A Comparative Study To Evolutionary Algorithms (original) (raw)
2014, ECMS 2014 Proceedings edited by: Flaminio Squazzoni, Fabio Baronio, Claudia Archetti, Marco Castellani
AI-generated Abstract
This paper provides a comparative study of several evolutionary algorithms, specifically Genetic Algorithms (GA), Simulated Annealing (SA), and Differential Evolution (DE), focusing on their application in solving optimization problems such as the Travelling Salesman Problem. Through experimental analysis, varying algorithm parameters are evaluated, including population size, mutation constants, and generation counts, to assess their impact on algorithm performance. Key findings indicate distinct advantages and limitations of each algorithm in terms of efficiency and effectiveness for optimization tasks.
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