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GECCO
2006
Springer

Evaluation relaxation using substructural information and linear estimation

11 years 5 months ago
Evaluation relaxation using substructural information and linear estimation
The paper presents an evaluation-relaxation scheme where a fitness surrogate automatically adapts to the problem structure and the partial contributions of subsolutions to the fitness of an individual are estimated efficiently and accurately. In particular, the probabilistic model built by extended compact genetic algorithm is used to infer the structural form of the surrogate and a least squares method is used to estimate the coefficients of the surrogate. Using the surrogate avoids the need for expensive fitness evaluation for some of the solutions, and thereby yields significant efficiency enhancement. Results show that a surrogate, which automatically adapts to problem knowledge mined from probabilistic
Kumara Sastry, Cláudio F. Lima, David E. Go
Added 23 Aug 2010
Updated 23 Aug 2010
Type Conference
Year 2006
Where GECCO
Authors Kumara Sastry, Cláudio F. Lima, David E. Goldberg
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