A Genetically based Evolutionary Computing Technique based on Cellular Automata

Evolutionary Computing techniques use an explicit fitness function or simulated to derive a solution to a problem from a population of individuals, over a number of generations. The general approach which allows such techniques to be used on problems in which evaluations are so costly, which cannot be expressed formally, or which are difficult to simulate, is examined [1]. Much work has been done on combining different evolutionary computing techniques particularly the genetic algorithm and neural networks. Our motivation here is to discuss how cellular automata techniques can be involved on evolutionary computation. A study of cellular automata based evolutionary computation in genetic analysis is an inherent problem. But the key problems of genetic analysis are very sensitive in the detection of fitness cells. Evolutionary algorithms use crossover operation to combine information from pairs of solutions and selection operation to retain the best solution. Here, we consider an interactive step so as to get a maximum amount of information that can be shared for the best evaluation of individual fitness cell [7].