Working principles, behavior, and performance of MOEAs on MNK-landscapes

DOI10.1016/j.ejor.2006.08.004zbMath1123.90063OpenAlexW1991817987MaRDI QIDQ877672

Kiyoshi Tanaka, Hernán E. Aguirre

Publication date: 3 May 2007

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2006.08.004

zbMATH Keywords

epistasisselectionrecombinationdriftevolutionary computationsmutationmultiobjective combinatorial optimizationmultiobjective evolutionary algorithmsdiscrete binary search spacesMNK-landscapesnon-linear multiobjective fitness functions

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Combinatorial optimization (90C27)

Related Items (8)

Distributed localized bi-objective search ⋮ Multiobjective optimization: when objectives exhibit non-uniform latencies ⋮ What if we increase the number of objectives? Theoretical and empirical implications for many-objective combinatorial optimization ⋮ Decision/objective space trajectory networks for multi-objective combinatorial optimisation ⋮ Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems ⋮ Deep reservoir computing ⋮ Evolutionary multiobjective optimization including practically desirable solutions ⋮ Random walk's correlation function for multi-objective NK landscapes and quadratic assignment problem

Uses Software

• SPEA2

Cites Work

• Evolutionary Multi-Criterion Optimization
• Evolutionary Multi-Criterion Optimization
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