Interactive Evolutionary Computation (IEC) is one of the techniques which incorporate a human in optimization processes. It is a technique that replaces the fitness function of Evolutionary Computation with human subjective assessment. Therefore, it is possible to optimize complex problems incorporating human subjectivity, affection, etc. without making a complex mathematical model. However, it has two general problems; one is burden of interactions to the user, and the other is difficulty of global search. This paper proposes two methods that reduce the problems of IEC by combining Genetic Algorithm (GA) and knowledge acquisition from interaction records which is held a decision table form. Simulation experiments are conducted using an artificial problem, in order to verify the efficiency of the proposed methods, and compare them with related work. The experiment showed that proposed methods have better solution search efficiency than the related work.