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

这笔赠款用于将流行的ECJ软件库转变为用于执行所谓的“元启发式”优化的通用软件工具。元启发式是为困难问题提供最优(或接近最优)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，如果我们想要为机器人找到最佳行走行为，但并不真正知道如何编程，我们可以通过在模拟器中尝试许多任意行为，然后选择表现最好的行为并培育它们来产生新一代，然后在模拟器中测试它们，从而“进化”出一个解决方案，依此类推。这种方法是遗传编程的一种形式，是元启发式的一个例子。元启发式被广泛应用于科学和工程中，从机器人设计到蛋白质折叠再到迁移模式的模拟，无所不包。元启发式在计算机科学教学中也很受欢迎，因为它们中的许多都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特别受欢迎的是遗传编程和相关方法，但作为一个整体，还没有标准的社区工具用于元启发式。这笔赠款将使开发这样一个工具成为可能。这将为学生和研究人员提供广泛的元启发式算法以供比较和使用，使这些算法在大规模并行环境中可用，并为社区贡献提供一个集中的环境。元启发式是一种广泛使用的随机优化技术，用于解决这类问题，如复杂模拟的参数，对于这些问题，没有“原则性”的优化方法。ECJ是一种流行的工具，它是元启发式的某些子家族，如遗传编程和遗传算法，它们主导了该领域的大部分早期研究。但最近的元启发式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共性，计量学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，目前还没有流行的通用库来实现这一点。这项提议旨在将ECJ扩展和增强为社区元启发式工具包，供研究人员和教育工作者使用。增强将包括：测试工具和各种测试设施；单态优化、混合架构、整齐、组合优化和模型匹配优化中的新的元启发式；图形用户界面和Eclipse环境；统计实用程序；以及用于测试元启发式方法的新基准问题。这项工作将与计量学研究社区以及华盛顿特区地区的高中教师一起完成，以评估该工具在STEM教育中的效用。

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.