Date of Publication :11th May 2017

Abstract: Knowledge Base in an integral part of any AI system. Knowledge Acquisition refers to purposeful addition or refinements of knowledge structures to a knowledge Base. However knowledge acquisition is a complex and difficult task for AI researchers.Machine learning is the autonomous/mechanical procedure involving computers through which an AI. System itself enriches its knowledge Base.A useful taxonomy for machine learning is one that is based on the behavarioul strategy employed in the learning process. Some important conventional methods depending upon the degree of inference procedure are memorization, Analogy Inductive &Deductive inference.Attempts to develop machine learning systems began in 1950s. These design included self Adaptive system which modified their own structures in an attempt to produce an optional response to some input stimuli.Genetic Algorithms (GA) are based on population genetics. GAs learn through crossovers and mutations to provide optimum results. Higher performing knowledge structures can be mated (cross-over) to give birth to offsprings which possess many of their parent traits. Generations of structures are thus created until an acceptable level of performance has been reached. The following paper presents a study in different aspects of implementation of GAs for knowledge Acquisition in AI systems

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