欺骗性和中立性--健身景观的ND系列
Deceptiveness and Neutrality - the ND family of fitness landscapes
摘要：
当相当数量的突变对适应度值没有影响时，适应度景观被称为中性。为了研究存在于许多现实世界应用中的中立性与元计算性能之间的相互作用，设计能够精确调整中立度分布的景观是有用的。尽管已经设计了许多中性景观模型，但它们都不足以创建具有特定中性度分布的景观。我们提出了三个步骤来设计这样的景观：首先使用一种算法，我们构建一个景观的分布大致符合目标，然后我们使用模拟退火启发式，使两个分布更接近，最后我们影响每个神经网络的适应值。然后使用这个新的适应度景观家族，我们能够突出欺骗性和中立性之间的相互作用。
英文摘要：
When a considerable number of mutations have no effects on fitness values, the fitness landscape is said neutral. In order to study the interplay between neutrality, which exists in many real-world applications, and performances of metaheuristics, it is useful to design landscapes which make it possible to tune precisely neutral degree distribution. Even though many neutral landscape models have already been designed, none of them are general enough to create landscapes with specific neutral degree distributions. We propose three steps to design such landscapes: first using an algorithm we construct a landscape whose distribution roughly fits the target one, then we use a simulated annealing heuristic to bring closer the two distributions and finally we affect fitness values to each neutral network. Then using this new family of fitness landscapes we are able to highlight the interplay between deceptiveness and neutrality.
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。
