Cited by 106Evolutionary Algorithms and ApplicationsModular Robots and Swarm IntelligenceReinforcement Learning in Robotics

Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity

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Mouret, Jean-Baptiste, Doncieux, Stephane·2009·2009 IEEE Congress on Evolutionary Computation

Abstract

The bootstrap problem is often recognized as one of the main challenges of evolutionary robotics: if all individuals from the first randomly generated population perform equally poorly, the evolutionary process won't generate any interesting solution. To overcome this lack of fitness gradient, we propose to efficiently explore behaviors until the evolutionary process finds an individual with a non-minimal fitness. To that aim, we introduce an original diversity-preservation mechanism, called behavioral diversity, that relies on a distance between behaviors (instead of genotypes or phenotypes) and multi-objective evolutionary optimization. This approach has been successfully tested and compared to a recently published incremental evolution method (multi-subgoal evolution) on the evolution of a neuro-controller for a light-seeking mobile robot. Results obtained with these two approaches are qualitatively similar although the introduced one is less directed than multi-subgoal evolution.

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Crossref · 10.1109/cec.2009.4983077

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  • The clustering approach aims to preserve diversity and is related to Lexicase and fitness uniform optimization.

    “This approach, which aims to preserve diversity55,56, is related to Lexicase57 in that both approaches consider a set of test cases for scoring an individual, and it is related to fitness uniform optimization58, which also clusters individuals on the basis of their fitness value; however, we sample the clusters on the basis of their score instead of uniformly, as detailed next.”

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Authors
Mouret, J.-B., & Doncieux, S.
Affiliations
Centre National de la Recherche Scientifique (FR)
Venue
2009 IEEE Congress on Evolutionary Computation
Publisher
IEEE
Published
2009-05
Pages
1161-1168
OpenAlex
W2026723619
Cited by
106
References
45
Citations by year
2025: 1 · 2024: 1 · 2023: 3 · 2022: 6 · 2021: 5 · 2020: 7 · 2019: 4 · 2018: 1 · 2017: 4 · 2016: 10 · 2015: 8 · 2014: 12 · 2013: 11 · 2012: 7
FWCI
7.74
Type
conference paper
Language
en
Open access
Licence
other-oa
Locations
  • IEEE Congress on Evolutionary Computation (CEC) (publishedVersion)
  • HAL (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
  • Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Keywords
evolutionary robotics, behavioral diversity, diversity preservation, evolutionary multi-objective optimization

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