Bandit-Based Random Mutation Hill-Climbing [article]

Jialin Liu, Diego Peŕez-Lieb́ana, Simon M. Lucas
<span title="2016-06-20">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The Random Mutation Hill-Climbing algorithm is a direct search technique mostly used in discrete domains. It repeats the process of randomly selecting a neighbour of a best-so-far solution and accepts the neighbour if it is better than or equal to it. In this work, we propose to use a novel method to select the neighbour solution using a set of independent multi- armed bandit-style selection units which results in a bandit-based Random Mutation Hill-Climbing algorithm. The new algorithm
more &raquo; ... antly outperforms Random Mutation Hill-Climbing in both OneMax (in noise-free and noisy cases) and Royal Road problems (in the noise-free case). The algorithm shows particular promise for discrete optimisation problems where each fitness evaluation is expensive.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:1606.06041v1</a> <a target="_blank" rel="external noopener" href="">fatcat:mku75irl4jduzfckpyehnxhw6e</a> </span>
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