ACM Transactions on Evolutionary Learning and Optimization最新文献_第4页

IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics IOHanalyzer:迭代优化启发式的详细性能分析

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2020-07-08 DOI: 10.1145/3510426

Hongya Wang, Diederick Vermetten, Furong Ye, Carola Doerr, Thomas Bäck

{"title":"IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics","authors":"Hongya Wang, Diederick Vermetten, Furong Ye, Carola Doerr, Thomas Bäck","doi":"10.1145/3510426","DOIUrl":"https://doi.org/10.1145/3510426","url":null,"abstract":"Benchmarking and performance analysis play an important role in understanding the behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic and evolutionary algorithms, Bayesian optimization algorithms, etc. This task, however, involves manual setup, execution, and analysis of the experiment on an individual basis, which is laborious and can be mitigated by a generic and well-designed platform. For this purpose, we propose IOHanalyzer, a new user-friendly tool for the analysis, comparison, and visualization of performance data of IOHs. Implemented in R and C++, IOHanalyzer is fully open source. It is available on CRAN and GitHub. IOHanalyzer provides detailed statistics about fixed-target running times and about fixed-budget performance of the benchmarked algorithms with a real-valued codomain, single-objective optimization tasks. Performance aggregation over several benchmark problems is possible, for example in the form of empirical cumulative distribution functions. Key advantages of IOHanalyzer over other performance analysis packages are its highly interactive design, which allows users to specify the performance measures, ranges, and granularity that are most useful for their experiments, and the possibility to analyze not only performance traces, but also the evolution of dynamic state parameters. IOHanalyzer can directly process performance data from the main benchmarking platforms, including the COCO platform, Nevergrad, the SOS platform, and IOHexperimenter. An R programming interface is provided for users preferring to have a finer control over the implemented functionalities.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128631755","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: 29

A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization 离散优化进化算法理论研究进展综述

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2020-06-30 DOI: 10.1145/3472304

Benjamin Doerr, F. Neumann

引用次数: 18

Greed Is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation 贪婪是好的:贝叶斯优化中的探索和开发权衡

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2019-11-28 DOI: 10.1145/3425501

George De Ath, R. Everson, A. Rahat, J. Fieldsend

{"title":"Greed Is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation","authors":"George De Ath, R. Everson, A. Rahat, J. Fieldsend","doi":"10.1145/3425501","DOIUrl":"https://doi.org/10.1145/3425501","url":null,"abstract":"The performance of acquisition functions for Bayesian optimisation to locate the global optimum of continuous functions is investigated in terms of the Pareto front between exploration and exploitation. We show that Expected Improvement (EI) and the Upper Confidence Bound (UCB) always select solutions to be expensively evaluated on the Pareto front, but Probability of Improvement is not guaranteed to do so and Weighted Expected Improvement does so only for a restricted range of weights. We introduce two novel -greedy acquisition functions. Extensive empirical evaluation of these together with random search, purely exploratory, and purely exploitative search on 10 benchmark problems in 1 to 10 dimensions shows that -greedy algorithms are generally at least as effective as conventional acquisition functions (e.g., EI and UCB), particularly with a limited budget. In higher dimensions, -greedy approaches are shown to have improved performance over conventional approaches. These results are borne out on a real-world computational fluid dynamics optimisation problem and a robotics active learning problem. Our analysis and experiments suggest that the most effective strategy, particularly in higher dimensions, is to be mostly greedy, occasionally selecting a random exploratory solution.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116854096","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: 60

Reusability and Transferability of Macro Actions for Reinforcement Learning 强化学习中宏观动作的可重用性和可转移性

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2019-08-05 DOI: 10.1145/3514260

Yi-Hsiang Chang, Kuan-Yu Chang, Henry Kuo, Chun-Yi Lee

引用次数: 0

Precise Runtime Analysis for Plateau Functions 平台函数的精确运行时分析

ACM Transactions on Evolutionary Learning and Optimization Pub Date : 2018-06-04 DOI: 10.1145/3469800

Denis Antipov, Benjamin Doerr

{"title":"Precise Runtime Analysis for Plateau Functions","authors":"Denis Antipov, Benjamin Doerr","doi":"10.1145/3469800","DOIUrl":"https://doi.org/10.1145/3469800","url":null,"abstract":"To gain a better theoretical understanding of how evolutionary algorithms (EAs) cope with plateaus of constant fitness, we propose the n-dimensional Plateauk function as natural benchmark and analyze how different variants of the (1 + 1) EA optimize it. The Plateauk function has a plateau of second-best fitness in a ball of radius k around the optimum. As evolutionary algorithm, we regard the (1 + 1) EA using an arbitrary unbiased mutation operator. Denoting by α the random number of bits flipped in an application of this operator and assuming that Pr [α = 1] has at least some small sub-constant value, we show the surprising result that for all constant k ≥ 2, the runtime T follows a distribution close to the geometric one with success probability equal to the probability to flip between 1 and k bits divided by the size of the plateau. Consequently, the expected runtime is the inverse of this number, and thus only depends on the probability to flip between 1 and k bits, but not on other characteristics of the mutation operator. Our result also implies that the optimal mutation rate for standard bit mutation here is approximately k/(en). Our main analysis tool is a combined analysis of the Markov chains on the search point space and on the Hamming level space, an approach that promises to be useful also for other plateau problems.","PeriodicalId":337052,"journal":{"name":"ACM Transactions on Evolutionary Learning and Optimization","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131147971","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: 8