Title :
DE-TDQL: An adaptive memetic algorithm
Author :
Bhowmik, Pavel ; Rakshit, Pratyusha ; Konar, Amit ; Kim, Eunjin ; Nagar, Atulya K.
Author_Institution :
ETCE Dept., Jadavpur Univ., Kolkata, India
Abstract :
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.
Keywords :
evolutionary computation; learning (artificial intelligence); search problems; DE-TDQL; Q-learning; adaptive memetic algorithm; cultural evolution; differential evolution algorithm; natural evolution; population-based metaheuristic search algorithm; Accuracy; Benchmark testing; Cultural differences; Indexes; Memetics; Silicon; Vectors; differential evolution algorithm; memetic algorithm; self adaptive differential evolution algorithm; temporal difference q- learning;
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
DOI :
10.1109/CEC.2012.6256573
