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GECCO
2009
Springer

Using behavioral exploration objectives to solve deceptive problems in neuro-evolution

9 years 7 months ago
Using behavioral exploration objectives to solve deceptive problems in neuro-evolution
Encouraging exploration, typically by preserving the diversity within the population, is one of the most common method to improve the behavior of evolutionary algorithms with deceptive fitness functions. Most of the published approaches to stimulate exploration rely on a distance between genotypes or phenotypes; however, such distances are difficult to compute when evolving neural networks due to (1) the algorithmic complexity of graph similarity measures, (2) the competing conventions problem and (3) the complexity of most neural-network encodings. In this paper, we introduce and compare two conceptually simple, yet efficient methods to improve exploration and avoid premature convergence when evolving both the topology and the parameters of neural networks. The two proposed methods, respectively called behavioral novelty and behavioral diversity, are built on multiobjective evolutionary algorithms and on a user-defined distance between behaviors. They can be employed with any genot...
Jean-Baptiste Mouret, Stéphane Doncieux
Added 24 Jul 2010
Updated 24 Jul 2010
Type Conference
Year 2009
Where GECCO
Authors Jean-Baptiste Mouret, Stéphane Doncieux
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