On the limitations of classical benchmark functions for evaluating robustness of evolutionary algorithms

DOI10.1016/j.amc.2009.10.009zbMath1183.65061OpenAlexW2056475462MaRDI QIDQ846417

Mohammad R. Saadatmand, Masoud Shariat-Panahi, Ali Ahrari, Ali Asghar Atai

Publication date: 9 February 2010

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

Full work available at URL: https://doi.org/10.1016/j.amc.2009.10.009

zbMATH Keywords

numerical examplesevolutionary algorithmsrobust algorithmscovariance matrix adaptation evolution strategyblack box optimizationmultimodal functionselite search processgeneralization of optimization resultssymmetric behavior

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05)

Related Items (2)

Artificial bee colony algorithm combined with grenade explosion method and Cauchy operator for global optimization ⋮ On the utility of randomly generated functions for performance evaluation of evolutionary algorithms

Uses Software

• CMA-ES
• ABC

Cites Work

• Unnamed Item
• Estimating the parameters of Weibull distribution using simulated annealing algorithm
• GEM: A novel evolutionary optimization method with improved neighborhood search
• Test functions with variable attraction regions for global optimization problems
• Scalability of the Bayesian optimization algorithm.
• Recent developments and trends in global optimization
• Enhancing the performance of differential evolution using orthogonal design method
• A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
• Iterated local search for the quadratic assignment problem
• Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems
• Simulation optimisation using a genetic algorithm

This page was built for publication: On the limitations of classical benchmark functions for evaluating robustness of evolutionary algorithms