论文标题
协同进化的帕累托多样性优化
Coevolutionary Pareto Diversity Optimization
论文作者
论文摘要
为给定优化问题计算各种高质量解决方案集已成为近年来的重要主题。在本文中，我们介绍了一种协同进化的帕累托多样性优化方法，该方法基于将约束的单目标优化问题重新制定为双向目标问题，它通过将约束变成额外的目标来重新制定为双向目标问题。我们的新帕累托多样性优化方法使用这种双向目标来优化问题，同时还维持了额外的高质量解决方案，该解决方案涉及对给定多样性度量进行优化的多样性。我们表明，我们的标准共同进化性帕累托多样性优化方法优于最近引入的分区算法，该算法通过概括性多样化贪婪的采样并提高了后来解决方案集的多样性，从而获得了初始人口。此外，我们研究了帕累托多样性优化方法的可能改进。特别是，我们表明使用间交叉的使用进一步提高了解决方案集的多样性。
Computing diverse sets of high quality solutions for a given optimization problem has become an important topic in recent years. In this paper, we introduce a coevolutionary Pareto Diversity Optimization approach which builds on the success of reformulating a constrained single-objective optimization problem as a bi-objective problem by turning the constraint into an additional objective. Our new Pareto Diversity optimization approach uses this bi-objective formulation to optimize the problem while also maintaining an additional population of high quality solutions for which diversity is optimized with respect to a given diversity measure. We show that our standard co-evolutionary Pareto Diversity Optimization approach outperforms the recently introduced DIVEA algorithm which obtains its initial population by generalized diversifying greedy sampling and improving the diversity of the set of solutions afterwards. Furthermore, we study possible improvements of the Pareto Diversity Optimization approach. In particular, we show that the use of inter-population crossover further improves the diversity of the set of solutions.
