Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP)

Author

Hirasawa, Kotaro ; Okubo, M. ; Katagiri, H. ; Hu, J. ; Murata, J.

Author_Institution

Kyushu Univ., Fukuoka, Japan

Volume

2

fYear

2001

fDate

2001

Firstpage

1276

Abstract

Recently, many methods of evolutionary computation such as genetic algorithm (GA) and genetic programming (GP) have been developed as a basic tool for modeling and optimizing of complex systems. Generally speaking, GA has the genome of a string structure, while the genome in GP is the tree structure. Therefore, GP is suitable for constructing complicated programs, which can be applied to many real world problems. However, GP might sometimes be difficult to search for a solution because of its bloat. A novel 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 of the evolution of ant behavior 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 ant behaviors

Keywords

behavioural sciences computing; biology computing; genetic algorithms; tree data structures; trees (mathematics); zoology; Genetic Network Programming; Genetic Programming; ant behavior simulation; bloat; complicated programs; evolutionary computation; evolutionary method; genetic algorithm; genome; real world problems; searching efficiency; string structure; tree structure; Bioinformatics; Computational intelligence; Computer networks; Economic indicators; Genetic programming; Genomics; Optimization methods; Parallel algorithms; Symbiosis; Tree data structures;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2001. Proceedings of the 2001 Congress on

Conference_Location

Seoul

Print_ISBN

0-7803-6657-3

Type

conf

DOI

10.1109/CEC.2001.934337

Filename

934337