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首页> 外文期刊>International journal of computer mathematics >OPTIMISATION WITH REAL-CODED GENETIC ALGORITHMS BASED ON MATHEMATICAL MORPHOLOGY
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OPTIMISATION WITH REAL-CODED GENETIC ALGORITHMS BASED ON MATHEMATICAL MORPHOLOGY

机译：基于数学形态学的实数编码遗传算法优化

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摘要

The goal of this work is to propose a novel approach to function optimisation by evolutionary techniques, in particular, real-coded genetic algorithms. A new genetic crossover operator, suitable for real codification, has been designed. This operator is called morphological crossover as it is based on mathematical morphology theory. The morphological crossover includes a new genetic diversity measure that has low computational cost. This operator is presented along with the resolution of a set of optimisation problems, including neural network training. The results are compared to other optimisation approaches as gradient descent methods or binary and real-coded genetic algorithms using different crossover operators. These tests show that the properties exhibited by the proposed operator when using real-coded genetic algorithms give higher convergence speed and less probability of being trapped in a local optimum.
机译：这项工作的目的是提出一种通过进化技术（特别是实编码遗传算法）进行功能优化的新方法。设计了一种适用于实际编码的新型遗传交叉算子。该算子基于数学形态学理论，因此被称为形态学交叉。形态交叉包括一种具有较低计算成本的新遗传多样性测度。连同一组优化问题的解决方案（包括神经网络训练）一起介绍了该算子。将结果与其他优化方法（例如梯度下降方法）或使用不同交叉算符的二进制和实数编码遗传算法进行比较。这些测试表明，当使用实数编码遗传算法时，拟议的算子所展现的特性可提供更高的收敛速度，并减少陷入局部最优的可能性。

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