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Multiple Solutions by Means of Genetic Programming: A Collision Avoidance Example

机译:遗传编程的多重解:避免碰撞的例子

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Seldom is it practical to completely automate the discovery of the Pareto Frontier by genetic programming (GP). It is not only difficult to identify all of the optimization parameters a-priori but it is hard to construct functions that properly evaluate parameters. For instance, the "ease of manufacture" of a particular antenna can be determined but coming up with a function to judge this on all manner of GP-discovered antenna designs is impractical. This suggests using GP to discover many diverse solutions at a particular point in the space of requirements that are quantifiable, only a-posteriori (after the run) to manually test how each solution fares over the less tangible requirements e.g. "ease of manufacture" . Multiple solutions can also suggest requirements that are missing. A new toy problem involving collision avoidance is introduced to research how GP may discover a diverse set of multiple solutions to a single problem. It illustrates how emergent concepts (linguistic labels) rather than distance measures can cluster the GP generated multiple solutions for their meaningful separation and evaluation.
机译:通过基因编程(GP)完全自动化帕累托边疆的发现是不切实际的。先验识别所有优化参数不仅困难,而且构建适当评估参数的函数也很困难。例如,可以确定特定天线的“制造容易性”,但是在所有发现GP的天线设计上都提出一种判断这一点的功能是不切实际的。这建议使用GP在可量化需求空间中的特定点上发现许多不同的解决方案,只有a后验(运行后)才能手动测试每种解决方案如何满足较不实际的需求,例如“易于制造”。多种解决方案也可以建议缺少的需求。引入了一个涉及避免碰撞的新玩具问题,以研究GP如何发现针对单个问题的多种解决方案。它说明了紧急概念(语言标签)而不是距离度量如何将GP生成的多个解决方案聚集在一起,以实现有意义的分离和评估。

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