Abstract
The paper describes an evolutionary algorithm for the general nonlinear programming problem using a surrogate model. Surrogate models are used in optimization when model evaluation is expensive. Two surrogate models are implemented, one for the objective function and another for a penalty function based on the constraint violations. The proposed method uses a sequential technique for updating these models. The quality of the surrogate models is determined by their consistency in ranking the population rather than their statistical accuracy. The technique is evaluated on a number of standard test problems.
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Runarsson, T.P. (2004). Constrained Evolutionary Optimization by Approximate Ranking and Surrogate Models. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_41
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DOI: https://doi.org/10.1007/978-3-540-30217-9_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23092-2
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