Expensive optimization, uncertain environment: an EA-based solution

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Expensive optimization, uncertain environment: an EA-based solution
Real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function evaluations. Use of any population based iterative technique such as evolutionary algorithm in such problem domains is thus practically prohibitive. A feasible alternative is to build surrogates or use an approximation of the actual fitness functions to be evaluated. Naturally these surrogate or meta models are order of magnitude cheaper to evaluate compared to the actual function evaluation. This paper presents two evolutionary algorithm frameworks which involve surrogate based fitness function evaluation. The first framework, namely the Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model [1] reduces computation time by controlled use of meta-models (in this case approximate model generated by Support Vector Machine regression) to partially replace the actual function evaluation by approximate function...
Maumita Bhattacharya
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Authors Maumita Bhattacharya
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