An Implementation of Binary and Floating Point Chromosome Representation in Genetic Algorithm

This paper describes the implementation details and compares two methods for optimisation of multi-dimensional cost functions. The implemented genetic algorithm uses two chromosome representations: binary and floating point. In both representations the algorithm is based on steady-state reproduction, roulette-wheel bad individuals selection and has the same parameters.