FAIME: A Feature based Framework to Automatically Integrate and Improve Metaheuristics via Examples.

给定一些示例问题，FAIME将通过修改其源代码来设计定制的元分析。与人类设计的元计算相比，这些将更好，更快，更强大。试点研究表明，它们将比人类设计的元生态学表现得更好，因为我们正在使用它们作为起点。它们的设计速度将比手动设计过程更快。我们将能够对它们的性质作出更有力的统计说明。简而言之，我们将采用机器学习方法来设计元算法，探索如何使用特征进行预测，以及如何有效地设计给定问题特征的元算法。 计算问题有三种广泛类型;容易（易于处理）、困难（难以处理）和不可能（不可计算）。这些问题之所以具有挑战性，纯粹是因为可能的解决方案数量庞大，尝试每一种可能性都需要不可行的时间。元分析处理困难的问题，通过对可能性的一小部分进行采样来缓解这个问题。一个缺点是没有性能保证。FAIME将检查自动设计元启发式的有效性，沿着元启发式的关键性能指标，如可扩展性，公差，鲁棒性，收敛性。有问题，我们计划直接解决元启发式研究目前的做法。这些包括以下几点：-有一些原则，以协助设计的metabolistics，使其成为一个试验和错误的过程。基于特征的方法将提供一个解决这个问题的框架。- -出版的元哲学著作数量激增，尤其是受到隐喻启发的著作，但几乎没有可供选择的指导方针。基于特征的方法可以提供一个基础，提供一些指导。元分析学的设计往往与它们所要解决的问题无关。FAIME将使用来自示例问题的反馈来通知设计过程。此外，手动设计元分析也有其局限性。虽然它确实产生了性能更好的元分析，但它无助于理解如何产生更好的元分析。FAIME通过自动生成元分类，然后使用功能来提供分类框架，从而为理解其设计提供基础。 这个FAIME项目将使用一个由两个级别组成的工厂自动设计元算法，一个生成级别和一个生成和测试元算法的测试级别。将通过修改源代码生成元代码，然后在一组问题上进行测试。我们将-调查的属性的metasteristics（可扩展性，公差，鲁棒性，收敛）。采用一种新的技术，采用现有的源代码并对其进行改进。可以在并行机器上实现，这非常适合设计过程。利用可计算性理论、概率论和其他最近的数学结果来通知设计过程和基于特征的方法。2我们还没有一个有效的元分析和问题分类方案。一个反映被分类实体之间关系的分类方案会带来清晰度，并允许进行预测。使用基于特征的分类方案，我们将研究我们可以对元分析和问题做出的预测的准确性。我们的目标是能够对元启发式算法的性能进行统计陈述，例如，元启发式算法H将在Z时间单位内提供质量X单位的解决方案，公差+/- Y单位，从而提供一些性能指标以及为哪个问题选择哪个元启发式算法。

Given a number of example problems, FAIME will design metaheuristics tailored by modifying their source code. Compared to human designed metaheuristics, these will be better, faster and stronger. Pilot studies indicate they will perform better than human designed metaheuristics, as we are using them as a starting point. They will be designed faster than via the manual design process. We will able to make stronger statistical statements about their properties. In short, we will employ a machine learning approach to design metaheuristics, explore how features can be used to make predictions, and how effectively we can design metaheuristics given a problem's features. There are three broad types of computational problems; easy (tractable), hard (intractable), and impossible (incomputable). These problems are challenging purely because of the astronomical number of possible solutions and trying every possibility would require an infeasible amount of time. Metaheuristics tackle the hard problems, mitigating this issue by sampling a tiny subset of the possibilities. One drawback is that there is no guarantee of performance. FAIME will examine the efficacy of automatically designing metaheuristics, along with key performance indicators of the metaheuristics, such as scalability, tolerance, robustness, convergence.There are issues with current practices in metaheuristic research which we plan to tackle directly. These include the following:-There are few principles to assist with the design of metaheuristics, making it a trial and error process. A feature based approach will provide a framework against which to address this. -There is an explosion in the number of published metaheuristics, in particular metaphorically inspired ones, with few guidelines to select among them. A feature based approach can provide a basis offering some guidance. -Metaheuristics are often designed in isolation from the problems they will be applied to. FAIME will use feedback from the example problems to inform the design process. Also, manually designing metaheuristics has its limitations. While it does produce better performing metaheuristics, it does not contribute to an understanding of how to produce better metaheuristics. FAIME drives deeper by automatically generating metaheuristics and then using features to provide a framework for classification, providing a basis to understand their design. This FAIME project will-automatically designs metaheuristics for example problems using a factory consisting of two levels, a generating-level and a test-level which generates and tests metaheuristics. The metaheuristics will be generated by modifying their source code, and then tested on a set of problems. We will -investigate the properties of the metaheuristics (scalability, tolerance, robustness, convergence).-employ a recent technique which takes existing source code and improves it.-be implemented on parallel machines, which are ideally suited to the design process.-draw on computability theory, probability theory, and other recent mathematical results to inform the design process and feature based approach.We do not yet have an effective classification scheme for metaheuristics and problems. A classification scheme which reflects the relationships of entities being classified brings clarity and allows predictions to be made. Using the feature based classification scheme, we will investigate the accuracy of predictions we can make about metaheuristics and problems. We aim to be able to make statistical statements about the performance of metaheuristics such as, metaheuristic H will deliver a solution of quality X units, with tolerance +/- Y units, within Z time units, thus offering some indication of performance and which metaheuristic to choose for which problem.