Title

The Modular Genetic Algorithm: Exploiting Regularities In The Problem Space

Abstract

We introduce the modular genetic algorithm (MGA). The modular genetic algorithm is a search algorithm designed for a class of problems pervasive throughout nature and engineering: problems with modularity and regularity in their solutions. We hypothesize that genetic search algorithms with explicit mechanisms to exploit regularity and modularity on the problem space would not only outperform conventional genetic search, but also scale better for this problem class. In this paper we present experimental evidence in support of our hypothesis. In our experiments, we compare a limited version of the modular genetic algorithm with a canonical genetic algorithm (GA) applied to the checkerboard-pattern discovery problem for search spaces of sizes 232, 2128, and 2512. We observe that the MGA significantly outperforms the GA for high complexities. More importantly, while the performance of the GA drops 22.50% when the complexity of the problem increases, the MGA performance drops only 11.38%. These results indicate that the MGA has a strong scalability property for problems with regularity and modularity in their solutions. © Springer-Verlag Berlin Heidelberg 2003.

Publication Date

1-1-2003

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

2869

Number of Pages

584-591

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-540-39737-3_73

Socpus ID

0142215137 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0142215137

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