Parameter Setting in Evolutionary Algorithms

By Fernando Graça Lobo, C.F.P. Lima, Z. Michalewicz. Studies in Computational Intelligence, 2007

374 citations recorded by the source corpus.

One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/3dc1f73975ceb3395d4b1316cd2b1d0e704dafe0

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Citations in the corpus

From evolutionary computation to the evolution of things (2015, methodology): “Techniques such as automated parameter tuning 64,65,66,67 and adaptive parameter control continue to make advances in this area 68,69,70,71 .”
Parameter tuning for configuring and analyzing evolutionary algorithms (2011, background): “To structure our discussion we identify two main factors that determine algorithm quality: performance and robustness.”
A Comprehensive Review of Swarm Optimization Algorithms (2015, neutral): “al [122] investigated parameter setting in Swarm Optimization Algorithms: A Survey”
Whole Test Suite Generation (2013, background): “, see the recent [3]) and adaptive parameter control [32].”