Evolution-ary Computation 1: Basic Algorithms and Operators

By Thomas Baeck, D. Fogel, Zbigniew Michalewicz. 2000

843 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

NSGA and SPEA Applied to Multiobjective Design of Power Distribution Systems (2006, background): “, applying these concepts to the solutions of a problem, assigning fitness to each one of the possible solutions [8].”
Metaheuristics (2016, background): “References [4, 5] These two articles can be considered as the founders of the discipline, even if the name “tabu search” and certain ideas already existed previously.”
Fundamentals of natural computing: an overview (2007, background): “Evolutionary computing, also called evolutionary computation, is the field of research that draws ideas from evolutionary biology in order to develop search and optimization techniques for solving complex problems [14,15,102,118, 142,194,196].”
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients (2019, background): “Given this large, combinatorial search space, traditional approaches to symbolic regression typically utilize evolutionary algorithms, especially genetic programming (GP) (Koza, 1992; Schmidt & Lipson, 2009; Bäck et al., 2018).”
Evolutionary algorithms for the optimization of advective control of contaminated aquifer zones (2004, background): “[19] Practical hints for the design of so-called competent (S)GAs that are perfectly adapted to a problem are proposed by several authors [e.g., Goldberg, 1998; Bäck et al., 2000; Reed et al., 2000; van Dijk et al., 2002].”
A Survey of Evolutionary Algorithms for Clustering (2009, methodology): “This description suggests that Scheunders [121] uses a variant of the so-called deterministic tournament selection [6].”