CI-EN: Enhancement of ECJ, A High-Performance Community Metaheuristics Library for Stochastic Optimization Research

CI-EN：ECJ 的增强，用于随机优化研究的高性能社区元启发式库

• 批准号: 1629850
• 负责人: Sean Luke
• 金额: $ 47.5万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629850&HistoricalAwards=false
• 关键词: CI; EN; Enhancement; ECJ; Performance

This grant funds the transformation of the popular ECJ software library into a general-purpose software tool for performing so-called "metaheuristic" optimization. Metaheuristics are methods for coming up with optimal (or close to optimal) solutions to hard problems, and particularly ones for which there are no straightforward ways to find answers. For example, imagine if we wanted to find the best walking behavior for a robot, but didn't really know how to program one, we could instead "evolve" a solution by trying many arbitrary behaviors in a simulator, then selecting the best-performing ones and breeding them to produce a new generation, then testing them in the simulator, and so on. This approach, a form of genetic programming, is an example of a metaheuristic. Metaheuristics are widely used in science and engineering for everything from robot design to protein folding to simulation of migration patterns. Metaheuristics are also popular in teaching computer science as many of them are closely related to similar notions drawn from genetics and population biology, swarms, human organizational behavior, and physics. While ECJ is particularly popular for genetic programming and related methods, there is no standard community tool for metaheuristics as a whole. This grant will enable development of such a tool. This would give students and researchers alike access to a broad range of metaheuristics algorithms to compare and use, make those algorithms usable in massively parallel environments, and provide a centralized environment for community contribution.Metaheuristics are stochastic optimization techniques in wide use for those classes of problems, such as the parameters of complex simulations, for which there is no "principled" optimization approach. ECJ is a popular tool certain subfamilies of metaheuristics, such as genetic programming and genetic algorithms, which have dominated much of the early research in the area. But much recent metaheuristics research has branched out to areas such as multiobjective optimization, combinatorial optimization, constrained methods, model-fitting, and so on, and has led to a host of new techniques. These techniques have a high degree of commonality, and the metheuristics community often seeks to "mix and match" elements from these techniques in order to customize methods for certain problems, and also to compare them against one another. However there is at present no popular common library to make this possible. This proposal aims to extend and enhance ECJ into a community metaheuristics toolkit for both researchers and educators. Enhancements will include: a testing harness and various testing facilities; new metaheuristics in single-state optimization, hybrid architectures, NEAT, combinatorial optimization, and model-fitting optimization; GUI and Eclipse environments; statistics utilities; and new benchmark problems for testing metaheuristics methods. The work will be done in conjunction with the metheuristics research community and also with teachers from high schools in the Washington D.C. area to assess the utility of the tool in STEM education.

该基金资助将流行的ECJ软件库转变为通用软件工具，用于执行所谓的“元启发式”优化。 元分析是为困难问题提出最佳（或接近最佳）解决方案的方法，特别是那些没有直接方法找到答案的问题。 例如，想象一下，如果我们想为机器人找到最佳的行走行为，但不知道如何编程，我们可以通过在模拟器中尝试许多任意行为来“进化”解决方案，然后选择表现最好的行为并繁殖它们以产生新一代，然后在模拟器中测试它们，等等。这种方法是遗传编程的一种形式，是元启发式的一个例子。 元分析广泛应用于科学和工程领域，从机器人设计到蛋白质折叠，再到迁移模式的模拟。 元分析学在计算机科学教学中也很受欢迎，因为它们中的许多与遗传学和种群生物学、群体、人类组织行为和物理学中的类似概念密切相关。 虽然ECJ在遗传编程和相关方法方面特别受欢迎，但作为一个整体，元遗传学并没有标准的社区工具。 这笔赠款将有助于开发这样一个工具。 这将使学生和研究人员能够获得广泛的元分析算法进行比较和使用，使这些算法可用于大规模并行环境，并为社区贡献提供一个集中的环境。元分析是广泛用于那些问题的随机优化技术，例如复杂模拟的参数，没有“原则性”优化方法。 ECJ是一种流行的工具，某些亚家族的元算法，如遗传编程和遗传算法，这已经主导了许多早期的研究在该地区。 但是最近的元分析学研究已经扩展到多目标优化、组合优化、约束方法、模型拟合等领域，并产生了许多新技术。 这些技术具有高度的通用性，metheuristics社区经常寻求从这些技术中“混合和匹配”元素，以便为某些问题定制方法，并将它们相互比较。 然而，目前还没有流行的公共图书馆使这成为可能。 该提案旨在扩展和增强ECJ，使其成为研究人员和教育工作者的社区元分析工具包。 增强功能将包括：测试工具和各种测试工具;单态优化、混合架构、NEAT、组合优化和模型拟合优化中的新元分析; GUI和Eclipse环境;统计实用程序;以及用于测试元分析方法的新基准问题。这项工作将与metheuristics研究社区以及华盛顿地区高中的教师一起完成，以评估该工具在STEM教育中的效用。