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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Searching the solution space in constructive geometric constraint solving with genetic algorithms
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Searching the solution space in constructive geometric constraint solving with genetic algorithms

机译：用遗传算法搜索构造性几何约束求解中的解空间

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摘要

Geometric problems defined by constraints have an exponential number of solution instances in the number of geometric elements involved. Generally, the user is only interested in one instance such that besides fulfilling the geometric constraints, exhibits some additional properties. Selecting a solution instance amounts to selecting a given root every time the geometric constraint solver needs to compute the zeros of a multi valuated function. The problem of selecting a given root is known as the Root Identification Problem.In this paper we present a new technique to solve the root identification problem. The technique is based on an automatic search in the space of solutions performed by a genetic algorithm. The user specifies the solution of interest by defining a set of additional constraints on the geometric elements which drive the search of the genetic algorithm. The method is extended with a sequential niche technique to compute multiple solutions. A number of case studies illustrate the performance of the method.
机译：由约束定义的几何问题在所涉及的几何元素数量中具有成倍数量的解实例。通常，用户仅对一种情况感兴趣，从而除了满足几何约束之外，还表现出一些其他属性。每次几何约束求解器需要计算多值函数的零点时，选择一个解决方案实例就等于选择一个给定的根。选择给定根的问题称为根识别问题。在本文中，我们提出了一种解决根识别问题的新技术。该技术基于通过遗传算法执行的解空间中的自动搜索。用户通过在驱动遗传算法搜索的几何元素上定义一组附加约束来指定感兴趣的解决方案。该方法使用顺序细分技术进行扩展，以计算多个解决方案。大量案例研究说明了该方法的性能。

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