While the main motivation for Parallel Genetic Algorithms (PGAs) has been to improve the scalability of Genetic Algorithms (GAs), techniques and strategies for maintaining population diversity is an equally active research topic. Island Model Genetic Algorithms (IMGAs) represent one of the most mature strategies for developing PGAs in an effective and scalable manner. However, identifying how much migration and which individuals should migrate are open research problems. Meanwhile, recent developments in Adaptive Genetic Algorithms (AGAs) have led to techniques for monitoring and maintaining population diversity in an online manner. The aim of the present work is to introduce adaptive techniques and mechanisms into PGAs in order to determine when, how much and which individuals are most suitable for migration. We present a number of adaptive PGAs that aim to maintain diversity and maximise coverage of the solution space by minimising the overlap between islands. PGAs presented in this work are empirically assessed for their abilities in scalability, ability to find good quality solutions and maintain population diversity in ordered problems. These metrics are compared to existing adaptive and parallel GAs selected from the literature for their performance. We estimated the overhead costs of monitoring diversity and communication would result in a trade off between scalability and search capabilities. Our results suggest that an asynchronous adaptive PGA has the greatest speedup potential. However, while localising adaptive populations by $k$-means clustering is less scalable, results indicate that the method is more effective at directing the search in order to reduce the likelihood of islands searching in the same areas of the solution space. For this reason, an adaptive PGA with clustering-based migration demonstrates greater potential in solution quality while maintaining good speedup performance.

尽管并行遗传算法（PGA）的主要动机是提高遗传算法（GA）的可扩展性，但保持种群多样性的技术和策略也是同样活跃的研究主题。岛模型遗传算法（IMGA）代表了以有效且可扩展的方式开发PGA的最成熟的策略之一。但是，确定多少移民以及哪些人应该移民是悬而未决的研究问题。同时，自适应遗传算法（AGA）的最新发展已导致以在线方式监视和维护种群多样性的技术。本工作的目的是将自适应技术和机制引入PGA，以确定何时，多少以及最适合迁移的人员。我们提出了许多自适应PGA，旨在通过最小化岛之间的重叠来保持多样性并最大化解决方案空间的覆盖范围。根据经验评估了这项工作中提出的PGA的可扩展性能力，找到优质解决方案的能力以及在有序问题中保持种群多样性的能力。将这些指标与从文献中选择的现有自适应和并行GA进行比较，以了解其性能。我们估计监视多样性和通信的间接费用将导致可扩展性和搜索功能之间的权衡。我们的结果表明，异步自适应PGA具有最大的加速潜力。但是，在通过 根据经验评估了这项工作中提出的PGA的可扩展性能力，找到优质解决方案的能力以及在有序问题中保持种群多样性的能力。将这些指标与从文献中选择的现有自适应和并行GA进行比较，以了解其性能。我们估计监视多样性和通信的间接费用将导致可扩展性和搜索功能之间的权衡。我们的结果表明，异步自适应PGA具有最大的加速潜力。但是，在通过 根据经验评估了这项工作中提出的PGA的可扩展性能力，找到优质解决方案的能力以及在有序问题中保持种群多样性的能力。将这些指标与从文献中选择的现有自适应和并行GA进行比较，以了解其性能。我们估计监视多样性和通信的间接费用将导致可扩展性和搜索功能之间的权衡。我们的结果表明，异步自适应PGA具有最大的加速潜力。但是，在通过 将这些指标与从文献中选择的现有自适应和并行GA进行比较，以了解其性能。我们估计监视多样性和通信的间接费用将导致可扩展性和搜索功能之间的权衡。我们的结果表明，异步自适应PGA具有最大的加速潜力。但是，在通过 将这些指标与从文献中选择的现有自适应和并行GA进行比较，以了解其性能。我们估计监视多样性和通信的间接费用将导致可扩展性和搜索功能之间的权衡。我们的结果表明，异步自适应PGA具有最大的加速潜力。但是，在通过$ķ$-means聚类的可伸缩性较差，结果表明该方法在指导搜索方面更有效，以减少在解决方案空间的相同区域中进行孤岛搜索的可能性。因此，具有基于群集的迁移的自适应PGA在保持良好的加速性能的同时，也显示出解决方案质量的更大潜力。