文章基本信息

标题：Real-coded GA for High-dimensional k -tablet Structures Proposal and Evaluation of Latent Variable Crossover LUNDX- m
作者：Jun Sakuma ; Shigenobu Kobayashi
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2004
卷号：19
期号：1
页码：28-37
DOI：10.1527/tjsai.19.28
出版社：The Japanese Society for Artificial Intelligence
摘要：This paper presents the Real-coded Genetic Algorithms(RCGA) which can treat with high-dimensional ill-scaled structures, what is called, k -tablet structure. The k -tablet structure is the landscape that the scale of the fitness function is different between the k -dimensional subspace and the orthogonal (n-k) -dimensional subspace. The search speed of traditional RCGAs degrades when high-dimensional k -tablet structures are included in the landscape of fitness function.
In this structure, offspring generated by crossovers is likely to spread wider region than the region where the parental population covers. This phenomenon causes the stagnation of the search. To resolve this problem, we propose a new crossover LUNDX- m , which uses only m -dimensional latent variables. The effectiveness of the proposal method is tested with several benchmark functions including k -tablet structures and we show that our proposal method performs better than traditional crossovers especially when the dimensionality n is higher than 100 .
关键词：real-coded genetic algorithm ; k-tablet structure ; landscape ; crossover ; latent variale