Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Schemata, Distributions and Graphical Models in Evolutionary Optimization
Journal of Heuristics
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Extending Population-Based Incremental Learning to Continuous Search Spaces
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Telephone Network Traffic Overloading Diagnosis and Evolutionary Computation Techniques
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Enhancing the Performance of Maximum---Likelihood Gaussian EDAs Using Anticipated Mean Shift
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Preventing Premature Convergence in a Simple EDA Via Global Step Size Setting
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Stochastic Local Search Techniques with Unimodal Continuous Distributions: A Survey
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Evolutionary flexible neural networks for intrusion detection system
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
Variance scaling for EDAs revisited
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Optimizing continuous problems using estimation of distribution algorithm based on histogram model
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
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
Experimental comparison of six population-based algorithms for continuous black box optimization
Evolutionary Computation
An intelligent multi-restart memetic algorithm for box constrained global optimisation
Evolutionary Computation
Benchmarking parameter-free amalgam on functions with and without noise
Evolutionary Computation
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The direct application of statistics to stochastic optimization based on iterated density estimation has become more important and present in evolutionary computation over the last few years. The estimation of densities over selected samples and the sampling from the resulting distributions, is a combination of the recombination and mutation steps used in evolutionary algorithms. We introduce the framework named IDEA to formalize this notion. By combining continuous probability theory with techniques from existing algorithms, this framework allows us to define new continuous evolutionary optimization algorithms.