| Abstract: |  This bachelor project aims on improvement of fitness evaluation process in evolutionary algorithms. In its progress, existing methods of fitness efficiency enhancement were explored and two new methods of crossover success estimation were proposed and then tested. The proposed methods served to create new types of selection of individuals for crossover process and as such were incorporated into an evolutionary algorithm. Performance of the designed algorithms, along with ordinary evolutionary algorithms, was then tested on optimisation of common testing objective functions with modified domains. Extensive tests proved that the methods of crossover success estimation can be efficient in optimisation of certain functions, but are not successful on all of the tested types of problems. Nevertheless, it was determined that the crossover success estimation is effective at least at the beginning of the evolution.
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