Annealing a genetic algorithm over constraints

Abstract

A class of variable fitness genetic algorithms is studied as a technique for use on constrained optimization problems. Fitness is taken as the product of the objective with an "attenuation" factor which is 1 for feasible solutions but some variable fraction of 1 for infeasible ones. It is shown that this technique leads to algorithms which converge in probability to globally optimal feasible solutions. An application of the technique is made to a problem of engineering interest with excellent results: the ground water treatment problem for unconfined aquifers.