论文标题
图形上的多重性跨界运算符。扩展版本
A multiplicity-preserving crossover operator on graphs. Extended version
论文作者
论文摘要
进化算法通常通过交叉和突变探索解决方案的搜索空间。虽然突变由溶液的局部修饰组成，但跨界将两种溶液的遗传信息混合在一起，以计算新的溶液。对于模型驱动的优化（MDO），模型直接提供了可能的解决方案（而不是首先将其转换为另一种表示），仅开发了一个通用的交叉操作员。我们将图形作为模型的正式基础，进一步完善了该运算符的方式，以至于保留了其他良好形式的约束：我们证明，给定两个模型，可以满足给定的一组多重性约束作为输入，我们的精制交叉运算符将两个新模型计算为满足约束集的输出。
Evolutionary algorithms usually explore a search space of solutions by means of crossover and mutation. While a mutation consists of a small, local modification of a solution, crossover mixes the genetic information of two solutions to compute a new one. For model-driven optimization (MDO), where models directly serve as possible solutions (instead of first transforming them into another representation), only recently a generic crossover operator has been developed. Using graphs as a formal foundation for models, we further refine this operator in such a way that additional well-formedness constraints are preserved: We prove that, given two models that satisfy a given set of multiplicity constraints as input, our refined crossover operator computes two new models as output that also satisfy the set of constraints.
