Using genetic algorithms to explore pattern recognition in the immune system

Authors:
Stephanie Forrest;Brenda Javornik;Robert E. Smith;Alan S. Perelson
Affiliations:
Department of Computer Science, University of New Mexico, Albuquerque, NM 87131 forrest@cs.unm.edu;Department of Computer Science, University of New Mexico, Albuquerque, NM 87131 javornik@wombat.nexagen.com;Department of Engineering Mechanics, University of Alabama, Tuscaloosa, AL 35487 rob@comec4.mh.ua.edu;Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545 asp@receptor.lanl.gov
Venue:
Evolutionary Computation
Year:
1993

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Abstract

This paper describes an immune system model based on binary strings. The purpose of the model is to study the pattern-recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of the model. The paper reports simulation experiments on two pattern-recognition problems that are relevant to natural immune systems. Finally, it reviews the relation between the model and explicit fitness-sharing techniques for genetic algorithms, showing that the immune system model implements a form of implicit fitness sharing.