Parallel Ant Programming using genetic operators

Abstract

Ant Programming (AP) is an automatic programming method, which combines tree-structural representations of Genetic Programming (GP) and search mechanism by pheromone communications of ants in Ant Colony Optimization (ACO). In AP, a single prototype tree, in which respective nodes have different pheromone tables, is prepared, and an ant searches solutions under the prototype tree. The structure of the prototype tree does not change during search. Therefore, premature convergence often occurs. To solve the problem, we propose parallel AP using genetic operators of GP. In this method, multiple prototype trees are generated and the structures change by GP operators such as selection, crossover and mutation. We applied our proposed method to symbolic regressions and logical function synthesis. As the results of experiments, our proposed method showed better performance than the conventional AP.