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首页> 外文会议>IEEE Congress on Evolutionary Computation >Verification of thermo-dynamical genetic algorithm to solve the function optimization problem through diversity measurement — Diversity measurement and its application to selection strategies in genetic algorithms
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Verification of thermo-dynamical genetic algorithm to solve the function optimization problem through diversity measurement — Diversity measurement and its application to selection strategies in genetic algorithms

机译：验证热力学遗传算法通过多样性度量解决功能优化问题的方法—多样性度量及其在遗传算法选择策略中的应用

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In this paper, it is experimentally verified that TDGA (Thermo Dynamical Genetic Algorithm) is effective in solving a function optimization problem using Genetic Algorithms, because of its sustainability of population diversity and efficiency of searching for solutions. We experimentally and quantitatively verify the hypothesis that we can improve the ratio of searching for the optimum solution and the accuracy of the solution by maintaining the diversity. In our investigation, we measure the diversity of the population with the entropy defined in TDGA. TDGA is a selection strategy based on the minimum free energy principle in thermodynamics. In applying the principle to GAs, we select individuals to make the average energy a minimum and the entropy H a maximum. TDGA is compared with immune-GA (immune Genetic Algorithm), simple GA and GA with scaling windows from the viewpoint of sustainability of diversity of population. Immune-GA is an optimization method based on Jerne's idiotype network, which hypothesizes homeostasis of organic immunity system. The effectiveness of these four GA models is verified quantitatively by diversity measurement. In this study, we use BLX-α for localizing and centralizing the search. Each of above four GA models generates individuals by two-point crossover operation, and if the diversity of the generated children falls below the required threshold, BLX-α starts to work. Experimental results by using ten well-known test functions including De Jong's test functions are reported in this paper.
机译：在本文中，通过实验验证了TDGA（热力学遗传算法）可以有效地解决遗传算法的功能优化问题，因为它具有种群多样性的可持续性和寻找解决方案的效率。我们通过实验和定量验证了以下假设：可以通过保持多样性来提高寻找最佳解的比率和解的准确性。在我们的调查中，我们使用TDGA中定义的熵来衡量总体的多样性。 TDGA是一种基于热力学中最小自由能原理的选择策略。在将该原理应用于遗传算法时，我们选择个体以使平均能量最小，而使熵H最大。从人口多样性的可持续性角度，将TDGA与immune-GA（免疫遗传算法），简单GA和具有缩放窗口的GA进行了比较。 Immune-GA是一种基于Jerne独特型网络的优化方法，它假设了有机免疫系统的稳态。这四个GA模型的有效性通过多样性测量进行了定量验证。在这项研究中，我们使用BLX-α进行本地化和集中化搜索。上面的四个GA模型中的每个模型都通过两点交叉运算来生成个体，并且如果所生成子代的多样性降到所需阈值以下，则BLX-α开始起作用。本文报道了通过使用十个著名的测试函数（包括De Jong的测试函数）进行的实验结果。

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