Memetic Algorithm: Hybridization of Hill Climbing with Selection Operator
Rakesh Kumar1, Sanjay Tyagi2, Manju Sharma3
1Rakesh Kumar, DCSA, Kurukshetra University, Kurukshetra, Haryana, India.
2Sanjay Tyagi, DCSA, Kurukshetra University, Kurukshetra, Haryana, India.
3Manju Sharma DCSA, Kurukshetra University, Kurukshetra, Haryana, India.
Manuscript received on April 03, 2013. | Revised Manuscript received on April 29, 2013. | Manuscript published on May 05, 2013. | PP: 140-145 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1465053213/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Genetic Algorithms are the population based search and optimization technique that mimic the process of natural evolution. Premature Convergence and genetic drift are the inherent characteristics of genetic algorithms that make them incapable of finding global optimal solution. A memetic algorithm is an extension of genetic algorithm that incorporates the local search techniques within genetic operations so as to prevent the premature convergence and improve performance in case of NP-hard problems. This paper proposes a new memetic algorithm where hill climbing local search is applied to each individual selected after selection operation. The experiments have been conducted using four different benchmark functions and implementation is carried out using MATLAB. The function’s result shows that the proposed memetic algorithm performs better than the genetic algorithm in terms of producing more optimal results and maintains balance between exploitation and exploration within the search space.
Keywords: Benchmark functions, hybrid genetic algorithms, hill climbing, memetic algorithms.