Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII最新文献

Parallel Evolutionary Algorithms Performing Pairwise Comparisons 并行进化算法执行两两比较

Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII Pub Date : 2015-01-17 DOI: 10.1145/2725494.2725499

M. Cauwet, O. Teytaud, Shih-Yuan Chiu, Kuo-Min Lin, Shi-Jim Yen, D. St-Pierre, F. Teytaud

引用次数: 0

Understanding Simple Asynchronous Evolutionary Algorithms 理解简单的异步进化算法

Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII Pub Date : 2015-01-17 DOI: 10.1145/2725494.2725509

Eric O. Scott, K. D. Jong

引用次数: 36

Hypomixability Elimination In Evolutionary Systems 进化系统中的低混性消除

Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII Pub Date : 2015-01-17 DOI: 10.1145/2725494.2725511

Keki M. Burjorjee

{"title":"Hypomixability Elimination In Evolutionary Systems","authors":"Keki M. Burjorjee","doi":"10.1145/2725494.2725511","DOIUrl":"https://doi.org/10.1145/2725494.2725511","url":null,"abstract":"Hypomixability Elimination is an intriguing form of computation thought to underlie general-purpose, non-local, noise-tolerant adaptation in recombinative evolutionary systems. We demonstrate that hypomixability elimination in recombinative evolutionary systems can be efficient by using it to obtain optimal bounds on the time and queries required to solve a subclass (k=7, η=1/5) of a familiar computational learning problem: PAC-learning parities with noisy membership queries; where k is the number of relevant attributes and η is the oracle's noise rate. Specifically, we show that a simple genetic algorithm with uniform crossover (free recombination) that treats the noisy membership query oracle as a fitness function can be rigged to PAC-learn the relevant variables in O(log (n/δ)) queries and O(n log (n/δ)) time, where n is the total number of attributes and δ is the probability of error. To the best of our knowledge, this is the first time optimally efficient computation has been shown to occur in, an evolutionary algorithm, on a non-trivial problem. The optimality result and indeed the implicit implementation of hypomixability elimination by a simple genetic algorithm depends crucially on recombination. This dependence yields a fresh, unified explanation for sex, adaptation, speciation, and the emergence of modularity in evolutionary systems. Compared to other explanations, Hypomixability Theory is exceedingly parsimonious. For example, it does not assume deleterious mutation, a changing fitness landscape, or the existence of building blocks.","PeriodicalId":112331,"journal":{"name":"Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115870706","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

Self-Adapting the Brownian Radius in a Differential Evolution Algorithm for Dynamic Environments 动态环境下微分进化算法中的布朗半径自适应

Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII Pub Date : 2015-01-17 DOI: 10.1145/2725494.2725505

M. D. Plessis, A. Engelbrecht, A. Calitz

引用次数: 2

Information Geometry of the Gaussian Distribution in View of Stochastic Optimization 随机优化下高斯分布的信息几何

Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII Pub Date : 2015-01-17 DOI: 10.1145/2725494.2725510

Luigi Malagò, Giovanni Pistone

引用次数: 50

Evolution Strategies with Additive Noise: A Convergence Rate Lower Bound 具有加性噪声的进化策略:收敛速率下界

Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII Pub Date : 2015-01-17 DOI: 10.1145/2725494.2725500

S. Morales, M. Cauwet, O. Teytaud

引用次数: 21

Run-Time Analysis of Population-Based Evolutionary Algorithm in Noisy Environments 噪声环境下基于种群的进化算法运行时分析

Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII Pub Date : 2015-01-17 DOI: 10.1145/2725494.2725498

A. Prügel-Bennett, J. Rowe, J. Shapiro

引用次数: 31

A More Efficient Rank-one Covariance Matrix Update for Evolution Strategies 进化策略中一种更有效的秩一协方差矩阵更新

Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII Pub Date : 2015-01-17 DOI: 10.1145/2725494.2725496

Oswin Krause, C. Igel

引用次数: 25

Efficient Optimisation of Noisy Fitness Functions with Population-based Evolutionary Algorithms 基于种群进化算法的噪声适应度函数高效优化

Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII Pub Date : 2015-01-17 DOI: 10.1145/2725494.2725508

D. Dang, P. Lehre

引用次数: 39

Evolutionary Dynamics on Graphs: Invited Talk 图上的进化动力学:特邀演讲

Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII Pub Date : 2015-01-17 DOI: 10.1145/2725494.2725495

L. A. Goldberg

{"title":"Evolutionary Dynamics on Graphs: Invited Talk","authors":"L. A. Goldberg","doi":"10.1145/2725494.2725495","DOIUrl":"https://doi.org/10.1145/2725494.2725495","url":null,"abstract":"The Moran process [5], as adapted by Lieberman, Hauert and Nowak [4], is a discrete-time random process which models the spread of genetic mutations through populations. Individuals are modelled as the vertices of a graph. Each vertex is either infected or uninfected. The model has a parameter r > 0. Infected vertices have fitness r and uninfected vertices have fitness 1. At each step, an individual is selected to reproduce with probability proportional to its fitness. This vertex chooses one of its neighbours uniformly at random and updates the state of that neighbour (infected or not) to match its own. In the initial state, one vertex is chosen uniformly at random to be infected and the other vertices are uninfected. If the graph is strongly connected then the process will terminate with probability 1, either in the state where every vertex is infected (known as fixation) or in the state where no vertex is infected (known as extinction). The principal quantities of interest are the fixation probability (the probability of reaching fixation) and the expected absorption time (the expected number of steps before fixation or extinction is reached). In general, these depend on both the graph topology and the parameter r. We study three questions.","PeriodicalId":112331,"journal":{"name":"Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121590058","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