Results 151 to 160 of about 543,996 (206)

A novel hybrid model for species distribution prediction using probabilistic random forest, principal component analysis and genetic algorithm. [PDF]

PLoS One
Adekunle TA, Ogundoyin IK, Akanbi CO.
europepmc   +1 more source

Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm. [PDF]

Sensors (Basel)
Zhao Y, Zheng G, Wang J, Liu J, Dong S, Xin J.   +5 more
europepmc   +1 more source

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Genetic clustering algorithms

European Journal of Operational Research, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chiou, Yu-Chiun, Lan, Lawrence W.
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Nature's algorithms [genetic algorithms]

IEEE Potentials, 2001
Combinatorial optimization problems typically require every possible solution to be evaluated to ensure finding the optimal solution. Since such exhaustive searches are often impractical, there is now a vast body of heuristic algorithms for them. Among the algorithms are those based on metaphors borrowed from other areas of science.
J. Carnahan, R. Sinha
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Genetic algorithms

Proceedings of the CUBE International Information Technology Conference, 2012
This paper discusses a case study of grammar induction. Grammar induction is the process of learning grammar from a set of training data of the positive (S+) and negative (S-) strings. An algorithm has been designed and implemented for the induction of context free grammar (CFG).
Hari Mohan Pandey, Anurag Dixit, Deepti Mehrotra   +2 more
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Genetic Algorithm Against Cancer

2006
We present an evolutionary approach to the search for effective vaccination schedules using mathematical computerized model as a fitness evaluator. Our study is based on our previous model that simulates the Cancer – Immune System competition activated by a tumor vaccine. The model reproduces pre-clinical results obtained for an immunoprevention cancer
PAPPALARDO, FRANCESCO, Mastriani E, Lollini PL, Motta S.   +3 more
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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
Bernd Freisleben, Michael Härtfelder
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Genetic algorithms

ACM SIGAPL APL Quote Quad, 1996
This paper describes an application oriented approach to the genetic-algorithm technique. The software is implemented in APL2 and exploits availability of user defined operators and separation of general purpose and problem specific code. The paper presents results of applying genetic algorithms to solve real life problems of simulation and predicting ...
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