Environmental adaption method for dynamic environment

Author

Tripathi, Anand ; Garbyal, Prateek ; Mishra, K.K. ; Misra, A.K.

Author_Institution

Comput. Sci. & Eng. Dept., Motilal Nehru Nat. Inst. of Technol. Allahabad, Allahabad, India

fYear

2014

fDate

5-8 Oct. 2014

Firstpage

216

Lastpage

221

Abstract

An Environmental adaption Method (EAM) has been established earlier [2]. In this paper an Environmental Adaption Method for Dynamic Environment (EAMD) has been proposed, which has been specially designed with real valued parameters in dynamic environment. It simulates an environment which gradually becomes more deadly for its inhabitants and only the individuals who are able to adapt to this changing environment will survive and improve their fitness over time. This change in the environment causes the solutions to converge towards the optimal solutions. EAMD is compared with two cellular genetic algorithms (grid16, grid100), a single population genetic algorithm (ga100) and a hill climber on the Black Box Optimization test-bed at dimensions 2D and 10D on a set of 24 benchmark functions. The proposed algorithm gives better results than the existing algorithms.

Keywords

environmental factors; genetic algorithms; EAMD; black box optimization; cellular genetic algorithm; dynamic environment; environmental adaption method; hill climber; single population genetic algorithm; Benchmark testing; Genetic algorithms; Heuristic algorithms; Optimization; Radiation detectors; Sociology; Statistics; EAMD; adaption algorithm; adaptive learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on

Conference_Location

San Diego, CA

Type

conf

DOI

10.1109/SMC.2014.6973910

Filename

6973910