genetic algorithm


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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.
References in periodicals archive ?
Due the nature of appliances connected to the grid, traditional task (job) scheduling is a time consuming, while the introduction of genetic algorithm to take the task of scheduling will shorten the task scheduling time and the cost and improvement of the grid performance [6].
During this literature survey we tend to found several algorithm are accustomed solve flow shop scheduling problem in manufacturing field by many of the researchers, however we might found that genetic algorithm is very old algorithm however very powerful algorithm to solve flowshop scheduling problem.
Performance results show the adaptive mechanism in Genetic Algorithm helps increase capability to converge sooner than the ordinary Genetic Algorithm.
The solutions for TSP using Genetic Algorithm started in 90's.
Keywords: Regional science and technology resource, allocation optimization, genetic algorithm
Compared with the traditional heuristic optimization search algorithm, the main characteristic of genetic algorithm is the population search strategy and the simple genetic operators.
Predicting Modeling Method of Ship Radiated Noise Based on Genetic Algorithm
In this paper, we introduce the genetic algorithm (GA) as one of these metaheuristics and review some of its applications in medicine.
Keywords: local search, iterative improvement, steepest descent, genetic algorithm, Wilcoxon test, least squares approximation
If there is a specialized algorithm for a particular problem, which uses some knowledge about the problem, it usually surpasses the genetic algorithm both in speed and quality of the solution [1].
2000) also proposed a genetic algorithm based optimization model by minimizing the sum of the absolute deviations between the resource usage and the average resource usage.
In the next section we discuss using a genetic algorithm to learn the weight distribution vector.

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