Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms.

DOI10.1016/S0888-613X(02)00090-7zbMath1056.68114MaRDI QIDQ1399506

Dirk Thierens, Peter A. N. Bosman

Publication date: 30 July 2003

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

zbMATH Keywords

combinatorial optimizationnumerical optimizationMulti-objective evolutionary algorithmsprobabilistic model learning

Mathematics Subject Classification ID

Nonnumerical algorithms (68W05) Learning and adaptive systems in artificial intelligence (68T05) Combinatorial optimization (90C27)

Related Items (9)

Iterated local search for the vehicle routing problem with a private fleet and a common carrier ⋮ An incremental-learning model-based multiobjective estimation of distribution algorithm ⋮ Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms. ⋮ Learning Structure Illuminates Black Boxes – An Introduction to Estimation of Distribution Algorithms ⋮ A hybrid multiobjective evolutionary algorithm: Striking a balance with local search ⋮ Evolutionary algorithms for solving multi-objective travelling salesman problem ⋮ A Review of Estimation of Distribution Algorithms and Markov Networks ⋮ MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm ⋮ A novel evolution strategy for multiobjective optimization problem

Uses Software

• MIM

Cites Work

• Estimating the dimension of a model
• Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms.
• A survey and annotated bibliography of multiobjective combinatorial optimization
• Printer graphics for clustering
• Approximating discrete probability distributions with dependence trees
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