CROSS-POLLINATING PARALLEL GENETIC ALGORITHMS FOR MULTI-OBJECTIVE SEARCH AND OPTIMIZATION
Lucas A. Wilson, Michelle Moore · International Journal of Foundations of Computer Science · 2005
This paper discusses the design of a parallel genetic algorithm to generate solutions to multi-objective problems. The algorithm uses multiple optimization criteria, independent cross-pollinating populations, and handles multiple hard constraints. Individuals in the population consist of multiple chromosomes. The complexity of the algorithm is the number of generations processed times O(N2) where N is the total number of individuals used for path generation on any of the optimizations. The results of initial empirical studies on the effects of pollination and recommendations for possible future work are presented.