Analysis of Lamarckian Evolution in Morphologically Evolving Robots

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

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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 publicationECAL 2017, the Fourteenth European Conference on Artificial Life
Subtitle of host publication[Proceedings]
PublisherMIT Press
Number of pages8
Publication statusPublished - 2017
EventECAL - Lyon, France
Duration: 4 Sept 20178 Sept 2017


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


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