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Monte Carlo Simulation on Adiabatic Ensembles and a Genetic Algorithm. [PDF]

Entropy (Basel)
Silva Fernandes FMS.
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Application and optimization of adaptive genetic algorithm in fencing training load prediction: a data visualization-based analytical approach. [PDF]

Sci Rep
Jia YN.
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The Proportional Genetic Algorithm: Gene Expression in a Genetic Algorithm

Genetic Programming and Evolvable Machines, 2002
Summary: We introduce a genetic algorithm (GA) with a new representation method which we call the proportional GA (PGA). The PGA is a multi-character GA that relies on the existence or non-existence of genes to determine the information that is expressed.
Annie S. Wu, Ivan I. Garibay
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Isomorphisms of genetic algorithms

Artificial Intelligence, 1991
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
David L. Battle, Michael D. Vose
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Putting More Genetics into Genetic Algorithms

Evolutionary Computation, 1998
The 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 ...
Donald S. Burke, Kenneth A. De Jong, John J. Grefenstette, Connie Loggia Ramsey, Annie S. Wu   +4 more
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A Genetic Engineering Approach to Genetic Algorithms

Evolutionary Computation, 2001
We 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 A. Kazakov
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Genetic algorithms for genetic mapping

1998
Constructing genetic maps is a prerequisite for most in-depth genetic studies of an organism. The problem of constructing reliable genetic maps for any organism can be considered as a complex optimization problem with both discrete and continuous parameters. This paper shows how genetic algorithms can been used to tackle this problem on simple pedigree.
Gaspin, Christine, Schiex, Thomas
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Genetic algorithms in chemistry

Journal of Chromatography A, 2007
Genetic algorithms (GAs) are a quite recent technique of optimization, whose basic concept is mimicking the evolution of a species, according to the Darwinian theory of the "survival of the fittest." The application of genetic algorithms to complex problems usually produces much better results than those obtained by the standard techniques.
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Affinity genetic algorithm

Journal of Heuristics, 2007
Based on some phenomena from human society and nature, we propose a binary affinity genetic algorithm (aGA) by adopting the following strategies: the population is adaptively updated to avoid stagnation; the newly generated individuals will be ensured to survive for some generations in order for them to have time to show their good genes; new ...
Xinchao Zhao, Xiao-Shan Gao
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Optimization of Genetic Algorithms by Genetic Algorithms

1993
This paper presents an approach to determine the optimal Genetic Algorithm (GA), i.e. the most preferable type of genetic operators and their parameter settings, for a given problem. The basic idea is to consider the search for the best GA as an optimization problem and use another GA to solve it. As a consequence, a primary GA operates on a population
Bernd Freisleben, Michael Härtfelder
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