Costas Voglis
ABSTRACT
Memetic algorithms are hybrid schemes that usually integrate metaheuristics with classical local search techniques, in order to attain more balanced intensification/diversification trade--off in the search procedure. MEMPSODE is a recently published software that implements such memetic schemes, based on the Particle Swarm Optimization and Differential Evolution algorithms, as well as on the Merlin optimization environment that offers a variety of local search methods. The present study aims at investigating the impact of the selected local search algorithm in the memetic schemes produced by MEMPSODE. Our interest was focused on gradient--free local search methods. We applied the derived memetic schemes on the noiseless testbed of the Black--Box Optimization Benchmarking 2012 workshop. The obtained results can offer significant insight to optimization practitioners with respect to the most promising approaches.
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Index Terms
- MEMPSODE: an empirical assessment of local search algorithm impact on a memetic algorithm using noiseless testbed
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