Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
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PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A well-conditioned estimator for large-dimensional covariance matrices
Journal of Multivariate Analysis
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Population-Based Continuous Optimization, Probabilistic Modelling and Mean Shift
Evolutionary Computation
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SDR: a better trigger for adaptive variance scaling in normal EDAs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Editorial: Special Issue on "Nature Inspired Problem-Solving"
Information Sciences: an International Journal
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EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Real options approach to evaluating genetic algorithms
Applied Soft Computing
Mutation Hopfield neural network and its applications
Information Sciences: an International Journal
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs
Information Sciences: an International Journal
Information Sciences: an International Journal
Beware the parameters: estimation of distribution algorithms applied to circles in a square packing
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
A Boltzmann based estimation of distribution algorithm
Information Sciences: an International Journal
Regularized continuous estimation of distribution algorithms
Applied Soft Computing
Towards large scale continuous EDA: a random matrix theory perspective
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
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Multivariate Gaussian models are widely adopted in continuous estimation of distribution algorithms (EDAs), and covariance matrix plays the essential role in guiding the evolution. In this paper, we propose a new framework for multivariate Gaussian based EDAs (MGEDAs), named eigen decomposition EDA (ED-EDA). Unlike classical EDAs, ED-EDA focuses on eigen analysis of the covariance matrix, and it explicitly tunes the eigenvalues. All existing MGEDAs can be unified within our ED-EDA framework by applying three different eigenvalue tuning strategies. The effects of eigenvalue on influencing the evolution are investigated through combining maximum likelihood estimates of Gaussian model with each of the eigenvalue tuning strategies in ED-EDA. In our experiments, proper eigenvalue tunings show high efficiency in solving problems with small population sizes, which are difficult for classical MGEDA adopting maximum likelihood estimates alone. Previously developed covariance matrix repairing (CMR) methods focusing on repairing computational errors of covariance matrix can be seen as a special eigenvalue tuning strategy. By using the ED-EDA framework, the computational time of CMR methods can be reduced from cubic to linear. Two new efficient CMR methods are proposed. Through explicitly tuning eigenvalues, ED-EDA provides a new approach to develop more efficient Gaussian based EDAs.