### Abstract

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 DynamicApproximate Fitness based Hybrid EA (DAFHEA) model [1]reduces computation time by controlled use of meta-models (in this case approximate model generated by Support VectorMachine regression) to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the meta-model are generated from a single uniform model.This does not take into account problem domains involving uncertain environment. The second model, DAFHEA-II, an enhanced version of the original DAFHEA framework,incorporates a multiple-model based learning approach for the support vector machine approximator to handle uncertain environment [2]. Empirical evaluation results have been presentedbased on application of the frameworks to commonly used benchmark functions.

Original language | English |
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Title of host publication | GECCO 2007 |

Editors | Dirk Thierens |

Place of Publication | USA |

Publisher | ACM Press |

Pages | 2407-2414 |

Number of pages | 8 |

ISBN (Electronic) | 9781595936981 |

Publication status | Published - 2007 |

Event | Genetic and Evolutionary Computation Conference (GECCO) - London, UK, United Kingdom Duration: 07 Jul 2007 → 11 Jul 2007 |

### Conference

Conference | Genetic and Evolutionary Computation Conference (GECCO) |
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Country | United Kingdom |

Period | 07/07/07 → 11/07/07 |

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## Cite this

Bhattacharya, M. (2007). Expensive Optimization, Uncertain Environment: An EA Solution. In D. Thierens (Ed.),

*GECCO 2007*(pp. 2407-2414). ACM Press.