Randomized algorithms
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
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
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
Not all linear functions are equally difficult for the compact genetic algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Drift and Scaling in Estimation of Distribution Algorithms
Evolutionary Computation
Addressing sampling errors and diversity loss in UMDA
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Parameter cross-validation and early-stopping in univariate marginal distribution algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A diversity maintaining population-based incremental learning algorithm
Information Sciences: an International Journal
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On the Model---Building Issue of Multi---Objective Estimation of Distribution Algorithms
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Using over-sampling in a Bayesian classifier EDA to solve deceptive and hierarchical problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Quantum-inspired evolutionary algorithm: a multimodel EDA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Sampling bias in estimation of distribution algorithms for genetic programming using prototype trees
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Extended artificial chromosomes genetic algorithm for permutation flowshop scheduling problems
Computers and Industrial Engineering
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
MB-GNG: Addressing drawbacks in multi-objective optimization estimation of distribution algorithms
Operations Research Letters
On the evolvability of a hybrid ant colony-cartesian genetic programming methodology
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
| Hi-index | 0.00 |
A very general class of EDAs is defined, on which universal results on the rate of diversity loss can be derived. This EDA class, denoted SML-EDA, requires two restrictions: 1) in each generation, the new probability model is build using only data sampled from the current probability model; and 2) maximum likelihood is used to set model parameters. This class is very general; it includes simple forms of many well-known EDAs, e.g. BOA, MIMIC, FDA, UMDA, etc. To study the diversity loss in SML-EDAs, the trace of the empirical covariance matrix is the proposed statistic. Two simple results are derived. Let N be the number of data vectors evaluated in each generation. It is shown that on a flat landscape, the expected value of the statistic decreases by a factor 1–1/N in each generation. This result is used to show that for the Needle problem, the algorithm will with a high probability never find the optimum unless the population size grows exponentially in the number of search variables.