Algorithms, genetics, and populations: The schemata theorem revisited

✍ Scribed by Freddy Bugge Christiansen; Marcus W. Feldman

• Publisher: John Wiley and Sons
• Year: 1998
• Tongue: English
• Weight: 114 KB
• Volume: 3
• Category: Article
• ISSN: 1076-2787
• DOI: 10.1002/(sici)1099-0526(199801/02)3:3<57::aid-cplx9>3.0.co;2-j

✦ Synopsis

Models for the genetic evolution of natural populations have supplied the inspiration for the adaptive computer algorithms known as "genetic algorithms." In its original form, a genetic algorithm simulates the evolution of a haploid population: Genetic variation is produced by mutation at a number of genetic loci in a chromosome, represented as a string of bits, and the variants in the various chromosomes are reshuffled by genetic recombination. In nature, the ability of a haploid individual to survive and reproduce is expressed as its fitness; in computer science the value of a string is measured by its ability to program a given task. The performance of genetic algorithms is evaluated in the "schemata theorem," which we present and discuss in the context of the population genetics of multiple loci and propose a generalization of the theorem.