An introduction to genetic algorithms
An introduction to genetic algorithms
Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Genetic algorithms-based symbolic aggregate approximation
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
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The differential evolution (DE) is a very powerful search method for solving many optimization problems. In this paper we present a new scheme (DESAX) based on the differential evolution to localize the breakpoints utilized with the symbolic aggregate approximation method; one of the most important symbolic representation techniques for times series data. We compare the new scheme with a previous one (GASAX), which is based on the genetic algorithms, and we show how the new scheme outperforms the original one. We also show how (DESAX) can be used for the symbolic aggregate approximation of non-normalized time series.