Parameter Control in Evolutionary Algorithms

By Agoston E. Eiben, Z. Michalewicz, Marc Schoenauer, James Edward Smith. 2007

2,017 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

A Survey of Automatic Parameter Tuning Methods for Metaheuristics (2020, methodology): “In this case, the results of parameter tuning, i.e., the optimal parameter setting founded by tuning process, is used in solving problems and these parameter values remain unchanged during the run [18].”
Surrogate-Assisted Learning of Neural Networks (2017, background): “No algorithm is able to deliver their best performance for every problem without adapting their control parameters; By parameter tuning [2, 6], we can exploit beneﬁcial parameter settings, but it is very time-demanding.”
A Multiobjective Genetic Algorithm to Find Communities in Complex Networks (2012, background): “It is known that setting parameter values is a challenging research problem in evolutionary algorithms [16].”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, methodology): “For example, Van den Bergh and Engelbrecht (2004) divided the problem into d subproblems (each dimension was considered as one subproblem) and SPSO was used to deal with each subproblem. However, this choice causes the algorithm to have the local convergence and transformation variance issues. To ad”
Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, background): “Usually in the community of stochastic search algorithms, robust search is weighted over the highest possible convergence rate [56], [57].”
Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems (2006, background): “Later, it was realized that in order to achieve optimal convergence, these parameters should be altered in the evolution process itself [5], [7].”
From evolutionary computation to the evolution of things (2015, methodology): “These can customize an initial algorithm setup for a given problem off-line (before the run), or on-line (during the run) 63 .”
Hybrid Genetic Algorithms: A Review (2006, methodology): “Hart et al. [122] and Morris et al. [17] applied local search to 6% of the population.”
A comprehensive survey on recent metaheuristics for feature selection (2022, neutral): “Setting the parameters of metaheuristics Controlling the parameters of a metaheuristic algorithm is one of the most important research areas [102].”