Abstract
Extending state-of-the-art evolutionary algorithms is a widespread research direction. This trend has resulted in algorithms that give good results but are complex and challenging to analyze. One of these algorithms is EA4Eig — the winner of the CEC 2022 competition on single objective bound constrained search. The algorithm internally uses four optimization algorithms with modified components. This paper presents an analysis of EA4Eig and proposes a simplified version thereof exhibiting better optimization performance. The analysis found that the original source code contains errors that impact the algorithm’s rank. The code was corrected, and the CEC 2022 competition ranking was recalculated. The impact of individual EA4Eig components on its performance was empirically analyzed. As a result, the algorithm was simplified by removing two of them. The best remaining component was analyzed further, which made it possible to remove some unnecessary and harmful code. Several versions of the algorithm were created and tested, varying in the degree of simplification. The simplest of them is implemented in 244 lines of C++ code, whereas the original implementation used 716 lines of Matlab code. Further analyses focused on the parameters of the algorithm. The constants hidden in the source code were named and treated as additional configurable parameters that underwent tuning. The ablation analyses showed that two of these hidden parameters had the most significant impact on the improvement achieved by the tuned version. The results of the original and simplified versions were compared on CEC 2022 and BBOB benchmarks. The results confirm that the simplified version is better than the original one on both these benchmarks.
