Adaptive Gene Level Mutation

Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, evaluated according predefined fitness function. These approaches can provide solutions in various tasks even, analytic not be or too complex computed. In this paper we will show, how certain set of problems partially solvable allowing us grade segments individually, which results local and individual tuning mutation parameters for genes. We demonstrate the efficiency our method on N-Queens travelling salesman that approach always faster convergence most cases lower error than traditional approach.