A comparison of cohort genetic algorithms with canonical serial and island-model distributed GA's

This work considers the cohort genetic algorithm, a new type of genetic algorithm introduced by Holland. The cohort GA differs in several ways from the traditional canonical serial GA and island-model distributed GA. A key motivation for its development was to reduce "hitchhiking" – premature convergence of currently low-significance loci located near loci at which good building blocks are found early in the search process. This work compares one version of the cohort GA with canonical serial and island-model distributed GA's on the basis of their abilities to reduce hitchhiking. The comparison is done using two types of test functions: the "royal road with potholes" function and hyperplane-defined functions ("HDF's"). It is experimentally shown that even though theoretically the cohort GA can reduce hitchhiking, the particular version of the cohort GA tested is prone to another form of premature convergence, and it performed worse than the other GA's. It is also shown that a small change in the placement of offspring among cohorts in the cohort GA may dramatically improve its performance. This suggests that further work on the cohort GA may well be fruitful.