genetic algorithm

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genetic algorithm

a computer-simulated evolutionary sequence used in cluster analysis.

genetic algorithm

n.
An algorithm that solves a problem using an evolutionary approach by generating mutations to the current solution method, selecting the better methods from this new generation, and then using these improved methods to repeat the process.
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The genetic algorithms are categorized as global search heuristics, the operation model based on biological evaluation such as selection, crossover, and mutation.
During this a general purpose schedule optimizer for manufacturing flow shops planning using genetic algorithms.
In order to avoid result from local optimum scenario by the Back Propagation optimizer, Genetic Algorithm optimizes synaptic weights of the network towards reducing prediction error.
Genetic Algorithms in search Optimization and Machine Learning.
Keywords: Regional science and technology resource, allocation optimization, genetic algorithm
Baklacioglu, "Fuel flow-rate modelling of transport aircraft for the climb flight using genetic algorithms," The Aeronautical Journal, vol.
Medical image registration using parallel genetic algorithms.
Genetic algorithms (GA) are optimization and search techniques based on the natural evolution principles.
Genetic algorithms have very good results with problems with a large set of possible solutions.
Genetic algorithms are search methods for locating the global optimal solution, based upon the idea of simulating the natural selection process in which stronger individuals stay alive in a competing environment (Man et al.
Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index.
The initial values of the uncertain parameters, ballistic coefficient and eccentricity, are estimated with the response surface technique using a genetic algorithm for the four time intervals where a near linear variation of the mean apogee altitude is observed.

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