Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Random Data: Analysis and Measurement Procedures
Random Data: Analysis and Measurement Procedures
| Hi-index | 0.00 |
This paper is described the analysis of fatigue road loading using the genetic algorithm approaches. This approach is based on a partitional clustering. A new method for temporal pattern matching of a time series is developed using pattern wavelets and genetic algorithms. This method is used to clustering the data into a sequence of a nested partition. Fatigue damage cumulating is a random variable in essence. It is caused by variable amplitude loading. The randomness comes from the loading process and fatigue resistance of material. This modulation is developed as a mathematical method that can be implemented directly into existing evolutionary algorithms without writing special operators and changing the program loop. This article is presented in order to solve some theoretical and practical issues in evolutionary algorithms like numerical bounded variables, dynamic focalized search, dynamic control of diversity and feasible region analysis. Finally it is suggested that the genetic algorithm approach provided a good platform for analyzing the time series in the aspect of the durability research.