A-Mazer with Genetic Algorithm

Typically Mazes require sequence of decision to be taken in order to reach to the goal state from the initial state. The maze structure considered in this paper is a random rectangular maze constructed by using the sequence of stochastic decisions taken over iterations to create the same. The paper also focuses to give solution of the rectangular maze by using Genetic Algorithm, an evolutionary heuristic method for finding optimum solution. Genetic algorithms are the heuristics methods from the category of evolutionary algorithms which are based on the Darwin‟s principle of origin of species by means of natural selection [4]. GA‟s are invented by John Holland in 1960‟s [5]. In contrast with Evolution Strategies and Evolutionary Programming, Holland‟s original goal was not to design algorithms to solve specific problems, but rather to formally study the phenomenon of adaptation as it occurs in nature and to develop ways in which the mechanisms of natural adaptation might be utilized into computer systems. Holland‟s 1975 book „Adaptation in Natural and Artificial Systems‟ presented the GA as an abstraction of biological evolution and gave a theoretical framework for adaptation under the GA. Many problems in engineering and related areas require the simultaneous genetic optimization of a number of, possibly competing, objectives have been solve by combining the multiple objectives in to single scalar by some linear combination[6].