Abstract
This paper presents an empirical comparison of some evolutionary algorithms to solve numerical optimization problems. The aim of the paper is to test a micro-evolutionary algorithm called Elitist evolution, originally designed to work with small populations, on a set of diverse test problems (unimodal, multimodal, separable, non-separable, shifted, and rotated) with different dimensionalities. The comparison covers micro-evolutionary algorithms based on differential evolution and particle swarm optimization. The number of successful runs, the quality of results and the computational cost, measured by the number of evaluations required to reach the vicinity of the global optimum, are used as performance criteria. Furthermore, a comparison against a state-of-the-art algorithm is presented. The obtained results suggest that the Elitist evolution is very competitive as compared with other algorithms, especially in high-dimensional search spaces. Key words: Optimization methods, nature-inspired algorithms, evolutionary computation, swarm intelligence.
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