协同算法在并行问题求解中令人惊讶的小效率
The surprising little effectiveness of cooperative algorithms in parallel problem solving
摘要：
生物和文化启发的优化算法是当今许多研究领域的基本工具包的一部分。通过模仿自然界和动物社会中的过程，这些通用搜索算法承诺提供最佳或接近最佳的解决方案，几乎不使用任何关于它们要解决的优化问题的信息。在这里，我们研究了一个文化启发的算法-模仿学习搜索-以及无性和性变体的进化算法在寻找全局最大值的NK-健身景观的性能。主要的性能指标是代理更新所需的算法，以找到这些全球最大值和基线性能，建立合作算法的有效性，是由盲搜索，其中代理探索的问题空间（二进制字符串）通过翻转位随机设置的总数量。我们发现，即使是平滑的景观，表现出一个单一的最大值，进化算法并没有表现得比盲搜索由于遗传轮盘赌的随机效应。模仿学习不受这种影响，这要归功于群体中最合适的字符串的确定性选择，它被用作模仿的模型。折衷的是，对于崎岖的景观，模仿学习搜索比进化算法更容易陷入局部最大值。事实上，在崎岖的景观与温和的局部最大值密度的情况下，盲搜索要么击败或匹配的合作算法，无论任务是找到全局最大值或找到最适合的状态在给定的运行时间。
英文摘要：
Biological and cultural inspired optimization algorithms are nowadays part of the basic toolkit of a great many research domains. By mimicking processes in nature and animal societies, these general-purpose search algorithms promise to deliver optimal or near-optimal solutions using hardly any information on the optimization problems they are set to tackle. Here we study the performances of a cultural-inspired algorithm -- the imitative learning search -- as well as of asexual and sexual variants of evolutionary algorithms in finding the global maxima of NK-fitness landscapes. The main performance measure is the total number of agent updates required by the algorithms to find those global maxima and the baseline performance, which establishes the effectiveness of the cooperative algorithms, is set by the blind search in which the agents explore the problem space (binary strings) by flipping bits at random. We find that even for smooth landscapes that exhibit a single maximum, the evolutionary algorithms do not perform much better than the blind search due to the stochastic effects of the genetic roulette. The imitative learning is immune to this effect thanks to the deterministic choice of the fittest string in the population, which is used as a model for imitation. The tradeoff is that for rugged landscapes the imitative learning search is more prone to be trapped in local maxima than the evolutionary algorithms. In fact, in the case of rugged landscapes with a mild density of local maxima, the blind search either beats or matches the cooperative algorithms regardless of whether the task is to find the global maximum or to find the fittest state within a given runtime.
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