Improving Heuristic Search by Machine Learning

探索和利用是（Meta）启发式优化中的两个基本概念。令人惊讶的是，缺乏正式的定义阻碍了对它们进行定量测量和分析的能力。我最近的研究为这些概念提供了正式的定义。根据这些定义，可以说明同时勘探和开采的消极后果。我们还证明了元启发式的选择如何偏向探索，促进失败的探索。这些发现是我们在过去几年中开发的一系列成功算法和多样化技术的基础（领导者和追随者，最小人口收敛和最小人口搜索）。它还激发了一种新的优化方法，该方法基于尽可能多地分离两个过程的想法。这种方法的最终目标是设计一个只探索只开发混合（EEH）。 在这种混合的关键挑战是设计的探索只的方法。局限性之一在于难以衡量探索的有效性。我们的定义形式化了这些概念，但几乎没有函数可以以实际的方式测量探索。因此，“勘探基准”的设计是一个需要实现的基本中间目标。探索基准和EEH方法为通过机器学习改进元分析提供了令人兴奋的机会。测量大量函数的探索能力将允许训练深度学习模型来估计探索性解决方案的前景，从而避免失败的探索。为给定函数选择最佳探索策略并预测从探索到利用的最佳过渡点也是机器学习在这种新方法中的潜在应用。改进的元分析和相关的机器学习模型的发展提供了无数的应用机会;特别是因为这项研究特别关注大规模领域。我将扩展我以前在分子对接方面的经验，目的是将一个基于机器学习的优化器（专为这个问题设计）纳入Autodock工具。与UPEI和Somru BioScience Inc.的量子计算研究小组合作。我还将把这些算法应用于计算生物学和化学领域的各种优化和预测问题。

Exploration and exploitation are two fundamental concepts in (meta)heuristic optimization. Surprisingly the lack of a formal definition has hindered the ability to measure and analyze them quantitatively. My most recent research has provided formal definitions for these concepts. Based on these definitions it has been possible to show the negative consequences of concurrent exploration and exploitation. We have also evidenced how selection in metaheuristic biases exploration, promoting failed exploration. These discoveries have been the base of a series of successful algorithms and diversifications techniques that we have developed during the past years (Leaders and Followers, Thresheld Convergence and Minimum Population Search). It has also motivated a new approach to optimization based on the idea of separating as much as possible both processes. The ultimate goal of this approach is the design of an exploration-only exploitation-only hybrid (EEH). The key challenge in such hybrid is the design of the exploration-only method. One of the limitations resides in the difficulty of measuring the effectiveness of exploration. Our definitions formalize these concepts, but there are few functions that allow measuring exploration in a practical way. Therefore the design of an "exploration benchmark" constitutes a fundamental intermediate goal to be achieved. The exploration benchmark and the EEH approach present exciting opportunities for the improvement of metaheuristics through machine learning. The ability to measure exploration on a broad number of functions will allow training deep learning models to estimate how promising an exploratory solution is, and thus avoid failed exploration. Selecting the best exploration strategy for a given function and predicting the optimum transition point from exploration to exploitation are also potential applications of machine learning into this new approach. The development of improved metaheuristics and the associated machine learning models provide a myriad of application opportunities; especially since this research is specifically focused on large scale domains. I will extend my previous experience in molecular docking with the aim of including a machine learning based optimizer, specifically designed for this problem, into the Autodock tool. In collaboration with the Quantum Computing research group at UPEI and Somru BioScience Inc. I will also apply these algorithms to a diversity of optimization and prediction problems in the fields of computational biology and chemistry.