Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity

By Jean-Baptiste Mouret, Stéphane Doncieux. 2009

127 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Diversity is All You Need: Learning Skills without a Reward Function (2018, background): “Prior work in neuroevolution and evolutionary algorithms has studied how complex behaviors can be learned by directly maximizing diversity (Lehman & Stanley, 2011a;b; Woolley & Stanley, 2011; Stanley & Miikkulainen, 2002; Such et al., 2017; Pugh et al., 2016; Mouret & Doncieux, 2009).”
On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks (2013, methodology): “This encoding has been previously employed in many papers, with similar parameters [4,50,66–68].”
Mathematical discoveries from program search with large language models (2023, background): “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…”
Novelty-Based Multiobjectivization (2011, methodology): “It had, in particular, been employed to evolve neural networks to compute a Boolean function [18] and to evolve neuro-controllers for a mobile robot [6, 17].”
AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence (2019, background): “haviors and high performance is to optimize for both of them. Many have shown such a combination is effective, whether in a weighted combination of novelty and reward [30], multi-objective algorithms [115, 117], or algorithms that intelligently rebalance between novelty and performance as a function”
Evolvability Search: Directly Selecting for Evolvability in order to Study and Produce It (2016, background): “Similar to other divergent EAs [7,25,26,31], it also increases population diversity and evolves solutions to deceptive problems.”