Reduced computation for evolutionary optimization in noisy environment

Evolutionary Algorithms’ (EAs’) application to real world optimization problems often involves expensive fitness function evaluation. Naturally this has a crippling effect on the performance of any population based search technique such as EA. Estimating the fitness of individuals instead of actually evaluating them is a workable approach to deal with this situation. Optimization problems in real world often involve expensive fitness. In [14] and [15] we presented two EA models, namely DAFHEA (Dynamic Approximate Fitness based Hybrid Evolutionary Algorithm) and DAFHEA-II respectively. The original DAFHEA framework [14] reduces computation time by controlled use of meta-models generated by Support Vector Machine regression to partly replace actual fitness evaluation by estimation. DAFHEA-II [15] is an enhancement to the original framework in that it can be applied to problems that involve uncertainty. DAFHEA-II, incorporates a multiple-model based learning approach for the support ...

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Maumita Bhattacharya
• Keywords: Expensive Fitness, Expensive Fitness Function, GECCO 2008, Optimization, Support Vector Machine