Abstract | ||
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The influence of non-constant population sizes in evolutionary multi-objective optimization algorithms is investigated. In contrast to evolutionary single-objective optimization algorithms an increasing population size is considered beneficial when approaching the Pareto-front. Firstly, different deterministic schedules are tested, featuring different parameters like the initial population size. Secondly, a simple adaptation method is proposed. Considering all results, an increasing population size during an evolutionary multi-objective optimization algorithm run saves fitness function evaluations compared to a fixed population size. In particular, the results obtained with the adaptive method are most promising. |
Year | DOI | Venue |
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2014 | 10.1007/978-3-319-10762-2_57 | PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XIII |
Field | DocType | Volume |
Population,Mathematical optimization,Adaptive method,Computer science,Evolutionary computation,Multi-objective optimization,Fitness function,Population size,Schedule,Optimization algorithm | Conference | 8672 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.38 |
References | Authors | |
10 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Tobias Glasmachers | 1 | 321 | 47.49 |
Boris Naujoks | 2 | 704 | 47.78 |
Günter Rudolph | 3 | 219 | 48.59 |