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Progressive Minimal Criteria Novelty Search

  • Jorge Gomes
  • Paulo Urbano
  • Anders Lyhne Christensen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7637)

Abstract

We propose progressive minimal criteria novelty search (PMCNS), which is an extension of minimal criteria novelty search. In PMCNS, we combine the respective benefits of novelty search and fitness-based evolution by letting novelty search freely explore new regions of behaviour space as long as the solutions meet a progressively stricter fitness criterion. We evaluate the performance of our approach in the evolution of neurocontrollers for a swarm of robots in a coordination task where robots must share a single charging station. The robots can only survive by periodically recharging their batteries. We compare the performance of PMCNS with (i) minimal criteria novelty search, (ii) pure novelty search, (iii) pure fitness-based evolution, and (iv) with evolutionary search based on a linear blend of novelty and fitness. Our results show that PMCNS outperforms all four approaches. Finally, we analyse how different parameter setting in PMCNS influence the exploration of the behaviour space.

Keywords

novelty search evolutionary swarm robotics deception 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jorge Gomes
    • 1
  • Paulo Urbano
    • 1
  • Anders Lyhne Christensen
    • 2
    • 3
  1. 1.LabMAg, Faculdade de CiênciasUniversidade de LisboaPortugal
  2. 2.Instituto Universitário de Lisboa (ISCTE-IUL)LisboaPortugal
  3. 3.Instituto de TelecomunicaçõesLisboaPortugal

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