Software Verification of Redundancy in Neuro-Evolutionary Robotics

  • Jason Teo
  • Hussein A. Abbass
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2903)

Abstract

Evolutionary methods are now commonly used to automatically generate autonomous controllers for physical robots as well as for virtually embodied organisms. Although it is generally accepted that some amount of redundancy may result from using an evolutionary approach, few studies have focused on empirically testing the actual amount of redundancy that is present in controllers generated using artificial evolutionary systems. Network redundancy in certain application domains such as defence, space, and safeguarding, is unacceptable as it puts the reliability of the system at great risk. Thus, our aim in this paper is to test and compare the redundancies of artificial neural network (ANN) controllers that are evolved for a quadrupedal robot using four different evolutionary methodologies. Our results showed that the least amount of redundancy was generated using a self-adaptive Pareto evolutionary multi-objective optimization (EMO) algorithm compared to the more commonly used single-objective evolutionary algorithm (EA) and weighted sum EMO algorithm. Finally, self-adaptation was found to be highly beneficial in reducing redundancy when compared against a hand-tuned Pareto EMO algorithm.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jason Teo
    • 1
  • Hussein A. Abbass
    • 2
  1. 1.School of Engineering and Information TechnologyUniversiti Malaysia SabahKota Kinabalu, SabahMalaysia
  2. 2.Artificial Life and Adaptive Robotics (A.L.A.R.) Lab School of Information Technology and Electrical EngineeringUniversity of New South Wales @ Australian Defence Force AcademyCanberra, ACTAustralia

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