What is genetic algorithm?
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M$1) Introduces random mutations in the population.
2) Creates new chromosomes by combining existing ones as in sexual reproduction (called "crossover").
3) Performs natural selection by eliminating those chromosomes that do not satisfy a certain threshold according to a predefined "fitness function".
After a given number of iterations, the fittest chromosome is selected to be the optimized solution.
Master's degree in computer science.
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M$and the other defination which a site called www.examville.com offers is, Genetic Algorithms (GAs) are adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. As such they represent an intelligent exploitation of a random search within a defined search space to solve a problem.
First pioneered by John Holland in the 60s, Genetic Algorithms has been widely studied, experimented and applied in many fields in engineering worlds. Not only does GAs provide an alternative methods to solving problem, it consistently outperforms other traditional methods in most of the problems link. Many of the real world problems involved finding optimal parameters, which might prove difficult for traditional methods but ideal for GAs. However, because of its outstanding performance in optimisation, GAs have been wrongly regarded as a function optimiser. In fact, there are many ways to view genetic algorithms. Perhaps most users come to GAs looking for a problem solver, but this is a restrictive view Jong ,1993 .
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M$
