Download:
|
by Byoung-tak Zhang, Soo-yong Shin
Parallel Problem Solving from Nature VI, LNCS 1917
http://bi.snu.ac.kr/~syshin/Publications/ppsn2000.ps.gz
Add To MetaCart
Abstract:
Abstract. Recently, several evolutionary algorithms have been proposed that build and use an explicit distribution model of the population to perform optimization. One of the main issues in this class of algorithms is how to estimate the distribution of selected samples. In this paper, we present a Bayesian evolutionary algorithm (BEA) that learns the sample distribution by a probabilistic graphical model known as Helmholtz machines. Due to the generative nature and availability of the wake-sleep learning algorithm, the Helmholtz machines provide an eective tool for modeling and sampling from the distribution of selected individuals. The proposed method has been applied to a suite of GA-deceptive functions. Experimental results show that the BEA with the Helmholtz machine outperforms the simple genetic algorithm. 1
Citations
|
535
|
Evolutionary Algorithms in theory and practice
– Back
- 1995
|
|
182
|
The wake-sleep algorithm for unsupervised neural networks
– Hinton, Dayan, et al.
- 1995
|
|
154
|
The Helmholtz machine
– Dayan, Hinton, et al.
- 1995
|
|
150
|
Boa: The bayesian optimization algorithm
– Pelikan, Goldberg, et al.
- 1999
|
|
149
|
From recombination of genes to the estimation of distributions i. binary parameters
– Mühlenbein, Paaß
- 1996
|
|
136
|
Removing the genetics from the standard genetic algorithm
– Baluja, Caruana
- 1995
|
|
100
|
Using optimal dependency–trees for combinatorial optimization: Learning the structure of the search space
– Baluja, Davies
- 1997
|
|
94
|
MIMIC: Finding optima by estimating probability densities
– Bonet, Jr, et al.
- 1997
|
|
78
|
Distributions and Graphical Models in Evolutionary Optimization
– Mühlenbein, Mahnig, et al.
- 1999
|
|
68
|
The bivariate marginal distribution algorithm
– Pelikan, Mühlebein
- 1999
|
|
43
|
FDA – a scalable evolutionary algorithm for the optimization of additively decomposed functions
– Mühlenbein, Mahnig
- 1999
|
|
21
|
Factor Analysis using Delta-Rule Wake-Sleep Learning
– Neal, Dayan
- 1996
|
|
11
|
A bayesian framework for evolutionary computation
– Zhang
- 1999
|
|
2
|
Bayesian methods for ecient genetic programming. Genetic Programming And Evolvable Machines
– Zhang
- 2000
|
|
1
|
Ecient model induction by a Bayesian evolutionary algorithm with incremental data inheritance
– Zhang, Joung
- 1998
|
