform (or stronger) selection fails in theory and in practice. We then show the power of inverse selection on function classes from the literature where populations are essential by providing rigorous proofs or experimental evidence that it outperforms uniform selection equipped with or without a restart strategy. We conclude the article by confirming our theoretical insights with an empirical analysis of the different selective pressures on standard benchmarks of the classical MaxSat and multidimensional knapsack problems.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126394814","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: 10

Genetic Improvement of Routing Protocols for Delay Tolerant Networks 延迟容忍网络路由协议的遗传改进

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-03-12 DOI: 10.1145/3453683

Michela Lorandi, Leonardo Lucio Custode, Giovanni Iacca

{"title":"Genetic Improvement of Routing Protocols for Delay Tolerant Networks","authors":"Michela Lorandi, Leonardo Lucio Custode, Giovanni Iacca","doi":"10.1145/3453683","DOIUrl":"https://doi.org/10.1145/3453683","url":null,"abstract":"Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of, e.g., (possibly, unmanned) vehicles and humans where, despite a lack of continuous connectivity, data must be transmitted while the network conditions change due to the nodes’ mobility. In these contexts, routing is NP-hard and is usually solved by heuristic “store and forward” replication-based approaches, where multiple copies of the same message are moved and stored across nodes in the hope that at least one will reach its destination. Still, the existing routing protocols produce relatively low delivery probabilities. Here, we genetically improve two routing protocols widely adopted in DTNs, namely, Epidemic and PRoPHET, in the attempt to optimize their delivery probability. First, we dissect them into their fundamental components, i.e., functionalities such as checking if a node can transfer data, or sending messages to all connections. Then, we apply Genetic Improvement (GI) to manipulate these components as terminal nodes of evolving trees. We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes and find that GI produces consistent gains in delivery probability in four cases. We then verify if this improvement entails a worsening of other relevant network metrics, such as latency and buffer time. Finally, we compare the logics of the best evolved protocols with those of the baseline protocols, and we discuss the generalizability of the results across test cases.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115594453","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: 3

Reproducibility in Evolutionary Computation 进化计算中的再现性

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2021-02-05 DOI: 10.1145/3466624

M. L'opez-Ib'anez, J. Branke, L. Paquete

引用次数: 21