DE-TDQL: An adaptive memetic algorithm

Memetic algorithms are population-based meta-heuristic search algorithms that combine the composite benefits of natural and cultural evolution. In this paper a synergism of the classical Differential Evolution algorithm and Q-learning is used to construct the memetic algorithm. Computer simulation w...

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Bibliographic Details
Main Authors: Bhowmik, P., Rakshit, P., Konar, A., Eunjin Kim, Nagar, A. K.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Benchmark testing
Cultural differences
differential evolution algorithm
Indexes
memetic algorithm
Memetics
self adaptive differential evolution algorithm
Silicon
temporal difference q- learning
Vectors
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Summary:Memetic algorithms are population-based meta-heuristic search algorithms that combine the composite benefits of natural and cultural evolution. In this paper a synergism of the classical Differential Evolution algorithm and Q-learning is used to construct the memetic algorithm. Computer simulation with standard benchmark functions reveals that the proposed memetic algorithm outperforms three distinct Differential Evolution algorithms.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2012.6256573