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Genetic algorithms and evolution
Journal of Theoretical Biology, 1990The genetic algorithm (GA) as developed by Holland (1975, Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press) is an optimization technique based on natural selection. We use a modified version of this technique to investigate which aspects of natural selection make it an efficient search procedure.
Alasdair I. Houston +4 more
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Genetic algorithms in chemometrics
Journal of Chemometrics, 2012This review covers the application of Genetic Algorithms (GAs) in Chemometrics. The first applications of GAs in chemistry date back to the 1970s, and in the last decades, they have been more and more frequently used to solve different kinds of problems, for example, when the objective functions do not possess properties such as continuity ...
A. Niazi, LEARDI, RICCARDO
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An alternative Genetic Algorithm
2006This paper presents a new Genetic Algorithm (GA), called Alternative Genetic Algorithm (AGA) which has been defined to facilitate theoretical investigations. We have shown that both AGA and the usual GA (UGA) obey similar difference equations. However, theoretical investigations on the AGA are much simpler than on the UGA. For the AGA, we can derive as
Hesser, Jürgen, Männer, Reinhard
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Putting More Genetics into Genetic Algorithms
Evolutionary Computation, 1998The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented ...
John J. Grefenstette +4 more
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2014
Genetic algorithms are among the most popular evolutionary algorithms in terms of the diversity of their applications. A vast majority of well-known optimization problems have been solved using genetic algorithms. In addition, genetic algorithms are population-based, and many modern evolutionary algorithms are directly based on genetic algorithms or ...
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Genetic algorithms are among the most popular evolutionary algorithms in terms of the diversity of their applications. A vast majority of well-known optimization problems have been solved using genetic algorithms. In addition, genetic algorithms are population-based, and many modern evolutionary algorithms are directly based on genetic algorithms or ...
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A Genetic Engineering Approach to Genetic Algorithms
Evolutionary Computation, 2001We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful ...
John S. Gero, Vladimir S. Kazakov
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On coevolutionary genetic algorithms
Soft Computing, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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An introduction to genetic algorithms
Sadhana, 1999Genetic algorithms (GAs) are search and optimization tools, which work differently compared to classical search and optimization methods. Because of their broad applicability, ease of use, and global perspective, GAs have been increasingly applied to various search and optimization problems in the recent past.
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Clinical management of metastatic colorectal cancer in the era of precision medicine
Ca-A Cancer Journal for Clinicians, 2022Davide Ciardiello +2 more
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