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Genetic Algorithms (GAs) and CMA Evolutionary Strategy to Optimize Electronic Nose Sensor Selection

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Paper number  066120,  2006 ASAE Annual Meeting . (doi: 10.13031/2013.21505) @2006
Authors:   Changying Li, Paul Heinemann, Patrick Reed
Keywords:   electronic nose, sensor selection, genetic algorithms, covariance matrix adaptation evolutionary strategy, optimization, apple quality and safety

The high dimensionality of electronic nose data increases the difficulty of their use in classification models. Reducing this high dimensionality helps reduce variable numbers, improve classification accuracy, and reduce computation time and sensor cost. In this research, the Cyranose 320 electronic nose, which was used for apple defect detection, was optimized by selecting only the most relevant of its internal 32 sensors using different selection methods. Two robust heuristic optimization algorithms, genetic algorithm (GA) and covariance matrix adaptation evolutionary strategy (CMAES), were applied and compared. Although both algorithms searched the optimal sensors resulting in a best classification error rate of 4.4%, the average classification error rate of CMA over 30 random seed runs was 5.0% (s.d.=0.006) which was better than 5.2% (s.d.=0.004) from the GA. The final optimal solution sets obtained by integer GA showed that including more sensors did not guarantee better classification performance. The best reduction in classification error rate was 10% while the number of sensors was reduced 78%. This study provided a robust and efficient optimization approach to reduce high data dimensionality of the electronic nose data, which substantially improved electronic nose performance in apple defect detection while potentially reducing the overall electronic nose cost for future specific applications.

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