Evolving plural programs by genetic network programming with multi-start nodes

Authors:
Shingo Mabu;Kotaro Hirasawa
Affiliations:
Graduate School of Information, Production and Systems, Waseda University, Fukuoka, Japan;Graduate School of Information, Production and Systems, Waseda University, Fukuoka, Japan
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

Citing 6
Cited 0

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Original Contribution: How to solve the N-bit parity problem with two hidden units

Neural Networks
Genetic programming II: automatic discovery of reusable programs

Genetic programming II: automatic discovery of reusable programs
A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning

Evolutionary Computation
A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automatic program generation is one of the applicable fields of evolutionary computation, and Genetic Programming (GP) is the typical method for this field. On the other hand, Genetic Network Programming (GNP) has been proposed as an extended algorithm of GP in terms of gene structures. GNP is a graph-based evolutionary algorithm and applied to automatic program generation in this paper. GNP has directed graph structures which have some features inherently such as re-usability of nodes and the fixed number of nodes. These features contribute to creating complicated programs with compact program structures. In this paper, the extended algorithm of GNP is proposed, which can create plural programs simultaneously in one individual by using multi-start nodes. In addition, GNP can evolve the programs in one individual considering the fitness and also its standard deviation in order to evolve the plural programs efficiently. In the simulations, Even-n-Parity problem and Mirror Symmetry problem are used for the performance evaluation, and the results show that the proposed method outperforms the original GNP.