The Research of the Genetic Algorithm Combined with Chromosome Fitness to Optimize the Flatness Error Evaluation

Abstract:

This paper suggests an improved genetic algorithm to seek the minimum range value in the ideal-plane flatness measurement. This algorithm increases measurement accuracy by using dynamic cross factor, mutation factor and a new concept called chromosome fitness. It was proved in simulation experiments that its accuracy is better than other flatness error evaluating algorithms like the minimal territory evaluating algorithm and the computational geometry algorithm etc. So it can be used for measuring industrial production components error and verifying assumed models in reverse engineering etc.

• Periodical: Applied Mechanics and Materials (Volumes 278-280)
• Pages: 1342-1348
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.278-280.1342
• Online since: January 2013
• Authors: Ying Di Guo, Wei Cong Wu, Mi Jia Jiang, Bo Nan Li, Bing Bing Fang, Xing Gao
• Keywords: Chromosome Fitness, Flatness Measurement, Genetic Algorithm (GA), Smart Evaluation of Flatness

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DOI: 10.3788/ope.20122002.0422

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