为什么模块不占主导地位-调查一个著名的模块诱导问题域的结构
Why don't the modules dominate - Investigating the Structure of a Well-Known Modularity-Inducing Problem Domain
摘要：
Wagner的模块化诱导问题域是对模块化演化研究的重要贡献，包括演化理论和演化计算。我们在经典的遗传算法下研究了它的行为。与我们在自然界中观察到的不同，模块化的出现是高度有条件和依赖的，例如，对搜索的渴望。在自然界中，模块化解决方案通常占主导地位的人口，而在这个领域，模块化，当它出现时，是一个相对罕见的变体。模块化的出现很大程度上取决于适应度函数的随机波动，如果适应度函数随机变化但不变，模块化的进化就少得多。有趣的是，高适应度的非模块化解决方案通常可以通过手动删除所有模块间的边来转换为更高适应度的模块化解决方案。尽管仔细探索，我们还没有一个完整的解释，为什么遗传算法是无法找到这些更好的解决方案。
英文摘要：
Wagner's modularity inducing problem domain is a key contribution to the study of the evolution of modularity, including both evolutionary theory and evolutionary computation. We study its behavior under classical genetic algorithms. Unlike what we seem to observe in nature, the emergence of modularity is highly conditional and dependent, for example, on the eagerness of search. In nature, modular solutions generally dominate populations, whereas in this domain, modularity, when it emerges, is a relatively rare variant. Emergence of modularity depends heavily on random fluctuations in the fitness function, with a randomly varied but unchanging fitness function, modularity evolved far more rarely. Interestingly, high-fitness non-modular solutions could frequently be converted into even-higher-fitness modular solutions by manually removing all inter-module edges. Despite careful exploration, we do not yet have a full explanation of why the genetic algorithm was unable to find these better solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
