Computational processes of evolution and the gene expression messy genetic algorithm

Conference · Wed May 01 04:00:00 EDT 1996

OSTI ID:251408

Kargupta, H ^[1]

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1. Los Alamos National Lab., NM (United States). Computational Science Methods Div.

This paper makes an effort to project the theoretical lessons of the SEARCH (Search Envisioned As Relation and Class Hierarchizing) framework introduced elsewhere (Kargupta, 1995b) in the context of natural evolution and introduce the gene expression messy genetic algorithm (GEMGA) -- a new generation of messy GAs that directly search for relations among the members of the search space. The GEMGA is an O({vert_bar}{Lambda}{vert_bar}{sup k}({ell} + k)) sample complexity algorithm for the class of order-k delineable problems (Kargupta, 1995a) (problems that can be solved by considering no higher than order-k relations) in sequence representation of length {ell} and alphabet set {Lambda}. Unlike the traditional evolutionary search algorithms, the GEMGA emphasizes the computational role of gene expression and uses a transcription operator to detect appropriate relations. Theoretical conclusions are also substantiated by experimental results for large multimodal problems with bounded inappropriateness of representation.

• Research Organization: Los Alamos National Lab., NM (United States)
• Sponsoring Organization: Department of the Air Force, Washington, DC (United States)
• DOE Contract Number: W-7405-ENG-36
• OSTI ID: 251408
• Report Number(s): LA-UR--96-429; CONF-960820--2; ON: DE96008116; CNN: Grant F49620-94-1-0103
• Country of Publication: United States
• Language: English

Related Subjects

55 BIOLOGY AND MEDICINE
BASIC STUDIES
ALGORITHMS
BIOLOGICAL EVOLUTION
DNA
GENE RECOMBINATION
GENES
GENETICS
NONLINEAR PROBLEMS