Evolution based memetic algorithm and its application in software cost estimation

Article type: Research Article

Authors: Mishra, K.K.^{a; *} | Tripathi, Ashish^b | Tiwari, Shailesh^c | Saxena, Nitin^a

Affiliations: [a] Computer Science and Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Allahabad, India | [b] Computer Science and Engineering Department, S P Memorial Institute of Technology, Allahabad, India | [c] Computer Science and Engineering Department, ABES Engineering College, Ghaziabad, India

Correspondence: [*] Corresponding author. K.K. Mishra, Computer Science and Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Allahabad, India. Tel.: +91 7080670706; E-mail: [email protected].

Abstract: A new memetic algorithm named EAMDGA is designed by combining the characteristics of Environmental Adaption Method for Dynamic Environment (EAMD) and Genetic Algorithm (GA). This algorithm is highly efficient and robust in solving the unimodal and multimodal problems. It avoids the problems of getting trapped in local optima and premature convergence. Performance of this algorithm is checked over a group of 24 unimodal and multimodal benchmark functions provided by Black Box Optimization Benchmarking (BBOB-2013). It is found that EAMDGA is superior in performance in comparison to the other algorithms.

Keywords: Evolutionary algorithm, adaptive learning, optimization, genetic algorithm, EAMD, phenotypic structure

DOI: 10.3233/JIFS-16463

Journal: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2485-2498, 2017