Artificial intelligence is a way of making a computer behave 'intelligently'. This can be accomplished by: studying how people think when they are trying to make decisions and solve problems; breaking those thought processes down into basic steps, and finally designing a computer program that solves problems using those same steps. AI thereby provides a simple, structured approach to designing complex decision making programs. The goal of an AI system is to analyse human behaviour in the fields of perception, comprehension and decision making with the ultimate hope of reproducing the behaviour on a machine, namely a computer. One major category of AI techniques is 'genetic algorithm'. Although it is recognised that the performance of an evolutionary system such as GA is affected by the parameters that are employed to implement them, there is hardly any work known to us that has shed much light on the interdependencies and interactions between these parameters. Most studies on the effects of these parameters on performance of GA-based systems have focused on a parameter, at a time, without considering the effect of other parameters on that parameter and vice versa. Consequently, there is hardly any theory about the interactions and interdependencies of these parameters. This paper contributes towards correcting the situation mentioned above by examining empirically the relationship between three parameters of GAs.