Convergence analysis of UMDAC with finite populations: a case study on flat landscapes

Authors:
Bo Yuan;Marcus Gallagher
Affiliations:
Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

Citing 7
Cited 3

Extending Population-Based Incremental Learning to Continuous Search Spaces

PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Towards an analytic framework for analysing the computation time of evolutionary algorithms

Artificial Intelligence
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 equation for response to selection and its use for prediction

Evolutionary Computation
From an individual to a population: an analysis of the firsthitting time of population-based evolutionary algorithms

IEEE Transactions on Evolutionary Computation
On stability of fixed points of limit models of univariate marginal distribution algorithm and factorized distribution algorithm

IEEE Transactions on Evolutionary Computation
On the convergence of a class of estimation of distribution algorithms

IEEE Transactions on Evolutionary Computation

When does dependency modelling help? using a randomized landscape generator to compare algorithms in terms of problem structure

PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Using landscape topology to compare continuous metaheuristics: A framework and case study on edas and ridge structure

Evolutionary Computation
Regularized continuous estimation of distribution algorithms

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents some new analytical results on the continuous Univariate Marginal Distribution Algorithm (UMDAC), which is a well known Estimation of Distribution Algorithm based on Gaussian distributions. As the extension of the current theoretical work built on the assumption of infinite populations, the convergence behavior of UMDAC with finite populations is formally analyzed. We show both analytically and experimentally that, on flat landscapes, the Gaussian model in UMDAC tends to collapse with high probability, which is an important fact that is not well understood before.