Closed-Loop Evolutionary Multiobjective Optimization

doi:10.1109/MCI.2009.933095

Closed-Loop Evolutionary Multiobjective Optimization

2009-08-01

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Joshua Knowles *

DOI：10.1109/MCI.2009.933095

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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.

Keywords:

FLIGHT MASS-SPECTROMETRY

KEY AROMA COMPOUNDS

GLOBAL OPTIMIZATION

GENETIC ALGORITHM

PERFORMANCE

EXPERIMENTATION

METAHEURISTICS

Closed-Loop Evolutionary Multiobjective Optimization

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