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Optimizing Least-Cost Steiner Tree in Graphs via an Encoding-Free Genetic Algorithm

机译：通过无编码遗传算法优化图中的最小成本斯坦纳树

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摘要

Most bio-inspired algorithms for solving the Steiner tree problem (STP) require the procedures of encoding and decoding. The frequent operations on both encoding and decoding inevitably result in serious time consumption and extra memory overhead, and then reduced the algorithms' practicability. If a bio-inspired algorithm is encoding-free, its practicability will be improved. Being motivated by this thinking, this article presents an encoding-free genetic algorithm in solving the STP. To verify our proposed algorithm's validity and investigate its performance, detailed simulations were carried out. Some insights in this article may also have significance for reference when solving the other problems related to the topological optimization.
机译：解决斯坦纳树问题（STP）的大多数受生物启发的算法都需要编码和解码过程。频繁地进行编码和解码操作不可避免地导致了严重的时间消耗和额外的内存开销，从而降低了算法的实用性。如果以生物为灵感的算法是无编码的，则其实用性将得到改善。出于这种想法，本文提出了一种无编码遗传算法来解决STP问题。为了验证我们提出的算法的有效性并研究其性能，进行了详细的仿真。本文中的一些见解在解决与拓扑优化有关的其他问题时也可能具有参考意义。

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