The two major groups representing biologically inspired algorithms are swarm intelligence (SI) and evolutionary computation (EC). Both SI and EC share common features such as the use of stochastic components during th...
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The two major groups representing biologically inspired algorithms are swarm intelligence (SI) and evolutionary computation (EC). Both SI and EC share common features such as the use of stochastic components during the optimisation process and various parameters for configuration. The setup of parameters in swarm and in evolutionary algorithms has an important role in defining their behaviour, guiding the search and biasing the quality of final solutions. In addition, an appropriate setting for the parameters may change during the optimisation process making this task even harder. The present work brings an up-to-date discussion focusing on online parameter control strategies applied in SI and EC. Also, this review analyses and points out the key techniques and algorithms used and suggests some directions for future research.
The heavy-tailed mutation operator proposed in Doerr et al. (GECCO 2017), called fast mutation to agree with the previously used language, so far was successfully used only in purely mutation-based algorithms. There, ...
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ISBN:
(纸本)9781450371285
The heavy-tailed mutation operator proposed in Doerr et al. (GECCO 2017), called fast mutation to agree with the previously used language, so far was successfully used only in purely mutation-based algorithms. There, it can relieve the algorithm designer from finding the optimal mutation rate and nevertheless obtain a performance close to the one that the optimal mutation rate gives. In this first runtime analysis of a crossover-based algorithm using a heavy-tailed choice of the mutation rate, we show an even stronger impact. With a heavy-tailed mutation rate, the runtime of the (1 + (lambda, lambda)) genetic algorithm on the ONEMAX benchmark function becomes linear in the problem size. This is asymptotically faster than with any static mutation rate and is the same asymptotic runtime that can be obtained with a self-adjusting choice of the mutation rate. This result is complemented by an empirical study which shows the effectiveness of the fast mutation also on random MAX-3SAT instances.
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