In a genetic algorithm, a population of strings (called chromosomes), which encode candidate solutions (called individuals or creatures) to an optimization problem, is evolved toward better solutions. Better solution is usually attained in form of succeeding generations. These generations are evolved using techniques inspired from the real world viz. , selections, mutations and crossovers. Till date, selections were performed using the most basic technique called natural selection, in which, the fitter population has a better chance of getting selected to become the parents of the successive generations. This 'natural selection' can be directly mapped to naturalist Charles Darwin's theory of survival of the fittest. However, Darwin also mentioned another method of nature's selection process called the 'Artificial Selection'. This paper is indented towards mapping the methodology of artificial selection in Artificial Intelligence to develop a new algorithm that aims at the same goal of survival. In this method, only the required traits or combination of traits of the population will be carried forward. This is done by introducing a factor which decides which traits are needed and which are not. This factor can be implicitly included in the program or can be explicitly decided by the user input. This method differs from the existing natural selection in the way that it does not depend totally on fitness of the generations but seeks mainly the required traits or combination of traits needed to solve the problem, and also that the generations evolve according to the factor which can sometimes be user itself who decides which traits he wants to be prominent and which are to be discarded. As a result, the successive generations are a product of selective breeding by the factor. Also, the generations can completely be different in terms of traits of their parents.

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Artificial Selection Algorithm Required Traits Lesser Dependency On Fitness Evolution Controlled By A Factor Selective Breeding