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A Performance Study of Chemo-Inspired Genetic Algorithm on Benchmark Functions

  • Kedar Nath Das
  • Rajashree Mishra
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 202)

Abstract

In solving non-linear optimization problems, Bacterial Foraging Optimization (BFO) is a novel heuristic algorithm inspired from foraging behavior of E. Coli bacterium. In the other hand, Genetic algorithm (GA) has attracted increased attention from the academic and industrial communities to deal with such problems. In recent literature, it is discovered that the hybrid techniques provides the better solution with faster convergence. In this paper, a novel approach of hybridization is presented. The Chemotactic step (from BFO) is only hybridized with GA, namely CGA. The better performance of the proposed CGA than Quadratic Approximation hybridized GA, is experimentally verified through a set of 22 benchmark problems taken from recent literature.

Keywords

Genetic algorithm Quadratic approximation Bacterial foraging optimization Hybridization Benchmark problems 

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Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.NIT SilcharSilcharIndia
  2. 2.KIIT UniversityBhubaneswarIndia

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