The Effect of Initial Population Sampling on the Convergence of Multi-Objective Genetic Algorithms

Silvia Poles (silvia.poles@esteco.com), Yan Fu (yfu4@ford.com) and Enrico Rigoni (enrico.rigoni@esteco.com)
Additional contact information
Silvia Poles: ESTECO, Area Science Park—Padriciano
Yan Fu: Ford Motor Company MD 2115
Enrico Rigoni: ESTECO, Area Science Park—Padriciano

A chapter in Multiobjective Programming and Goal Programming, 2009, pp 123-133 from Springer

Abstract: Abstract This paper aims to demonstrate that the initial population plays an important role in the convergence of genetic algorithms independently from the algorithm and the problem. Using a well-distributed sampling increases the robustness and avoids premature convergence. The observation is proved using MOGA-II and NSGA-II with different sampling methods. This result is particularly important whenever the optimization involves time-consuming functions.

Keywords: Convergence; Initial population; MOGA-II; Multi-objective genetic algorithms; NSGA-II (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-540-85646-7_12

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DOI: 10.1007/978-3-540-85646-7_12

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