• Publications
  • Influence
Evolutionary programming made faster
TLDR
A "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator and is proposed and tested empirically, showing that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.
Stochastic ranking for constrained evolutionary optimization
  • T. Runarsson, X. Yao
  • Computer Science, Mathematics
    IEEE Trans. Evol. Comput.
  • 1 September 2000
TLDR
A novel approach to balance objective and penalty functions stochastically, i.e., stochastic ranking, is introduced, and a new view on penalty function methods in terms of the dominance of penalty and objective functions is presented.
Evolving artificial neural networks
  • X. Yao
  • Computer Science
  • 1 September 1999
TLDR
It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.
A new evolutionary system for evolving artificial neural networks
  • X. Yao, Yong Liu
  • Computer Science, Medicine
    IEEE Trans. Neural Networks
  • 1 May 1997
TLDR
The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms, and has been tested on a number of benchmark problems in machine learning and ANNs.
Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization
TLDR
An automatic decomposition strategy called differential grouping is proposed that can uncover the underlying interaction structure of the decision variables and form subcomponents such that the interdependence between them is kept to a minimum and greatly improve the solution quality on large-scale global optimization problems.
Evolving Artificial Neural Networks
Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANN’s) in recent years. This
Ensemble learning via negative correlation
Diversity analysis on imbalanced data sets by using ensemble models
  • Shuo Wang, X. Yao
  • Computer Science
    IEEE Symposium on Computational Intelligence and…
  • 15 May 2009
TLDR
This paper explores the impact of diversity on each class and overall performance and improves SMOTE in a novel way for solving multi-class data sets in ensemble model - SMOTEBagging.
MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
TLDR
A new method, called Majority Weighted Minority Oversampling TEchnique (MWMOTE), is presented for efficiently handling imbalanced learning problems and is better than or comparable with some other existing methods in terms of various assessment metrics.
Cooperatively Coevolving Particle Swarms for Large Scale Optimization
  • Xiaodong Li, X. Yao
  • Mathematics, Computer Science
    IEEE Transactions on Evolutionary Computation
  • 1 April 2012
TLDR
The experimental results and analysis suggest that CCPSO2 is a highly competitive optimization algorithm for solving large-scale and complex multimodal optimization problems.
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