Genetic Algorithms + Data Structures = Evolution Programs

By Z. Michalewicz. 1996

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, methodology): “When applied to EPANNs, artiﬁcial evolution is characterized by systems of growing complexity and changing search spaces (Holland, 1975; Michalewicz, 1994).”
TOP-IDP-scale: a new amino acid scale measuring propensity for intrinsic disorder. (2008, methodology): “Specifically, both a Canonical Genetic Algorithm (CGA) [31] and a Stochastic Hill Climbing Algorithm (SHCA) [32] were tested.”
An updated survey of GA-based multiobjective optimization techniques (2000, background): “…importance of each objective. only because of the multiobjective na-The fuzziness of this area lies in the fact ture of most real-world problems but that there is no accepted definition of also because there are still many open optimum as in single-objective optimiquestions in this area.”
Learning Complexity-Bounded Rule-Based Classifiers by Combining. Association Analysis and Genetic Algorithms (2005, methodology): “For the genetic search algorithm, the following settings have been used: Ranking selection of parents, elitist selection for environmental selection, 1-point crossover (probability 0.8), and single point random mutation (probability 0.5) [11].”
A global multilevel coordinate search procedure for estimating the unsaturated soil hydraulic properties (2002, methodology): “Takeshita and Kohno [1999] presented a global method that uses a Genetic algorithm [Michalewicz, 1996] to estimate the saturated hydraulic conductivity Ks and parameters a and n of the van Genuchten retention model.”
A comparative study of Artificial Bee Colony algorithm (2009, background): “It should be noted that a GA using floating point representation could be faster and more consistent from run to run and could provide higher precision for some specific problems [49].”
Parameter Control in Evolutionary Algorithms (2007, background): “[3, 62] in their GA with variable population size (GAVaPS).”
Empirical Investigation of the Benefits of Partial Lamarckianism (1997, methodology): “For each problem instance, the GA was replicated 30 times, with common random seeds across the different search strategies.”
A new crossover operator for real coded genetic algorithms (2007, background): “Michalewicz’s Non-Uniform Mutation is one of the widely used mutation operators in real coded GAs [12,28].”
Comparison of the Luus–Jaakola optimization procedure and the genetic algorithm (2005, methodology): “The GA was introduced by Holland (1975) and Goldberg (1989), and has been modified to solve different kinds of optimization problems (Michalewicz 1996, Coello 2000, Deb 2000, Summanwar et al. 2002).”
Self-Organisation: Paradigms and Applications (2003, unknown): “We can also cite [18][16] which develop similar ideas.”
Dispersed generator placement using fuzzy-GA in distribution systems (2002, methodology): “Genetic algorithm (GA) is very powerful search method based on the mechanics of natural selection and natural genetics, and is different from conventional search algorithms [ 11 ].”