The Effect of Entropy on the Performance of Modified Genetic Algorithm Using Earthquake and Wind Time Series

The dynamic complexity of time series of natural phenomena allowed to improve the performance of the genetic algorithm to optimize the test mathematical functions. The initial populations of stochastic origin of the genetic algorithm were replaced using the series of time of winds and earthquakes. T...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2018-01, Vol.2018 (2018), p.1-13
Main Authors: Gutierrez, Sebastian, Gatica, Gustavo, Alfaro, Miguel, Fuertes, Guillermo, Vargas, Manuel, Peralta, María
Format: Article
Language:English
Subjects:
Adaptation
Algorithms
Analysis
Chaos theory
Chile
Cooperative learning
Determinism
Earthquakes
Engineering
Entropy
Entropy (Information theory)
Fuzzy logic
Genetic algorithms
Genetic research
Imports
Laws, regulations, etc
Mathematical functions
Mathematical models
Mathematical problems
Methods
Mutation
Optimization
Performance enhancement
Populations
Seismology
Time series
Wind
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Summary:The dynamic complexity of time series of natural phenomena allowed to improve the performance of the genetic algorithm to optimize the test mathematical functions. The initial populations of stochastic origin of the genetic algorithm were replaced using the series of time of winds and earthquakes. The determinism of the time series brings in more information in the search of the global optimum of the functions, achieving reductions of time and an improvement of the results. The information of the initial populations was measured using the entropy of Shannon and allowed to establish the importance of the entropy in the initial populations and its relation with getting better results. This research establishes a new methodology for using determinism time series to search the best performance of the models of optimization of genetic algorithms (GA).
ISSN:1076-2787
1099-0526
DOI:10.1155/2018/4392036