GISMO: Genetic Improvement of Software for Multiple Objectives

GISMO：多目标软件的遗传改进

• 批准号: EP/I033688/1
• 负责人: Mark Harman
• 金额: $ 64.02万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FI033688%2F1
• 关键词: GISMO; Genetic; Improvement; Software; Multiple

Humans find it hard to develop systems that balance many competing and conflicting operational objectives. Even meeting a single objective requires automated support. For example, there has been a long and rich history of research into techniques to optimise compiled code size and speed. Unfortunately, speed and size are but two of many objectives that the next generation of software systems will have to meet. Emergent computing application paradigms require systems that are not only reliable, compact and fast, but also which optimise many different competing and conflicting objectives such as response time, throughput and consumption of resources. Humans cannot be expected to optimally balance these multiple competing constraints and may miss potentially valuable solutions. Techniques are therefore required that can either automatically create code that balances many conflicting objectives or that can provide support to the human who seeks to do so. The GISMO project seeks to do both. It will develop automated techniques to produce new versions of components of existing systems that meet newly defined objectives. After a period of running the old and new component in parallel, the programmer may decide to adopt the newly evolved component. However, the beauty of the GISMO approach is that it does not insist that the programmer must accept the evolved solution in order to be useful. The programmer can also use GISMO to explore the multi-objective candidate solution space, gaining insight into what can be achieved by balancing several competing constraints.

人类发现很难开发出能够平衡许多相互竞争和冲突的操作目标的系统。即使实现单一目标也需要自动化支持。例如，对于优化编译代码大小和速度的技术有着悠久而丰富的研究历史。不幸的是，速度和大小只是下一代软件系统必须满足的众多目标中的两个。新兴计算应用范例不仅需要可靠、紧凑和快速的系统，而且还可以优化许多不同的竞争和冲突目标，例如响应时间、吞吐量和资源消耗。不能指望人类能够最佳地平衡这些多重竞争约束，并且可能会错过潜在有价值的解决方案。因此，需要能够自动创建代码来平衡许多相互冲突的目标的技术，或者能够为寻求这样做的人提供支持的技术。 GISMO 项目力求做到这两点。它将开发自动化技术来生产现有系统组件的新版本，以满足新定义的目标。在并行运行新旧组件一段时间后，程序员可能会决定采用新演化的组件。然而，GISMO 方法的优点在于它并不坚持程序员必须接受演进的解决方案才能有用。程序员还可以使用 GISMO 探索多目标候选解决方案空间，深入了解通过平衡多个竞争约束可以实现的目标。

项目成果

Achievements, Open Problems and Challenges for Search Based Software Testing

• DOI: 10.1109/icst.2015.7102580
• 发表时间: 2015-04
• 期刊: 2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST)
• 作者: M. Harman;Yue Jia;Yuanyuan Zhang

Learning Combinatorial Interaction Test Generation Strategies Using Hyperheuristic Search

• DOI: 10.1109/icse.2015.71
• 发表时间: 2015-05
• 期刊: 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering
• 作者: Yue Jia;Myra B. Cohen;M. Harman;J. Petke

Optimizing Existing Software with Genetic Programming

• DOI: 10.1109/tevc.2013.2281544
• 发表时间: 2015-02-01
• 期刊: IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
• 影响因子: 14.3
• 作者: Langdon, William B.;Harman, Mark
• 通讯作者: Harman, Mark

Distilling GeneChips with GP on the emerald GPU supercomputer

在 Emerald GPU 超级计算机上使用 GP 提取 GeneChips

• DOI: 10.1145/2384697.2384699
• 发表时间: 2012
• 期刊: ACM SIGEVOlution
• 作者: Langdon W