Summary
In this article we present and compare two methods for forming and using surrogate models to speed up genetic-algorithm-based optimization. The methods work by forming functional approximations of the fitness function which are used to speed up the GA optimization. One method speeds up the optimization by making the genetic operators more informed. The other method speeds up the optimization by genetically engineering some individuals instead of using the regular Darwinian evolution approach. Empirical results in several engineering design domains are presented.
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Rasheed, K., Ni, X., Vattam, S. (2005). Methods for Using Surrogate Models to Speed Up Genetic Algorithm Optimization: Informed Operators and Genetic Engineering. In: Jin, Y. (eds) Knowledge Incorporation in Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44511-1_6
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DOI: https://doi.org/10.1007/978-3-540-44511-1_6
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