Self-adaptation of mutation rates in non-elitist populationsTools Lehre, Per Kristian and Dang, Duc-Cuong (2016) Self-adaptation of mutation rates in non-elitist populations. In: 14th International Conference on Parallel Problem Solving from Nature, 17-21 Sept 2016, Edinburgh, UK. (In Press) Full text not available from this repository.AbstractThe runtime of evolutionary algorithms (EAs) depends critically on their parameter settings, which are often problem-specific. Automated schemes for parameter tuning have been developed to alleviate the high costs of manual parameter tuning. Experimental results indicate that self-adaptation, where parameter settings are encoded in the genomes of individuals, can be effective in continuous optimisation. However, results in discrete optimisation have been less conclusive. Furthermore, a rigorous runtime analysis that explains how self adaptation can lead to asymptotic speedups has been missing. This paper provides the first such analysis for discrete, population-based EAs. We apply level-based analysis to show how a self-adaptive EA is capable of fine-tuning its mutation rate, leading to exponential speedups over EAs using fixed mutation rates.
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