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

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Kumara Sastry, Cláudio F. Lima, David E. Goldberg
• Keywords: Expensive Fitness Evaluation, Fitness Surrogate, GECCO 2006, Optimization, Surrogate Avoids