Publication Type : Conference Proceedings
Publisher : Computational Vision and Bio-Inspired Computing, Springer International Publishing
Source : Computational Vision and Bio-Inspired Computing, Springer International Publishing, Volume 1108, Cham (2020)
Url : https://link.springer.com/chapter/10.1007/978-3-030-37218-7_53
ISBN : 9783030372187
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2020
Abstract : Albeit Evolutionary Algorithms (EAs) are prominent, proven tools for resolution of optimization problems in the real world, appraisal of their appropriateness in solving wide variety of mathematical problems, from simple to complex, continues to be an active research area in the domain of Computer Science. This paper portrays an evaluation of the relevance of Genetic Algorithm (GA) in addressing the Subset Sum Problem (SSP) of Mathematics and providing empirical results with discussions. A GA with pertinent mutation and crossover operators is designed and implemented to solve SSP. Design of the proposed algorithm are clarified in detail. The results obtained by the proposed GA are assessed among different instances with different initial population by the intermediary solutions obtained and the execution time. This study also adapted the traditional Dynamic Programming (DP) approach, pursuing a bottom-up strategy, to solve the SSP. The findings revealed that the GA approach would be unpreferred on account of its longer execution time.
Cite this Research Publication : K. Harsha Saketh and Dr. Jeyakumar G., “Comparison of Dynamic Programming and Genetic Algorithm Approaches for Solving Subset Sum Problems”, Computational Vision and Bio-Inspired Computing, vol. 1108. Springer International Publishing, Cham, 2020.