Abstract
In this paper, a new direction-based exponential crossover operator (DEX) for a real-coded genetic algorithm (RCGA) has been developed. Its name indicates that this newly designed DEX is influenced by the directional knowledge of the problem. This knowledge about a problem actually helps to decide the search direction of the algorithm in the variable space to move toward the globally optimum solution. Now, the task of collecting this data is quite tricky and may have several ways. However, we suggest one approach to obtain this knowledge during the evolution of solutions. Utilizing this prior knowledge during the crossover operation, the children solutions are created with a biasness of that search direction. This, in fact, makes the searching mechanism of an RCGA more efficient. To measure the performance of the DEX, ten classical benchmark test functions have been taken and the experiments are done using an RCGA with the proposed crossover operator, and the results are compared with another popular crossover operator, namely, simulated binary crossover (SBX).
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Das, A.K., Pratihar, D.K. (2020). A Direction-Based Exponential Crossover Operator for Real-Coded Genetic Algorithm. In: Singh, B., Roy, A., Maiti, D. (eds) Recent Advances in Theoretical, Applied, Computational and Experimental Mechanics. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-1189-9_25
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DOI: https://doi.org/10.1007/978-981-15-1189-9_25
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