genetic algorithm


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genetic algorithm

[jə‚ned·ik ′al·gə‚rith·əm]
(computer science)
A search procedure based on the mechanics of natural selection and genetics. Also known as evolutionary strategy.

genetic algorithm

(GA) An evolutionary algorithm which generates each individual from some encoded form known as a "chromosome" or "genome". Chromosomes are combined or mutated to breed new individuals. "Crossover", the kind of recombination of chromosomes found in sexual reproduction in nature, is often also used in GAs. Here, an offspring's chromosome is created by joining segments choosen alternately from each of two parents' chromosomes which are of fixed length.

GAs are useful for multidimensional optimisation problems in which the chromosome can encode the values for the different variables being optimised.

Illinois Genetic Algorithms Laboratory (IlliGAL).
References in periodicals archive ?
The building energy simulation model eQUEST is used to generate the hourly cooling load, the chiller model is used to determine the chiller power, and the genetic algorithm is used to solve the optimization problem.
4 Tabu Search and Genetic Algorithm based weight optimization in neural network
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The parameters values used for the genetic algorithm are mentioned below in table (1).
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In order to apply the fundamental theorem of genetic algorithm, the data structure (string) representing the member of the population is defined because of the following reasons (Conway and Venkataramanan, 1994).
Furtado, Cai1 & Xiaobo at China University of Geosciences [3], performed supervised classification using genetic algorithm and inferred that the use of different number of classes to classify an image is not that effective once we can note that the pixels in some of the images--same image and different classes--were not classified.
Today, genetic algorithms routinely inspire professionals across many industries to find practical solutions to business problems that help to optimize productivity.
Genetic algorithms (or GA for short) are one of these evolution techniques, which mostly imitate principles of natural evolution process.
A genetic algorithm is a search and optimization techniques based on the principle of natural genetics and natural selection.
The designed planning tool consists of the following components: Radio Wave Propagation module, comparison module, Coverage Area Builder, optimizer that essentially interacts with the genetic algorithm module to produce optimal solutions, and, finally, the GIS part, the user interface that allows the user to capture initial locations on the map and also display the results as points on the map.

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