A Monte-Carlo study of genetic algorithm initial population generation methods

Abstract

Briefly describes genetic algorithms (GAs) and focuses attention on initial population generation methods for 2D knapsack problems. Based on work describing the probability that a random solution vector is feasible for 0-1 knapsack problems, we propose a simple heuristic for randomly generating good initial populations for GA applications to 2D knapsack problems. We report on an experiment comparing a current population generation technique with our proposed approach and find our proposed approach does a very good job of generating good initial populations.