Aleksandr Smirnov
ABSTRACT
The choice of parameter values greatly affects the performance of evolutionary algorithms. Many current parameter tuning approaches require multiple runs of the tuned algorithm, which makes it hard to use them in budget-constrained environments. Recently, an approach to parameter tuning on binary string problems was proposed, which uses machine learning and fitness landscape analysis to transfer knowledge on good parameter choices between similar problem instances. This approach allows using the performance data obtained on simple benchmark problems with different fitness landscape features to quickly choose suitable parameters for a given problem instance.
In this paper, we aim to extend this approach to permutation-based problems by tuning the recently proposed version of the (1 + (λ, λ)) genetic algorithm for permutations-based problems. To do this, we develop a set of fitness landscape features that can be computed for permutations. We collect the algorithm's performance dataset on multiple instances of the W-model benchmark problem layered over the Ham problem for permutations. Finally, we present the preliminary experimental evaluation of the (1 + (λ, λ)) genetic algorithm tuned by the proposed approach.
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Index Terms
- Towards landscape-aware parameter tuning for the (1 + (λ, λ)) genetic algorithm for permutations
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