Genetic network programming - application to intelligent agents

Abstract

Recently many studies have been made on the automatic design of complex systems using evolutionary optimization techniques such as genetic algorithms (GA), evolution strategy (ES), evolutionary programming (EP) and genetic programming (GP). It is generally recognized that these techniques are very useful for optimizing fairly complex systems such as the generation of intelligent behavior sequences of robots. A new method, genetic network programming (GNP), is proposed in order to acquire these behavior sequences efficiently. GNP is composed of plural nodes for agents to execute simple judgment/processing and they are connected with each other to form a network structure. Agents behave according to the contents of the nodes and their connections in GNP. In order to obtain a better structure, the GNP changes itself using evolutionary optimization techniques.