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Optimization is essential for finding suitable answers to real-life problems. In particular, genetic (or more generally, evolutionary) algorithms can provide satisfactory approximate solutions to many problems to which exact analytical results are not accessible. In this paper, we present both the theoretical and experimental results on a new genetic algorithm called Dissimilarity and Similarity of Chromosomes (DSC). This methodology constructs new chromosomes starting with the pairs of existing ones by exploring their dissimilarities and similarities. To demonstrate the performance of the algorithm, it is run on 17 two-dimensional, 1 four-dimensional, and 2 ten-dimensional optimization problems described in the literature, and compared with the well-known GA, CMA-ES, and DE algorithms.The results of the tests show the superiority of our strategy in the majority of cases.

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