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Recently, many methods of evolutionary computation such as Genetic Algorithm (GA) and Genetic Pro gramming (GP) have been developed as a basic tool for modeling and optimizing the complex systems. Generally speaking, GA has the genome of string structure, while the genome in GP is the tree structure. Therefore, GP is suitable to construct the complicated programs, which can be applied to many real world problems. But, GP is sometimes difficult to search for a solution because of its bloat and introns and also because the effect of crossover and mutation deffers depending on which nodes are operated by crossover and mutation, therefore, sometimes premature convergences emerge in GP. In this paper, a new evolutionary method named Genetic Network Programming (GNP), whose genome is a network structure is proposed to overcome the low searching efficiency of GP and is applied to the problem on evolution of behaviors of ants in order to study the effectiveness of GNP. In addition, the comparison of the performances between GNP and GP is carried out in simulations on ants behaviors.

Comparison between Genetic Network Programing and Genetic Programming using evolution of ant's behaviors