Local Optima Networks of NK Landscapes with Neutrality
INRIA Lille - Nord Europe（法国国家信息与自动化研究所里尔-北欧分部）；University of Nice Sophia-Antipolis（尼斯索菲亚·安提波利斯大学）；CNRS（法国国家科学研究中心）；University of Nottingham（诺丁汉大学）；University of Lausanne（洛桑大学）
Sébastien Verel, Gabriela Ochoa, Marco Tomassini
备注：
IEEE Transactions on Evolutionary Computation volume 14, 6 (2010) to appear
In previous work we have introduced a network-based model that abstracts many details of the underlying landscape and compresses the landscape information into a weighted, oriented graph which we call the local optima network. The vertices of this graph are the local optima of the given fitness landscape, while the arcs are transition probabilities between local optima basins. Here we extend this formalism to neutral fitness landscapes, which are common in difficult combinatorial search spaces. By using two known neutral variants of the NK family (i.e. NKp and NKq) in which the amount of neutrality can be tuned by a parameter, we show that our new definitions of the optima networks and the associated basins are consistent with the previous definitions for the non-neutral case. Moreover, our empirical study and statistical analysis show that the features of neutral landscapes interpolate smoothly between landscapes with maximum neutrality and non-neutral ones. We found some unknown structural differences between the two studied families of neutral landscapes. But overall, the network features studied confirmed that neutrality, in landscapes with percolating neutral networks, may enhance heuristic search. Our current methodology requires the exhaustive enumeration of the underlying search space. Therefore, sampling techniques should be developed before this analysis can have practical implications. We argue, however, that the proposed model offers a new perspective into the problem difficulty of combinatorial optimization problems and may inspire the design of more effective search heuristics.
Local Optima Networks: A New Model of Combinatorial Fitness Landscapes
University of Stirling（斯特林大学）；INRIA Lille - Nord Europe（法国国家信息与自动化研究所里尔-北欧分所）；University of Nice Sophia-Antipolis（尼斯索菲亚-安提波利斯大学）；University of Lausanne（洛桑大学）
Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance
University of Málaga（马拉加大学）；University of Lausanne（洛桑大学）；University of Nottingham（诺丁汉大学）；INRIA Lille - Nord Europe（法国国家信息与自动化研究所里尔-北欧洲分所）；University of Nice Sophia-Antipolis（尼斯索菲亚·安提波利斯大学）
Local optima networks and the performance of iterated local search
University of Lausanne（洛桑大学）；Université Nice-Sophia Antipolis（尼斯-索菲亚·安提波利斯大学）；INRIA Lille Nord Europe（法国国家信息与自动化研究所里尔-北欧分所）；University of Nottingham（诺丁汉大学）
Local Optima Networks of the Quadratic Assignment Problem
University of Lausanne（洛桑大学）；INRIA Lille - Nord Europe（法国国家信息与自动化研究所里尔-北欧分所）；University of Nice Sophia-Antipolis（尼斯索菲亚·安提波利斯大学）；CNRS（法国国家科学研究中心）；University of Nottingham（诺丁汉大学）
Land Operations Division, Defence Science and Technology Organisation（国防科学技术组织陆地作战分部）
理解系统如何被设计成可进化的是优化、进化和复杂系统科学研究的基础。因此，许多研究人员已经认识到可进化性的重要性，即在生物进化和演化计算领域中发现更高适应度的新变体的能力。Ciliberti等人（Proc.Nat.Acad.科学，2007）和Wagner（Proc. R. Soc. B.，2008）提出了一个潜在的重要联系之间的鲁棒性和系统的可进化性。特别是，有人认为，鲁棒性实际上可能导致进化的出现。在这里，我们研究了两个设计原则，冗余和退化，实现鲁棒性，我们表明，他们有一个显着不同的影响系统的演化。特别是，纯冗余系统被发现有很少的进化，而系统退化，即分布式鲁棒性，可以是数量级更进化。这些结果提供了深入了解的一般原则，实现进化，并可能被证明是一个重要的一步，在追求进化表示演化计算。
Local Optimal Sets and Bounded Archiving on Multi-objective NK-Landscapes with Correlated Objectives
Université Libre de Bruxelles（布鲁塞尔自由大学）；Université Lille 1（里尔第一大学）；Inria Lille-Nord Europe（里尔-北欧欧洲信息与自动化研究所）；Université du Littoral Côte d’Opale（滨海奥帕尔海岸大学）
First-improvement vs. Best-improvement Local Optima Networks of NK Landscapes
University of Nottingham（诺丁汉大学）；INRIA Lille - Nord Europe（法国国家信息与自动化研究所里尔-北欧洲分所）；University of Nice Sophia-Antipolis（尼斯索菲亚·安提波利斯大学）；University of Lausanne（洛桑大学）
Clustering of Local Optima in Combinatorial Fitness Landscapes
University of Nottingham（诺丁汉大学）；INRIA Lille - Nord Europe（法国国家信息与自动化研究所里尔-北欧分所）；University of Nice Sophia-Antipolis（尼斯索菲亚·安提波利斯大学）；University of Lausanne（洛桑大学）
