Plateaus can be harder in multi-objective optimization

DOI10.1016/j.tcs.2009.06.020zbMath1191.68827OpenAlexW2138894958MaRDI QIDQ847662

Tobias Friedrich, Frank Neumann, Nils Hebbinghaus

Publication date: 19 February 2010

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2009.06.020

zbMATH Keywords

multi-objective optimizationevolutionary algorithmsruntime analysis

Mathematics Subject Classification ID

Nonnumerical algorithms (68W05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization ⋮ Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front ⋮ Convergence of set-based multi-objective optimization, indicators and deteriorative cycles

Uses Software

• SPEA2

Cites Work

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• Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
• Comparison of simple diversity mechanisms on plateau functions
• Random orders
• On the analysis of the \((1+1)\) evolutionary algorithm
• Helper-objectives: Using multi-objective evolutionary algorithms for single-objective optimisation
• Expected runtimes of evolutionary algorithms for the Eulerian cycle problem
• Multicriteria Optimization
• STACS 2005

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