Analysis of Genetic Algorithm in Infinite Monkey Problem

Revolutionary enhancements have been made in the field of Artificial Intelligence and Machine Learning. One of the key aspects of the latter is to solve problems of which little is known in advance. Such problems encompasses a very broad search space and it is, more often than not, tedious to design a situation specific algorithm. In such cases, adaptive methods such as Evolutionary algorithms are preferred. One of the most widespread used evolutionary algorithms is Genetic Algorithms. Genetic algorithms mimic the processes involved in natural selection and evolution to repeatedly improve on its solution and arrive at an optimal or near optimal result. Genetic algorithms, or evolutionary algorithms in general, are extensively used to solve many search and optimisation problems specially NP-hard problems where it is computationally infeasible to arrive at an optimal solution but quite often a near optimal solution is sufficient. Other problems which can be solved by genetic algorithms include problems where it is unclear as to how an algorithm must be designed to solve it. Such problems such as the designing character movement in games can be tackled by Genetic algorithms as long as we can define a fitness function to evaluate the effectiveness of our result. In the suggested system, we have aimed to implement Genetic Algorithms to tackle the Infinite Monkey Problem. We have aimed to understand the basic functioning of the simple Genetic Algorithm used and have expected to find a correlation of various parameters in the Genetic Algorithm used such as the Length of the input string, Size of the population, Mutation Rate, Elitism and Scoring pattern with the associated efficiency of the algorithm in arriving at a solution by varying these parameters in a suitable range.