Analysis of Lamarckian Evolution in Morphologically Evolving Robots

Milan Jelisavcic, Rafael Kiesel, Kyrre Glette, Evert Haasdijk, A.E. Eiben

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Evolving robot morphologies implies the need for lifetime learning so that newborn robots can learn to manipulate their bodies. An individual’s morphology will obviously combine traits of all its parents; it must adapt its own controller to suit its morphology, and cannot rely on the controller of any one parent to perform well without adaptation. This paper investigates the practicability and benefits of Lamarckian evolution in this setting. Implementing lifetime learning by means of on-line evolution, we first establish the suitability of an indirect encoding scheme that combines Compositional Pattern Producing Networks (CPPNs) and Central Pattern Generators (CPGs) as a relevant learner and controller for open-loop gait controllers. We then analyze a Lamarckian set-up and the effect of the parental genetic material on the early convergence to good locomotion performance.
Original languageEnglish
Title of host publicationProceedings of the European Conference on Artificial Life 2017, ECAL 2017
PublisherMIT Press
Pages214-221
Number of pages8
Volume14
DOIs
Publication statusPublished - Sep 2017
EventECAL - Lyon, France
Duration: 4 Sep 20178 Sep 2017
https://project.inria.fr/ecal2017/

Conference

ConferenceECAL
CountryFrance
CityLyon
Period4/09/178/09/17
Internet address

Keywords

  • Artificial life
  • Evolutionary robotics
  • Online algorithms
  • Indirect encoding

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  • Cite this

    Jelisavcic, M., Kiesel, R., Glette, K., Haasdijk, E., & Eiben, A. E. (2017). Analysis of Lamarckian Evolution in Morphologically Evolving Robots. In Proceedings of the European Conference on Artificial Life 2017, ECAL 2017 (Vol. 14, pp. 214-221). MIT Press. https://doi.org/10.7551/ecal_a_038