ASAGA: an adaptive surrogate-assisted genetic algorithm

Genetic algorithms (GAs) used in complex optimization domains usually need to perform a large number of fitness function evaluations in order to get near-optimal solutions. In real world application domains such as the engineering design problems, such evaluations might be extremely expensive computationally. It is therefore common to estimate or approximate the fitness using certain methods. A popular method is to construct a so called surrogate or meta-model to approximate the original fitness function, which can simulate the behavior of the original fitness function but can be evaluated much faster. It is usually difficult to determine which approximate model should be used and/or what the frequency of usage should be. The answer also varies depending on the individual problem. To solve this problem, an adaptive fitness approximation GA (ASAGA) is presented. ASAGA adaptively chooses the appropriate model type; adaptively adjusts the model complexity and the frequency of model usage...

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Liang Shi, Khaled Rasheed
• Keywords: Fitness Function, Fitness Function Evaluations, GECCO 2008, Optimization, Original Fitness Function