论文标题
一种在高复杂性下在NK景观上找到卓越的健身的算法：混乱
An Algorithm to find Superior Fitness on NK Landscapes under High Complexity: Muddling Through
论文作者
论文摘要
在高复杂性下 - 由NK景观中决策的成分元素之间的普遍相互依存给出 - 我们的算法获得了与现有研究中报道的更高的适应性。我们将包括决策组成的决策要素分发到集群中。当考虑决策元素的价值变化时，如果居住在决策元素旁边的群集成员的总适合度更高，则进行远期移动。选择了路径中最高健身的决策配置。增加簇的数量获得了更高的适应度。此外，实施群集中最多两个变化的动作也获得了更高的健身性。我们的算法通过启用更广泛的搜索来获得卓越的结果，从而可以检查更遥远的配置。我们将这种算法命名为“通过算法”的混乱，以纪念查尔斯·林德布罗姆（Charles Lindblom），他在复杂的计算机模拟开始之前就发现了该过程的功效。
Under high complexity - given by pervasive interdependence between constituent elements of a decision in an NK landscape - our algorithm obtains fitness superior to that reported in extant research. We distribute the decision elements comprising a decision into clusters. When a change in value of a decision element is considered, a forward move is made if the aggregate fitness of the cluster members residing alongside the decision element is higher. The decision configuration with the highest fitness in the path is selected. Increasing the number of clusters obtains even higher fitness. Further, implementing moves comprising of up to two changes in a cluster also obtains higher fitness. Our algorithm obtains superior outcomes by enabling more extensive search, allowing inspection of more distant configurations. We name this algorithm the muddling through algorithm, in memory of Charles Lindblom who spotted the efficacy of the process long before sophisticated computer simulations came into being.
