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Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity
Open accessAbstract
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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“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.”
From Mathematical discoveries from program search with large language models (2024) · cites this paper as [55]
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Metadata
- 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
- green
- hal.science
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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