Due to the scale and computational complexity of current simulation codes, surrogate models have become indispensable tools for exploring and understanding the design space. Consequently, there is great interest in techniques that facilitate the construction and evaluation of such approximation models while minimizing the computational cost and maximizing surrogate model accuracy. Many surrogate model types exist (polynomials, Kriging models, RBF models,...) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. The same is true for setting the surrogate model parameters (Bias- Variance trade-off). Traditionally, the solution to both problems has been a pragmatic one, guided by intuition, prior experience or simply available software packages. This thesis presents a more solid approach to these problems. It describes an adaptive surrogate modeling environment, driven by speciated evolution, to automatically determine the optimal model type and complexity. Its performance is illustrated on a number of benchmark problems. Copyright Java and Jini are registered trademarks of Sun Microsystems in the United States and other countries. Linux is a trademark of Linus Torvalds and Matlab of The Mathworks Inc. All other trademarks are the property

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 according to time spent and model accuracy. ASAGA also introduces a stochastic penalty function method to handle constraints. Experiments show that ASAGA outperforms non-adaptive surrogate-assisted GAs with statistical significance.

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