Agent Learning Using Immune Evolved Genetic Network Programming

Abstract

Genetic Network Programming(GNP) is extension of Genetic Algorithm(GA). The GNP is suitable for an agent programming. The GNP can evolve anoperation program of the agent. But, the GNP has the premature convergence problem as an evolution technique as well as GA. On the other hand, to avoid the initial convergence of the GA, Immune Alogorithm(IA) which is introduced the immune suppression to the GA had developed.
Then if the GNP and IA are combined, a better algorithm for agent programming can be developed. Authors proposed Immune evolved Genetic Network Programming (IGNP). To compare the GNP and IGNP, the simmulation was done. As a result, IGNP is more exellent without the premature convergence than the GNP.
In this paper, the authors explain the IGNP outline and a effectiveness of the IGNP is stated through the simulation result.