期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2011
卷号：2
期号：5
页码：2412-2415
出版社：TechScience Publications
摘要：This Genetic Algorithms (GAs) are a type of optimization algorithms which combine survival of the fittest and a simplified version of Genetic Process .It has as yet not been proved whether machine learning can be considered as a problem apt for applying GAs. Therefore the work explores the use of GAs in Machine learning. A detailed study on the success of GAs in machine learning was carried out by R. D. King, R. Henery, C. Feng, and A. Sutherland [4] but it was limited to comparison. The paper takes the example of Chess to apply GA and proposes a new technique to apply GA to machine learning which can substitute the existing methodologies .The work proposed is shown to be robust and thus making the learning a natural process rather than an algorithmic one. The paper relies on the randomness of GAs and their ability to make the population converge towards the desired point using a fitness function and combines it with the concept of feedback similar to that of neural networks.
