arrow
Return

Gradual distributed real-coded genetic algorithms

delete2000-04-01
delete207
delete
OA
AI
F
Francisco Herrera
M
Manuel Lozano
DOI:10.1109/4235.843494delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
A major problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of diversity in the population. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallell several subpopulations that are processed by genetic algorithms, with each one being independent of the others. Furthermore. a migration mechanism produces a chromosome exchange between the subpopulations. Making distinctions between the subpopulations by applying genetic algorithms with different configurations, ne obtain the so-called heterogeneous distributed genetic algorithms, These algorithms represent a promising mag for introducing a correct exploration/exploitation balance in order to avoid premature convergence and reach approximate final solutions. This paper presents the gradual distributed real-coded genetic algorithms, a type of heterogeneous distributed real-coded genetic algorithms that apply a different crossover operator to each subpopulation, The importance of this operator on the genetic algorithm's performance allowed us to differentiate between the subpopulations in this fashion. Using crossover operators presented for real-coded genetic algorithms, we implement three instances of gradual distributed real-coded genetic algorithms. Experimental results show that the proposals consistently outperform sequential real-coded genetic algorithms and homogeneous distributed real-coded genetic algorithms, which are equivalent to them and other mechanisms presented in the literature. These proposals offer two important advantages at the same time: better reliability and accuracy.
Keywords:
crossover operator
distributed genetic algorithms
multiresolution
premature convergence
selective pressure

Journal

IEEE Transactions on Evolutionary Computation cover
IEEE Transactions on Evolutionary Computation
IF:
12
Papers:
1.9K
Citations:
2.4W

Organization

No organization information available
Cited Papers

Cited Papers

Effects of ethanol and nomifensine on NE clearance in the cerebellum of young and aged Fischer 344 rats
err1997-05-01
err0
PREAI
errAnya M.-Y Lin; Paula C Bickford; Michael R Palmer; Elizabeth J Cline; Greg A Gerhardt
errShare
errSave
Dioxin Exposure and Benign Prostatic Hyperplasia
err2006-07-01
err0
PREAI
errAmit Gupta; Arnold Schecter; Corinne C. Aragaki; Claus G. Roehrborn
errShare
errSave
Chemical Modification of Banana Trunk Fibers for the Production of Green Composites
err2021-06-11
err0
errOAAI
errKathiresan V. Sathasivam; Mas Rosemal Hakim Mas Haris; Shivkanya Fuloria; Neeraj Kumar Fuloria; Rishabha Malviya; Vetriselvan Subramaniyan
errShare
errSave
PL04.10 Osimertinib With or Without Savolitinib as 1L in De Novo MET Aberrant, EGFRm Advanced NSCLC (CTONG 2008): A Phase II Trial
err2024-10-01
err0
PREAI
errJ. Yang; A. Li; W.N. Feng; J. Li; H.H. Yan; B.F. Xu; J. Zhao; Y. Jia; K.J. Tang; Y.S. Li; C.Z. Zhou; Y. Fan; C.R. Xu; Y.L. Sun; H.J. Chen
errShare
errSave
Characteristics of the Raman spectra of diamond-like carbon films. Influence of methods of synthesis
err2017-01-01
err0
PREAI
errAlexander Zolkin; Anna Semerikova; Sergey Chepkasov; Maxim Khomyakov
errShare
errSave
researcher View more