Development of Control Signatures with a Hybrid Data Mining and Genetic Algorithm

Development of Control Signatures with a Hybrid Data Mining and Genetic Algorithm

Alex Burns (The University of Iowa, USA), Shital Shah (Rush University Medical Center, Health Systems Management, USA) and Andrew Kusiak (The University of Iowa, USA)
DOI: 10.4018/978-1-59904-951-9.ch131
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This paper presents a hybrid approach that integrates a genetic algorithm (GA) and data mining to produce control signatures. The control signatures define the best parameter intervals leading to a desired outcome. This hybrid method integrates multiple rule sets generated by a data mining algorithm with the fitness function of a GA. The solutions of the GA represent intersections among rules providing tight parameter bounds. The integration of intuitive rules provides an explanation for each generated control setting and it provides insights into the decision making process. The ability to analyze parameter trends and the feasible solutions generated by the GA with respect to the outcomes is another benefit of the proposed hybrid method. The presented approach for deriving control signatures is applicable to various domains, such as energy, medical protocols, manufacturing, airline operations, customer service, and so on. Control signatures were developed and tested for control of a power plant boiler. These signatures discovered insightful relationships among parameters. The results and benefits of the proposed method for the power plant boiler are discussed in the paper.

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