Abstract
One of the main drawbacks of EAs is the CPU time needed due to many expensive fitness function evaluations of candidate solutions. For this reason it is necessary to introduce advanced techniques. This chapter describes techniques including distributed and parallel EAs, hierarchical EAs, asynchronous EAs, advanced mutation operators as well as game strategies and hybridized games. All these techniques aim to increase diversity and speed up the search for optimal solutions.
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Periaux, J., Gonzalez, F., Lee, D. (2015). Advanced Techniques for Evolutionary Algorithms (EAs). In: Evolutionary Optimization and Game Strategies for Advanced Multi-Disciplinary Design. Intelligent Systems, Control and Automation: Science and Engineering, vol 75. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9520-3_4
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DOI: https://doi.org/10.1007/978-94-017-9520-3_4
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