Efficiency analysis of a self-configuring binary genetic algorithm with a modified method of dynamic correction of the search space

This paper presents a modification of the self-configuring genetic algorithm (SelfCGA) aimed at improving search efficiency in global optimization problems. The proposed approach combines dynamic correction of the search domain with phenotype clustering of the population, which makes it possible to identify promising regions of the solution space more effectively. The use of clustering helps maintain population diversity and reduces the risk of premature convergence to local optima. To evaluate the proposed modification, computational experiments were conducted using the CEC2017 benchmark suite with problem dimensions of 10, 30, and 50. Each algorithm was executed 50 independent times, ensuring statistical reliability of the results. The performance was assessed by comparing average and best fitness values, as well as by analyzing the convergence dynamics during the evolutionary process. The experimental results demonstrate that the modified SelfCGA with dynamic correction of the search domain reaches a stabilization state – where further improvements during the evolutionary search become negligible – in fewer generations for most benchmark functions. This advantage remains evident even as the dimensionality of the search space increases. The proposed modification does not require manual parameter tuning and does not increase the structural complexity of the base SelfCGA, which makes it well suited for practical applications.

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