What really sparked my interest was how certain parameters worked better at executing and optimization algorithm convergence even though the objective formula had no significant differences. Thus the research question stated: 'Which parameters provides an upmost optimal convergence solution of an Objective formula using the on-the-fly method?' This research was done in an experimental concept in which five different algorithms were tested with different objective functions to discover which parameter would result well for the best convergence. To find the correct parameter a method called 'on-the-fly' was applied. I run the experiments with five different optimization algorithms. One of the test runs showed that each parameter has an increasing or decreasing convergence accuracy towards the subjective function depending on which specific optimization algorithm you choose. Each parameter has an increasing or decreasing convergence accuracy toward the subjective function. One of the results in which evolutionary algorithm was applied with only the recombination technique did well at finding the best optimization. As well that some results have an increasing accuracy visualization by combing mutation or several parameters in one test performance. In conclusion, each algorithm has its own set of the parameter that converge differently. Also depending on the target formula that is used. This confirms that the fly method a suitable approach at finding the best parameter. This means manipulations and observe the effects in process to find the right parameter works as long as the learning cost rate decreases over time.

中文翻译：

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用于优化算法最佳收敛的参数 On-The-Fly

真正引起我兴趣的是某些参数如何更好地执行和优化算法收敛，即使目标公式没有显着差异。因此，研究问题指出：“哪些参数使用动态方法提供了目标公式的最高最优收敛解决方案？” 这项研究是在一个实验概念中完成的，其中使用不同的目标函数测试了五种不同的算法，以发现哪个参数会产生最佳收敛效果。为了找到正确的参数，应用了一种称为“on-the-fly”的方法。我用五种不同的优化算法运行实验。其中一次测试表明，每个参数对主观函数的收敛精度会增加或减少，具体取决于您选择的特定优化算法。每个参数对主观函数具有增加或减少的收敛精度。仅使用重组技术应用进化算法的结果之一在寻找最佳优化方面做得很好。此外，通过在一个测试性能中组合突变或多个参数，一些结果具有更高的准确度可视化。总之，每个算法都有自己的一组收敛不同的参数。也取决于所使用的目标公式。这证实了飞行方法是寻找最佳参数的合适方法。