Closed-Loop Evolutionary Multiobjective Optimization
Closed-Loop Evolutionary Multiobjective Optimization
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DOI：
10.1109/mci.2009.933095
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发表时间：
2009-08-01
影响因子：
9
通讯作者：
Knowles, Joshua
中科院分区：
文献类型：
--
作者：
Knowles, Joshua
人工进化作为一种优化方法在工程、运筹学和计算智能领域已有50多年的历史。在闭环进化(统计学家George Box使用的术语)或相当于进化实验(Ingo Rechenberg的术语)中，通过进行物理实验在现实世界中评估“表型”，同时模拟选择和繁殖。著名的人工进化早期工作--流体力学设计工程问题和化工厂工艺优化--就是在这种实验模式下进行的。最近，闭环方法已经成功地应用于许多进化硬件和进化机器人研究，以及一些微生物学和生物化学应用。在这篇文章中，进一步考虑了闭环系统进化和多目标优化的几个新目标。来自我自己的合作工作的四个案例研究描述了：(I)分析生物化学中的仪器优化；(Ii)在体外寻找有效的药物组合；(Iii)芯片上合成生物分子设计；以及(Iv)改进巧克力生产工艺。由于复杂性或缺乏足够的分析模型，在这些应用中不可能进行准确的模拟。在讨论的这些应用程序和其他应用程序中，实验优化带来了几个挑战：噪声、滋扰因素、短暂的昂贵评估，以及必须以(大)批处理的评估。进化算法在很大程度上等同于这些变幻莫测的算法，而现代多目标进化算法也使人们能够在相互冲突的优化目标之间进行权衡。然而，其他学科的原理，如统计学、实验设计、机器学习和全局优化，也与闭环系统问题的各个方面相关，并可能激励多目标进化算法的进一步发展。
Artificial evolution has been used for more than 50 years as a method of optimization in engineering, operations research and computational intelligence. In closed-loop evolution (a term used by the statistician, George Box) or, equivalently, evolutionary experimentation (Ingo Rechenberg's terminology), the "phenotypes" are evaluated in the real world by conducting a physical experiment, whilst selection and breeding is simulated. Well-known early work on artificial evolution-design engineering problems in fluid dynamics, and chemical plant process optimization-was carried out in this experimental mode. More recently, the closed-loop approach has been successfully used in much evolvable hardware and evolutionary robotics research, and in some microbiology and biochemistry applications. In this article, several further new targets for closed-loop evolutionary and multiobjective optimization are considered. Four case studies from my own collaborative work ire described: (i) instrument optimization in analytical biochemistry; (ii) finding effective drug combinations in vitro; (iii) on-chip synthetic biomolecule design; and (iv) improving chocolate production processes. Accurate simulation in these applications is not possible due to complexity or a lack of adequate analytical models. In these and other applications discussed, optimizing experimentally brings with it several challenges: noise; nuisance factors; ephemeral expensive evaluations, and evaluations that must be done in (large) batches. Evolutionary algorithms (EAs) are largely equal to these vagaries, whilst modern multiobjective EAs also enable tradeoffs among conflicting optimization goals to be explored. Nevertheless, principles from other disciplines, such as statistics, Design of Experiments, machine learning and global optimization are also relevant to aspects of the closed-loop problem, and may inspire further development of multiobjective EAs.