GHS + LEM: Global-best harmony search using learnable evolution models

DOI10.1016/j.amc.2011.07.073zbMath1243.65067OpenAlexW2103817605MaRDI QIDQ425444

J. Herrera, H. S. Yoon

Publication date: 8 June 2012

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2011.07.073

zbMATH Keywords

performanceoptimizationprismconvergencenumerical examplesmeta-heuristicsevolutionary algorithmsmachine learningharmony searchlearnable evolution models

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Approximation methods and heuristics in mathematical programming (90C59)

Related Items

On the iterative convergence of harmony search algorithm and a proposed modification, A dynamic self-adaptive harmony search algorithm for continuous optimization problems, Global dynamic harmony search algorithm: GDHS

Uses Software

• CEC 05

Cites Work

• An improved harmony search algorithm for solving optimization problems
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• Solving the sum-of-ratios problems by a harmony search algorithm
• A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 special session on real parameter optimization
• Learnable evolution model: Evolutionary processes guided by machine learning
• Recent advances in harmony search algorithm
• Soft computing applications in industry.
• Global-best harmony search
• A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice
• PRISM: An algorithm for inducing modular rules