Why one must use reweighting in estimation of distribution algorithms

Authors:
Fabien Teytaud;Olivier Teytaud
Affiliations:
TAO (Inria), U. Paris-Sud, Orsay, France;TAO (Inria), U. Paris-Sud, Orsay, France
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

Citing 15
Cited 3

Elements of simulation

Elements of simulation
Fundamentals of queueing theory (2nd ed.).

Fundamentals of queueing theory (2nd ed.).
Random number generation and quasi-Monte Carlo methods

Random number generation and quasi-Monte Carlo methods
When are quasi-Monte Carlo algorithms efficient for high dimensional integrals?

Journal of Complexity
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Evolutionary computation and Wright's equation

Theoretical Computer Science - Natural computing
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning

Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
The Estimation of Distributions and the Minimum Relative Entropy Principle

Evolutionary Computation
Drift and Scaling in Estimation of Distribution Algorithms

Evolutionary Computation
The correlation-triggered adaptive variance scaling IDEA

Proceedings of the 8th annual conference on Genetic and evolutionary computation
Cross entropy and adaptive variance scaling in continuous EDA

Proceedings of the 9th annual conference on Genetic and evolutionary computation
DCMA: yet another derandomization in covariance-matrix-adaptation

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Model Learning and Variance Control in Continuous EDAs Using PCA

ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
On the ultimate convergence rates for isotropic algorithms and the best choices among various forms of isotropy

PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
The compact genetic algorithm

IEEE Transactions on Evolutionary Computation

Stochastic local search in continuous domains: questions to be answered when designing a novel algorithm

Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Log(λ) modifications for optimal parallelism

PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Linkage neighbors, optimal mixing and forced improvements in genetic algorithms

Proceedings of the 14th annual conference on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

We study the update of the distribution in Estimation of Distribution Algorithms, and show that a simple modification leads to unbiased estimates of the optimum. The simple modification (based on a proper reweighting of estimates) leads to a strongly improved behavior in front of premature convergence.