以爬坡过坎的积木式结构克服层级困难
Overcoming Hierarchical Difficulty by Hill-Climbing the Building Block Structure
摘要：
积木假说表明，遗传算法（GAs）是非常适合分层问题，其中有效的解决需要适当的问题分解和组装的解决方案，从子解决方案具有很强的非线性相互依赖性。本文提出了一种爬山操作的积木（BB）空间，可以有效地解决层次问题。新的积木爬山（BBHC）使用过去的爬山经验来提取BB信息，并相应地调整其邻域结构。邻域结构的永久自适应允许该方法爬上层次结构，连续求解层次级别。对于完全非欺骗的层次BB结构，BBHC可以在线性时间内求解层次问题。实证结果证实，所提出的方法规模几乎与问题的大小，从而明显优于基于人口的重组方法。
英文摘要：
The Building Block Hypothesis suggests that Genetic Algorithms (GAs) are well-suited for hierarchical problems, where efficient solving requires proper problem decomposition and assembly of solution from sub-solution with strong non-linear interdependencies. The paper proposes a hill-climber operating over the building block (BB) space that can efficiently address hierarchical problems. The new Building Block Hill-Climber (BBHC) uses past hill-climb experience to extract BB information and adapts its neighborhood structure accordingly. The perpetual adaptation of the neighborhood structure allows the method to climb the hierarchical structure solving successively the hierarchical levels. It is expected that for fully non deceptive hierarchical BB structures the BBHC can solve hierarchical problems in linearithmic time. Empirical results confirm that the proposed method scales almost linearly with the problem size thus clearly outperforms population based recombinative methods.
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