Abstract
One excellent crossover method for the real-coded genetic algorithm (RGA) is the unimodal normal distribution crossover method (UNDX). The UNDX is superior to the blend crossover method (BLX). The UNDX uses Gaussian distribution functions based on the main and sub searching lines. In this article, we present a method of improving the searching performance of the RGA. We propose the use of biased probability distribution functions (BPDFs) based on the main and sub searching lines in the crossover process. The crossover with BPDFs frequently produces offspring that are close to the best individuals in the current generation, and it is highly likely that these offspring will offer the best solution to the problem. Furthermore, we propose a mutation that has a constant and extended range that is wider than that of the UNDX. Simulations show the efficiency of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
DE Goldberg (1989) Genetic algorithms in search, optimization and machine learning Addison-Wesley Reading 217–307 Occurrence Handle0721.68056
L Davis (1991) Handbook of genetic algorithms Van Nostrand Reinhold New York 1–101
M Mitchell (1996) An introduction to genetic algorithms MIT Press Cambridge 1–33
Eshelman LJ, Schaffer JD (1993) Real-coded genetic algorithms and interval schemata. Foundations of genetic algorithms 2. In: Whitley LD (ed) Morgan Kaufmann, Las Altos, pp 187–202
H Kinjo N Oshiro K Kurata et al. (2006) ArticleTitleImprovement of searching performance of real-coded genetic algorithm by use of crossover with biased probability distribution function and mutation (in Japanese) Trans SICE 42 IssueID6 581–590
I Ono H Satoh S Kobayashi (1999) ArticleTitleA real-coded genetic algorithm for function optimization using the unimodal normal distribution crossover (in Japanese) Trans Jpn Soc Artif Intell 16 IssueID6 1146–1155
H Kita I Ono S Kobayashi (1999) ArticleTitleTheoretical analysis of the unimodal normal distribution crossover for real-coded genetic algorithms (in Japanese) Trans SICE 35 IssueID11 1333–1339
H Kita I Ono S Kobayashi (2000) ArticleTitleMulti-parental extension of the unimodal normal distribution crossover for real-coded genetic algorithms (in Japanese) Trans SICE 36 IssueID10 875–883
H Okamura K Kitasuka T Dohi (2003) ArticleTitlePerformance evolution of genetic algorithms based on Markovian analysis (in Japanese) Trans ISCIE 16 IssueID7 303–312
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006
About this article
Cite this article
Nakanishi, H., Kinjo, H., Oshiro, N. et al. Searching performance of a real-coded genetic algorithm using biased probability distribution functions and mutation. Artif Life Robotics 11, 37–41 (2007). https://doi.org/10.1007/s10015-006-0396-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10015-006-0396-6