Exploring A Two-market Genetic Algorithm

Abstract

The ordinary genetic algorithm may be thought of as conducting a single market in which solutions compete for success, as measured by the fitness funtion. We introduce a two-market genetic algorithm, consisting of two phases, each of which is an ordinary single-market genetic algorithm. The twomarket genetic algorithm has a natural interpretation as a method of solving constrained optimization problems. Phase 1 is optimality improvement; it works on the problem without regard to constraints. Phase 2 is feasibility improvement; it works on the existing population of solutions and drives it towards feasibility. We tested this concept on 14 standard knapsack test problems for genetic algorithms, with excellent results. The paper concludes with discussions of why the twomarket genetic algorithm is successful and of how this work can be extended.