A comparison of dynamic fitness schedules for evolutionary design of amplifiers

Proceedings of the First NASA/DoD Workshop on Evolvable Hardware Pub Date : 1999-07-19 DOI:10.1109/EH.1999.785439

J. Lohn, G. Haith, S. Colombano, D. Stassinopoulos

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引用次数: 34

Abstract

High-level analog circuit design is a complex problem domain in which evolutionary search has recently produced encouraging results. However, little is known about how to best structure evolution far these tasks. The choices of circuit representation, fitness evaluation technique, and genetic operators clearly have a profound effect on the search process. In this paper, we examine fitness evaluation by comparing the effectiveness of four fitness schedules. Three fitness schedules are dynamic-the evaluation function changes over the course of the run, and one is static. Coevolutionary search is included, and we present a method of evaluating the problem population that is conducive to multiobjective optimization. Twenty-five runs of an analog amplifier design task using each fitness schedule are presented. The results indicate that solution quality is highest with static and coevolving fitness schedules as compared to the other two dynamic schedules. We discuss these results and offer two possible explanations for the observed behavior: retention of useful information, and alignment of problem difficulty with circuit proficiency.

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放大器进化设计中动态适应度计划的比较

高级模拟电路设计是一个复杂的问题领域，进化搜索最近产生了令人鼓舞的结果。然而，对于如何在这些任务中最好地组织进化，人们知之甚少。电路表示、适应度评估技术和遗传算子的选择显然对搜索过程有深远的影响。在本文中，我们通过比较四种健身计划的有效性来检验健身评估。有三种健身计划是动态的——评估函数在跑步过程中发生变化，还有一种是静态的。在此基础上，提出了一种有利于多目标优化的问题群评估方法。给出了一个模拟放大器设计任务使用每个适应度计划的25次运行。结果表明，与其他两种动态方案相比，静态和共同进化适应度方案的解决方案质量最高。我们讨论了这些结果，并为观察到的行为提供了两种可能的解释:保留有用的信息，以及问题难度与电路熟练程度的一致。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the First NASA/DoD Workshop on Evolvable Hardware

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