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Cartesian Genetic Programming in an Open-Ended Evolution Environment

  • António Simões
  • Tiago Baptista
  • Ernesto Costa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10423)

Abstract

In this paper we describe and analyze the use of the Cartesian Genetic Programming method to evolve Artificial Neural Networks (CGPANN) in an open-ended evolution scenario. The issue of open-ended evolution has for some time been considered one of the open problems in the field of Artificial Life. In this paper we analyze the capabilities of CGPANN to evolve behaviors in a scenario without artificial selection, more specifically, without the use of explicit fitness functions. We use the BitBang framework and one of its example scenarios as a proof of concept. The results obtained in these first experiments show that it is indeed possible to evolve CGPANN brains, in an open-ended environment, without any explicit fitness function. We also present an analysis of different parameter configurations for the CGPANN when used in this type of scenario.

Keywords

Artificial life Multi-agent systems Open-ended evolution Neuroevolution Cartesian genetic programming 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • António Simões
    • 1
  • Tiago Baptista
    • 1
  • Ernesto Costa
    • 1
  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal

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