论文标题
双线性功能最大化优化的竞争共同进化算法的运行时分析
Runtime Analysis of Competitive co-Evolutionary Algorithms for Maximin Optimisation of a Bilinear Function
论文作者
论文摘要
共同进化算法具有广泛的应用程序，例如在硬件设计中，棋盘游戏策略的演变以及修补软件错误。但是，这些算法知之甚少，应用通常受到病理行为的限制，例如梯度丧失，相对过度属性和平庸的客观性停滞。开发一种可以预测共同进化算法有效和可靠的解决方案的理论是一个开放的挑战。 本文为开发基于人群的竞争共同进化算法的运行时分析提供了第一步。我们提供了一个数学框架，用于描述和推理共同进化过程的性能。框架的示例应用显示了一个方案，其中简单的共同进化算法在多项式预期时间中获得了解决方案。最后，我们描述了共同进化算法需要指数时间的设置，并以压倒性的高可能性获得解决方案。
Co-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching software bugs. However, these algorithms are poorly understood and applications are often limited by pathological behaviour, such as loss of gradient, relative over-generalisation, and mediocre objective stasis. It is an open challenge to develop a theory that can predict when co-evolutionary algorithms find solutions efficiently and reliable. This paper provides a first step in developing runtime analysis for population-based competitive co-evolutionary algorithms. We provide a mathematical framework for describing and reasoning about the performance of co-evolutionary processes. An example application of the framework shows a scenario where a simple co-evolutionary algorithm obtains a solution in polynomial expected time. Finally, we describe settings where the co-evolutionary algorithm needs exponential time with overwhelmingly high probability to obtain a solution.
