Comparison of certain evolution-inspired algorithms

Purpose: This paper aims at making a comparison of three optimization algorithms - standard Genetic Algorithm and its two modifications: Extended Compact Genetic Algorithm and Population-based Incremental Learning. Design/methodology/approach: To reach the objectives of the paper the solver based on algorithms was developed. Certain test functions were applied to test them and evaluate their performance. Findings: Modifications of Genetic Algorithm reach optimal values faster and more precisely. Research limitations/implications: Problem of optimization of certain cost functions frequently occurs in many management problems of organizing the optimal workflow in organizations. It can be used also in engineering problems of designing optimal devices at lowest possible cost. Practical implications: One can optimize function faster using discussed algorithms than by using standard evolutionary algorithm. Originality/value: The paper shows results of comparisons of three algorithms, discusses how tuning meta parameters helps to increase their efficiency and accuracy.