Results 141 to 150 of about 1,131,016 (198)

Evolvoid: A genetic algorithm for shaping optimal cellular constructs

Mancini P, Fontana F, Botte E, Magliaro C, Ahluwalia A.   +4 more
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Genetic algorithms for genetic mapping [PDF]

, 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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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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Genetic Algorithms-a Tool for OR? [PDF]

Journal of the Operational Research Society, 1996
Summary: Compared with other metaheuristic techniques such as simulated annealing and tabu search, research into the use of genetic algorithms for the solution of OR problems is still in its infancy. This paper provides an introduction to genetic algorithms and their use in the solution of both classical and practical operational research problems ...
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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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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
Michael Härtfelder, Bernd Freisleben
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Introduction to genetic algorithms

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2007
The Introduction to Genetic Algorithms Tutorial is aimed at GECCO attendees with limited knowledge of genetic algorithms, and will start "at the beginning," describing first a "classical" genetic algorithm in terms of the biological principles on which it is loosely based, then present some of the fundamental results that describe its performance ...
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Genetic algorithms

Proceedings of the international conference on APL '91, 1991
Genetic algorithms, invented by J. H. Holland, emulate biological evolution in the computer and try to build programs that can adapt by themselves to perform a given function. In some sense, they are analogous to neural networks, but there are important differences between them.
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