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Diversity improvement of solutions in multiobjective genetic algorithms using pseudo function inverses

机译:使用伪函数逆的多目标遗传算法解的多样性改进

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Diversity improvement methods generally implement niching and fitness sharing schemes. In this work we propose a general principle based on using the inverse mapping from objective space to decision space that allows for the creation of diverse solutions in a direct manner. When analytical forms of objective functions are known, we propose a method of generating set-valued inverse maps, in functional or algorithmic form, which when restricted to the feasible search range yield pseudo inverses of objectives. In the absence of analytical functional forms we propose the use of artificial neural networks(ANNs) in a novel configuration to directly learn the inverse map without network inversion procedures. We implement two diversity creation operators and use them in a standard binary multi-objective genetic algorithm(MOGA) to solve standard bi-objective optimization problems. We also propose a parameter less approach of fixing the number and desirable locations of solutions in sparse regions. Proposed algorithms are compared with NSGA-II and it is shown that the proposed algorithms achieve desired level of diversity in fewer function evaluations compared to NSGA-II.
机译:多样性改善方法通常实现小生境和适应度共享方案。在这项工作中,我们提出了基于从目标空间到决策空间的逆映射的通用原理,该原理允许直接创建各种解决方案。当目标函数的解析形式已知时,我们提出一种以函数或算法形式生成集值逆映射的方法,当将其限制在可行的搜索范围内时,会产生目标的伪逆。在缺乏分析功能形式的情况下,我们建议在新颖的配置中使用人工神经网络(ANN)直接学习逆映射,而无需使用网络反演程序。我们实现了两个多样性创建算子,并在标准的二进制多目标遗传算法(MOGA)中使用它们来解决标准的双目标优化问题。我们还提出了一种无参数的方法来固定稀疏区域中解的数量和所需位置。将拟议的算法与NSGA-II进行了比较,结果表明,与NSGA-II相比,拟议的算法在较少的功能评估中就达到了所需的多样性。

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