Proceedings of the 11th Annual conference on Genetic and evolutionary computation最新文献

Generative relations for evolutionary equilibria detection 进化平衡检测的生成关系

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570103

D. Dumitrescu, R. Lung, T. Mihoc

引用次数: 23

pCMALib: a parallel fortran 90 library for the evolution strategy with covariance matrix adaptation 具有协方差矩阵自适应进化策略的并行fortran 90库

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570090

C. Müller, B. Baumgartner, Georg Ofenbeck, B. Schrader, I. Sbalzarini

{"title":"pCMALib: a parallel fortran 90 library for the evolution strategy with covariance matrix adaptation","authors":"C. Müller, B. Baumgartner, Georg Ofenbeck, B. Schrader, I. Sbalzarini","doi":"10.1145/1569901.1570090","DOIUrl":"https://doi.org/10.1145/1569901.1570090","url":null,"abstract":"We present pCMALib, a parallel software library that implements the Evolution Strategy with Covariance Matrix Adaptation (CMA-ES). The library is written in Fortran 90/95 and uses the Message Passing Interface (MPI) for efficient parallelization on shared and distributed memory machines. It allows single CMA-ES optimization runs, embarrassingly parallel CMA-ES runs, and coupled parallel CMA-ES runs using a cooperative island model. As one instance of an island model CMA-ES, the recently presented Particle Swarm CMA-ES (PS-CMA-ES) is included using collaborative concepts from Swarm Intelligence for the migration model. Special attention has been given to an efficient design of the MPI communication protocol, a modular software architecture, and a user-friendly programming interface. The library includes a Matlab interface and is supplemented with an efficient Fortran implementation of the official CEC 2005 set of 25 real-valued benchmark functions. This is the first freely available Fortran implementation of this standard benchmark test suite. We present test runs and parallel scaling benchmarks on Linux clusters and multi-core desktop computers, showing good parallel efficiencies and superior computational performance compared to the reference implementation.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115769238","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: 21

Elitism, fitness, and growth 精英主义，健康和成长

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570199

Gerardo Gonzalez, Dean Frederick Hougen

引用次数: 0

The bee colony-inspired algorithm (BCiA): a two-stage approach for solving the vehicle routing problem with time windows 蜂群算法(BCiA):一种求解带时间窗车辆路径问题的两阶段方法

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1569906

S. Häckel, P. Dippold

引用次数: 30

FUGA: a fuzzy-genetic analog circuit optimization kernel FUGA:一个模糊遗传模拟电路优化核

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570156

Pedro Sousa, C. Duarte, N. Horta

引用次数: 0

Benchmarking coevolutionary teaming under classification problems with large attribute spaces 大属性空间分类问题下协同进化团队的基准研究

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570226

J. Doucette, P. Lichodzijewski, M. Heywood

引用次数: 1

Solving complex high-dimensional problems with the multi-objective neural estimation of distribution algorithm 用分布的多目标神经估计算法求解复杂高维问题

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1569987

Luis Martí, Jesús García, A. Berlanga, J. M. Molina

引用次数: 21

Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change 动态进化优化:对变化的频率和幅度的分析

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570131

Philipp Rohlfshagen, P. Lehre, X. Yao

{"title":"Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change","authors":"Philipp Rohlfshagen, P. Lehre, X. Yao","doi":"10.1145/1569901.1570131","DOIUrl":"https://doi.org/10.1145/1569901.1570131","url":null,"abstract":"In this paper, we rigorously analyse how the magnitude and frequency of change may affect the performance of the algorithm (1+1) EAdyn on a set of artificially designed pseudo-Boolean functions, given a simple but well-defined dynamic framework. We demonstrate some counter-intuitive scenarios that allow us to gain a better understanding of how the dynamics of a function may affect the runtime of an algorithm. In particular, we present the function Magnitude, where the time it takes for the (1+1) EAdyn to relocate the global optimum is less than n2log n (i.e., efficient) with overwhelming probability if the magnitude of change is large. For small changes of magnitude, on the other hand, the expected time to relocate the global optimum is eΩ(n) (i.e., highly inefficient). Similarly, the expected runtime of the (1+1) EAdyn on the function Balance is O(n2) (efficient) for a high frequencies of change and nΩ(√n) (highly inefficient) for low frequencies of change. These results contribute towards a better understanding of dynamic optimisation problems in general and show how traditional analytical methods may be applied in the dynamic case.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122813302","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: 73

On the scalability of XCS(F) XCS(F)的可扩展性

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570077

Patrick O. Stalph, Martin Volker Butz, D. Goldberg, Xavier Llorà

{"title":"On the scalability of XCS(F)","authors":"Patrick O. Stalph, Martin Volker Butz, D. Goldberg, Xavier Llorà","doi":"10.1145/1569901.1570077","DOIUrl":"https://doi.org/10.1145/1569901.1570077","url":null,"abstract":"Many successful applications have proven the potential of Learning Classifier Systems and the XCS classifier system in particular in datamining, reinforcement learning, and function approximation tasks. Recent research has shown that XCS is a highly flexible system, which can be adapted to the task at hand by adjusting its condition structures, learning operators, and prediction mechanisms. However, fundamental theory concerning the scalability of XCS dependent on these enhancements and problem difficulty is still rather sparse and mainly restricted to boolean function problems. In this article we developed a learning scalability theory for XCSF---the XCS system applied to real-valued function approximation problems. We determine crucial dependencies on functional properties and on the developed solution representation and derive a theoretical scalability model out of these constraints. The theoretical model is verified with empirical evidence. That is, we show that given a particular problem difficulty and particular representational constraints XCSF scales optimally. In consequence, we discuss the importance of appropriate prediction and condition structures regarding a given problem and show that scalability properties can be improved by polynomial orders, given an appropriate, problem-suitable representation.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122871943","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: 7

Simulating human grandmasters: evolution and coevolution of evaluation functions 模拟人类大师:评价函数的进化与协同进化

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570100

E. David, H. J. Herik, Moshe Koppel, N. Netanyahu

引用次数: 12