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Each individual represents a solution in search space for given problem. The population of individuals are maintained within search space. Thus each successive generation is more suited for their environment.Genes from “fittest” parent propagate throughout the generation, that is sometimes parents create offspring which is better than either parent.
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Those individuals who are successful (fittest) then mate to create more offspring than others.Individual in population compete for resources and mate.Following is the foundation of GAs based on this analogy – Genetic algorithms are based on an analogy with genetic structure and behavior of chromosomes of the population. This string is analogous to the Chromosome. Each individual is represented as a string of character/integer/float/bits. Each generation consist of a population of individuals and each individual represents a point in search space and possible solution. In simple words, they simulate “survival of the fittest” among individual of consecutive generation for solving a problem. Genetic algorithms simulate the process of natural selection which means those species who can adapt to changes in their environment are able to survive and reproduce and go to next generation. They are commonly used to generate high-quality solutions for optimization problems and search problems. These are intelligent exploitation of random search provided with historical data to direct the search into the region of better performance in solution space. Genetic algorithms are based on the ideas of natural selection and genetics. Genetic Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Top 10 Projects For Beginners To Practice HTML and CSS Skills.Must Do Coding Questions for Product Based Companies.
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