Non-deterministic decoding with memory to enhance precision in binary-coded genetic algorithms

A non-deterministic decoding algorithm for binary coded genetic algorithms is presented. The proposed algorithm enhances the precision of the GA solutions by introducing a Gaussian perturbation to the decoding function. This non-deterministic decoding enables individuals to represent any point in the continuum instead of finite discrete points. As the generations evolve, information gathered from the most fit members is continuously used to rearrange the binary representation grid on the search space, thus establishing a search memory such that the best known individual is always positioned at the center of the Gaussian offset.