Analysis of the behavior of MGG and JGG as a selection model for real-coded genetic algorithms

被引：8

作者：

Akimoto Y. ^{[1
]}

Nagata Y.

Sakuma J. ^{[2
]}

Ono I. ^{[1
]}

Kobayashi S.

机构：

[1] Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

[2] Graduate School of Systems and Information Engineering, University of Tsukuba

来源：

Transactions of the Japanese Society for Artificial Intelligence | 2010年 / 25卷 / 02期

关键词：

Function optimization; Just generation gap; Minimal generation gap;

D O I：

10.1527/tjsai.25.281

中图分类号：

学科分类号：

摘要：

In this paper, we focus on analyzing the behavior of the selection models for real-coded genetic algorithms. Recent studies show that Just Generation Gap (JGG) selection model outperforms Minimal Generation Gap (MGG) model when a multi-parental crossover operator based on the hypothesis of the preservation of the statistics of parents is used. However, the validation of JGG selection model is not done yet. To validate the selection method of JGG, we analyze the differences of the behavior of JGG selection model and that of MGG selection model.

引用

页码：281 / 289

页数：8

共 5 条

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[2]

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[3]

Kita H., Ono I., Kobayashi S., Theoretical Analysis of the Unimodal Normal Distribution Crossover for Real-Coded Genetic Algorithms, Proceedings of the IEEE Congress on Evolutionary Computation, pp. 529-534, (1998)

[4]

Kita H., Ono I., Kobayashi S., Multi-parental Extension of the Unimodal Normal Distribution Crossover for Real-coded Genetic Algorithms, Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1581-1587, (1999)

[5]

Someya H., Theoretical Parameter Value for Appropriate Population Variance of the Distribution of Children in Real-coded GA, Proceedings of the IEEE Congress on Evolutionary Computation: CEC 2008 (IEEE World Congress on Compu-tational Intelligence: WCCI2008), pp. 2722-2729, (2008)

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