Dynamics in Proportionate Selection.
This paper proposes a new selection method for Genetic Algorithms. The motivation behind the proposed method is to investigate the effect of different selection methods on the rate of convergence. The new method Dynamic Selection Method (DSM) is based on proportionate selection. DSM functions by continuously changing the criteria for parent selection (dynamic) based on the number of generations in a run and the current generation. Results show that by using DSM to maintain diversity in a population gives slower convergence, but, their overall performance was an improvement. Relationship between slower convergences, in GA runs, leading to better solutions, has been identified.
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