Evolving directed graphs with artificial bee colony algorithm

Abstract

Artificial bee colony (ABC) algorithm is a relatively new optimization technique that simulates the intelligent foraging behavior of honey bee swarms. It has been applied to several optimization domains to show its efficient evolution ability. In this paper, ABC algorithm is applied for the first time to evolve a directed graph chromosome structure, which derived from a recent graph-based evolutionary algorithm called genetic network programming (GNP). Consequently, it is explored to new application domains which can be efficiently modeled by the directed graph of GNP. In this work, a problem of controlling the agents's behavior under a wellknown benchmark testbed called Tileworld are solved using the ABC-based evolution strategy. Its performance is compared with several very well-known methods for evolving computer programs, including standard GNP with crossover/mutation, genetic programming (GP) and reinforcement learning (RL).