Locality in genetic algorithms
Vahl Scott Gordon · 2002
Quantifies spatial locality in various genetic algorithms. In particular, the following algorithms are examined: Goldberg's (1989) standard genetic algorithm (SGA), several "island" models, and two cellular algorithms (fixed topology and random walk). The approaches are also applicable to evolution strategies that employ methods such as recombination or parameter averaging. Two different locality metrics are presented: the percentage of remote references (for parallel machines with a few processors), and the traffic per link (for massively parallel machines). We derive expressions for computing locality in this manner, and discuss the utility, implications and limitations of our results.>