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Visualization of GA Progress and Improvement while Resolving Standard Optimization Problems

机译：解决标准优化问题的同时可视化GA进度和改进

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摘要

Genetic Algorithm (GA), designed according to the principles of natural genetics, is a search and optimization algorithm, used in optimizing (minimizing or maximizing) a given function and possibly finding its most suitable solution. By adjusting the parameters and operators of GA, it can be modeled and adjusted to solve a specific problem, such as the traveling salesman problem and the 2D packing problem, treated in this paper. The data obtained from the programs that implement the GA are visually presented as a graph from which we can see the progress of the GA's fitness value minimization over the generations. In addition to this dependence, also shown is the data for the smallest fitness value, the generation of its occurrence and the solution itself (the chromosome with the smallest fitness value). Within this visualization we have analysed the impact of the number of generations and the number of chromosomes on the fitness value and the time required to find the optimal solution.
机译：根据天然遗传学原理设计的遗传算法（GA）是一种搜索和优化算法，用于优化（最小化或最大化）给定功能并可能找到最合适的解决方案。通过调整GA的参数和操作员，可以建模和调整以解决特定问题，例如旅行推销员问题和2D包装问题，在本文中处理。从实现GA的程序获得的数据视觉上呈现为图的图表，我们可以从中看到GA对世代的体重最小化的进展。除了这种依赖之外，还示出了最小的健康价值的数据，其发生的产生和溶液本身（具有最小的健身值的染色体）。在这种可视化内，我们已经分析了几代人数和染色体数量的影响，以及找到最佳解决方案所需的时间。

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