Paper:
Language: English:

Effect of Genetic Encoding on Evolution of Efficient Neural Controllers

Genci Capi

Graduate School of Science and Engineering for Research, University of Toyama, Gofuku Campus, 3190 Gofuku, Toyama 930-8555, Japan

Received: April 23, 2007

Accepted: June 29, 2007

Keywords: multiobjective optimization, evolution, neural controllers

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.12, No.4 pp. 377-381, 2008

Abstract

In this paper, we present a new method based on multiobjective evolutionary algorithms to evolve low complexity neural controllers for the robots that have to perform two different tasks, simultaneously. In our method, each task and the structure of neural controller are considered as separated objective functions. We compare the results of two different encoding schemes: (1) Connectionist encoding and (2) Node based encoding. Simulation results show that multiobjective evolution can be successfully applied to generate low complexity neural controllers. In addition, node based encoding outperformed connectionist encoding in terms of robot performance and robustness of the neural controller.

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