The DOI matches its Crossref record; 43 of the 45 references it cites were found in a registry or an index.
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]
Citations
Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Multi-Objective Optimization Using Evolutionary Algorithms
Deb, K., & Kalyanmoy, D. (2001) · Andalas University Repository (Andalas University) · Cited by 14,936
Introduction to Reinforcement Learning
Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855
SPEA2: Improving the strength pareto evolutionary algorithm
Zitzler, E., Laumanns, M., & Thiele, L. (2001) · doi:10.3929/ethz-a-004284029 · Cited by 5,099
Evolving Neural Networks through Augmenting Topologies
Stanley, K. O., & Miikkulainen, R. P. (2002) · Evolutionary Computation · doi:10.1162/106365602320169811 · Cited by 3,269
Evolving artificial neural networks
Yao, X. (1999) · Proceedings of the IEEE · doi:10.1109/5.784219 · Cited by 2,995
Genetic algorithms with sharing for multimodal function optimization
Goldberg, D. E., & Richardson, J. T. (1987) · international conference on Genetic algorithms · Cited by 1,891
GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION
Fonseca, C. M., & Fleming, P. J. (1993) · Cited by 1,828
Research works printed but not linked: 3
- deb, 2001, Multi-objectives Optimization Using Evolutionnary Algorithms
- winkeler, 1998, incremental evolution in genetic programming, Genetic Programming, 403
- walker, 2004, comparing the performance of incremental evolution to direct evolution, 2nd international conference on Autonomous Robots and Agents
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Such, F. P., Madhavan, V., Conti, E., Lehman, J., Stanley, K. O., & Clune, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.06567 · Cited by 846
Mathematical discoveries from program search with large language models
Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J. R., Ellenberg, J. S., Wang, P., Fawzi, O., et al. (2024) · Nature · doi:10.1038/s41586-023-06924-6 · Cited by 544
“This approach, which aims to preserve diversity 55,56 , is related to Lexicase 57 in that both approaches consider a set of test cases for scoring an individual, and it is related to fitness uniform optimization 58 , which also clusters individuals on the basis of their fitness value; however, we…”
BackgroundQuality Diversity: A New Frontier for Evolutionary Computation
Pugh, J. K., Soros, L. B., & Stanley, K. O. (2016) · Frontiers in Robotics and AI · doi:10.3389/frobt.2016.00040 · Cited by 514
Encouraging Behavioral Diversity in Evolutionary Robotics: An Empirical Study
Mouret, J.-B., & Doncieux, S. (2011) · Evolutionary Computation · doi:10.1162/evco_a_00048 · Cited by 263
Self-citationNovelty-Based Multiobjectivization
Mouret, J.-B. (2011) · Studies in computational intelligence · doi:10.1007/978-3-642-18272-3_10 · Cited by 139
Self-citation
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
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