Some practical aspects of exploratory projection pursuit
SIAM Journal on Scientific Computing
Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Journal of Global Optimization
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Computational Statistics Handbook with MATLAB, Second Edition (Chapman & Hall/Crc Computer Science & Data Analysis)
Particle swarm optimisation with spatial particle extension
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Extending particle swarm optimisers with self-organized criticality
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
A projection pursuit algorithm for anomaly detection in hyperspectral imagery
Pattern Recognition
Interactive and Dynamic Graphics for Data Analysis With R and GGobi
Interactive and Dynamic Graphics for Data Analysis With R and GGobi
A new stochastic approach for solution of Riccati differential equation of fractional order
Annals of Mathematics and Artificial Intelligence
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Exploratory Projection Pursuit (EPP) methods have been developed thirty years ago in the context of exploratory analysis of large data sets. These methods consist in looking for low-dimensional projections that reveal some interesting structure existing in the data set but not visible in high dimension. Each projection is associated with a real valued index which optima correspond to valuable projections. Several EPP indices have been proposed in the statistics literature but the main problem lies in their optimization. In the present paper, we propose to apply Genetic Algorithms (GA) and recent Particle Swarm Optimization (PSO) algorithm to the optimization of several projection pursuit indices. We explain how the EPP methods can be implemented in order to become an efficient and powerful tool for the statistician. We illustrate our proposal on several simulated and real data sets.