Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Implementing discrete mathematics: combinatorics and graph theory with Mathematica
Implementing discrete mathematics: combinatorics and graph theory with Mathematica
Digital Image Processing
Performance Comparison of Two Evolutionary Schemes
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Multicriteria Optimization
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Comparing evolutionary algorithms on binary constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
The edge-window-decoder representation for tree-based problems
IEEE Transactions on Evolutionary Computation
A new memory based variable-length encoding genetic algorithm for multiobjective optimization
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
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This paper presents an enhanced approach for comparing evolutionary algorithm. This approach is based on three statistical techniques: (a) Principal Component Analysis, which is used to make the data uncorrelated; (b) Bootstrapping, which is employed to build the probability distribution function of the merit functions; and (c) Stochastic Dominance Analysis, that is employed to make possible the comparison between two or more probability distribution functions. Since the approach proposed here is not based on parametric properties, it can be applied to compare any kind of quantity, regardless the probability distribution function. The results achieved by the proposed approach have provided more supported decisions than former approaches, when applied to the same problems.