Author(s): Abhishek Shetye , Rushabh Tike , Varun K. V. , Manish Wadkar
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Volume. 6 - Issue. 03 , March - 2017
Optimization problem is a problem that is often encountered in everyday life. It is inseparable from human nature that wants to get maximum profit and minimum losses. Genetic Algorithm is a meta heuristic search technique based on the mechanism of natural selection, genetics and evolution. Genetic Algorithms (GA) is an optimization technique for searching very large spaces that models the role of genetic material in living organisms. A small population of individual exemplars can effectively search a large space because they contain schemata, useful substructures that can be potentially combined to make fitter individuals. Formal study of competing schemata shows that the best policy for replicating them is to increase them exponentially according to their relative fitness. This turns out to be policy used by genetic algorithms. Fitness is determined by examining a large number of individual fitness cases. This process can be very efficient if the fitness cases also evolve by their own GAs. Network Models such as multi-layered perceptions, make local changes and so find local minima. To find a global minima genetic algorithms are used. Genetic Algorithm is merely a stochastic, discrete event and a non-linear process. The obtained optima is an end product containing the best product containing the best elements of previous generations where the attributes of a stronger individual tend to be carried forward into the following generation. The rule states, Survival of fittest will win.
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