DocumentCode
2822780
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
fYear
2012
fDate
10-15 June 2012
Firstpage
1
Lastpage
8
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/CEC.2012.6256573
Filename
6256573
Link To Document