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Empirical Evaluation of Hill Climbing Algorithm

Empirical Evaluation of Hill Climbing Algorithm

Manju Khari, Prabhat Kumar
Copyright: © 2017 |Volume: 8 |Issue: 4 |Pages: 14
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522513247|DOI: 10.4018/IJAMC.2017100102
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MLA

Khari, Manju, and Prabhat Kumar. "Empirical Evaluation of Hill Climbing Algorithm." IJAMC vol.8, no.4 2017: pp.27-40. http://doi.org/10.4018/IJAMC.2017100102

APA

Khari, M. & Kumar, P. (2017). Empirical Evaluation of Hill Climbing Algorithm. International Journal of Applied Metaheuristic Computing (IJAMC), 8(4), 27-40. http://doi.org/10.4018/IJAMC.2017100102

Chicago

Khari, Manju, and Prabhat Kumar. "Empirical Evaluation of Hill Climbing Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC) 8, no.4: 27-40. http://doi.org/10.4018/IJAMC.2017100102

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Abstract

The software is growing in size and complexity every day due to which strong need is felt by the research community to search for the techniques which can optimize test cases effectively. The current study is inspired by the collective behavior of finding paths from the colony of food and uses different versions of Hill Climbing Algorithm (HCA) such as Stochastic, and Steepest Ascent HCA for the purpose of finding a good optimal solution. The performance of the proposed algorithm is verified on the basis of three parameters comprising of optimized test cases, time is taken during the optimization process, and the percentage of optimization achieved. The results suggest that proposed Stochastic HCA is significantly average percentage better than Steepest Ascent HCA in reducing the number of test cases in order to accomplish the optimization target.

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