Interactive One-Max Problem Allows to Compare the Performance of Interactive and Human-Based Genetic Algorithms
Interactive One-Max Problem Allows to Compare the Performance of Interactive and Human-Based Genetic Algorithms
复制标题
交互式最大问题可以比较交互式遗传算法和基于人类的遗传算法的性能
DOI：
10.1007/978-3-540-24854-5_98
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Alexander Kosorukoff
中科院分区：
文献类型：
--
作者：
Chihyung Derrick Cheng;Alexander Kosorukoff
基于人类的遗传算法（HBGA）使用人类评估和创新来优化解决方案的种群（Kosorukoff，2001）。HBGA的新贡献是引入了以人为本的创新运营商。然而，没有人试图定量地衡量基于人类的创新运营商对遗传算法整体性能的影响，特别是通过比较HBGAs和交互式遗传算法（伊加）不使用人类创新的性能。本文表明，上述效果是可衡量的，并进一步侧重于定量比较这两类算法的效率。为了实现这一目的，本文提出了一个交互式模拟的一个最大值问题，提出了基于人的创新运营商适合这个问题，并比较了HBGA和伊加的收敛结果相同的问题。
Human-based genetic algorithms (HBGA) use both human evaluation and innovation to optimize a population of solutions (Kosorukoff, 2001). The novel contribution of HBGAs is an introduction of human-based innovation operators. However, there was no attempt to measure the effect of human-based innovation operators on the overall performance of GAs quantitatively, in particular, by comparing the performance of HBGAs and interactive genetic algorithms (IGA) that do not use human innovation. This paper shows that the mentioned effect is measurable and further focuses on quantitative comparison of the efficiency of these two classes of algorithms. In order to achieve this purpose, this paper proposes an interactive analog of the one-max problem, suggests human-based innovation operators appropriate for this problem, and compares convergence results of HBGA and IGA for the same problem.