The Speciating Island Model: An alternative parallel evolutionary algorithm

被引:28
作者
Gustafson, Steven [1 ]
Burke, Edmund K.
机构
[1] GE Global Res, Comp & Decis Sci, Niskayuna, NY 12309 USA
[2] Univ Nottingham, Sch Comp Sci & IT, Nottingham NG8 1BB, England
基金
英国工程与自然科学研究理事会;
关键词
parallel evolutionary algorithms; genetic programming; islands;
D O I
10.1016/j.jpdc.2006.04.017
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper presents an investigation of a novel model for parallel evolutionary algorithms (EAs) based on the biological concept of species. In EA population search, new species represent solutions that could lead to good solutions but are disadvantaged due to their dissimilarity from the rest of the population. The Speciating Island Model (SIM) attempts to exploit new species when they arise by allocating them to new search processes executing on other islands (other processors). The long term goal of the SIM is to allow new species to diffuse throughout a large (conceptual) parallel computer network, where idle and unimproving processors initiate a new search process with them. In this paper, we focus on the successful identification and exploitation of new species and show that the SIM can achieve improved solution quality as compared to a canonical parallel EA. (C) 2006 Elsevier Inc. All rights reserved.
引用
收藏
页码:1025 / 1036
页数:12
相关论文
共 25 条
[1]   Parallelism and evolutionary algorithms [J].
Alba, E ;
Tomassini, M .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (05) :443-462
[2]  
ANDRE D, 1996, ADV GENETIC PROGRAMM, V2, pCH16
[3]  
[Anonymous], P 2 INT C GEN ALG
[4]  
[Anonymous], 2004, THESIS U NOTTINGHAM
[5]   A general framework to understand parallel performance in heterogeneous clusters: analysis of a new adaptive parallel genetic algorithm [J].
Bazterra, VE ;
Cuma, M ;
Ferraro, MB ;
Facelli, JC .
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2005, 65 (01) :48-57
[6]  
Bessaou M., 2000, Parallel Problem Solving from Nature PPSN VI. 6th International Conference. Proceedings (Lecture Notes in Computer Science Vol.1917), P437
[7]   Diversity in genetic programming: An analysis of measures and correlation with fitness [J].
Burke, EK ;
Gustafson, S ;
Kendall, G .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2004, 8 (01) :47-62
[8]   A massively parallel architecture for distributed genetic algorithms [J].
Eklund, SE .
PARALLEL COMPUTING, 2004, 30 (5-6) :647-676
[9]  
Eldredge N., 1972, P82
[10]   An empirical study of multipopulation genetic programming [J].
Francisco Fernández ;
Marco Tomassini ;
Leonardo Vanneschi .
Genetic Programming and Evolvable Machines, 2003, 4 (1) :21-51