260 research outputs found
Denoising Autoencoders for fast Combinatorial Black Box Optimization
Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Autoencoders (AE) are
generative stochastic networks with these desired properties. We integrate a
special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate
the performance of DAE-EDA on several combinatorial optimization problems with
a single objective. We asses the number of fitness evaluations as well as the
required CPU times. We compare the results to the performance to the Bayesian
Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a
generative neural network which has proven competitive with BOA. For the
considered problem instances, DAE-EDA is considerably faster than BOA and
RBM-EDA, sometimes by orders of magnitude. The number of fitness evaluations is
higher than for BOA, but competitive with RBM-EDA. These results show that DAEs
can be useful tools for problems with low but non-negligible fitness evaluation
costs.Comment: corrected typos and small inconsistencie
Analyzing limits of effectiveness in different implementations of estimation of distribution algorithms
Conducting research in order to know the range of problems in which a search
algorithm is effective constitutes a fundamental issue to understand the algorithm
and to continue the development of new techniques. In this work, by progressively
increasing the degree of interaction in the problem, we study to what extent different
EDA implementations are able to reach the optimal solutions. Specifically, we deal
with additively decomposable functions whose complexity essentially depends on
the number of sub-functions added. With the aim of analyzing the limits of this
type of algorithms, we take into account three common EDA implementations that
only differ in the complexity of the probabilistic model. The results show that
the ability of EDAs to solve problems quickly vanishes after certain degree of
interaction with a phase-transition effect. This collapse of performance is closely
related with the computational restrictions that this type of algorithms have to
impose in the learning step in order to be efficiently applied. Moreover, we show
how the use of unrestricted Bayesian networks to solve the problems rapidly becomes
inefficient as the number of sub-functions increases. The results suggest that
this type of models might not be the most appropriate tool for the the development
of new techniques that solve problems with increasing degree of interaction. In
general, the experiments proposed in the present work allow us to identify patterns
of behavior in EDAs and provide new ideas for the analysis and development of
this type of algorithms
A quantitative analysis of estimation of distribution algorithms based on Bayesian networks
The successful application of estimation of distribution algorithms
(EDAs) to solve different kinds of problems has reinforced their candidature
as promising black-box optimization tools. However, their internal behavior
is still not completely understood and therefore it is necessary to work
in this direction in order to advance their development. This paper
presents a new methodology of analysis which provides new information
about the behavior of EDAs by quantitatively analyzing the probabilistic
models learned during the search. We particularly focus on calculating the
probabilities of the optimal solutions, the most probable solution given by
the model and the best individual of the population at each step of the
algorithm. We carry out the analysis by optimizing functions of different
nature such as Trap5, two variants of Ising spin glass and Max-SAT. By
using different structures in the probabilistic models, we also analyze the
influence of the structural model accuracy in the quantitative behavior
of EDAs. In addition, the objective function values of our analyzed key
solutions are contrasted with their probability values in order to study
the connection between function and probabilistic models. The results not
only show information about the EDA behavior, but also about the quality
of the optimization process and setup of the parameters, the relationship
between the probabilistic model and the fitness function, and even about
the problem itself. Furthermore, the results allow us to discover common
patterns of behavior in EDAs and propose new ideas in the development
of this type of algorithms
Sub-structural Niching in Estimation of Distribution Algorithms
We propose a sub-structural niching method that fully exploits the problem
decomposition capability of linkage-learning methods such as the estimation of
distribution algorithms and concentrate on maintaining diversity at the
sub-structural level. The proposed method consists of three key components: (1)
Problem decomposition and sub-structure identification, (2) sub-structure
fitness estimation, and (3) sub-structural niche preservation. The
sub-structural niching method is compared to restricted tournament selection
(RTS)--a niching method used in hierarchical Bayesian optimization
algorithm--with special emphasis on sustained preservation of multiple global
solutions of a class of boundedly-difficult, additively-separable multimodal
problems. The results show that sub-structural niching successfully maintains
multiple global optima over large number of generations and does so with
significantly less population than RTS. Additionally, the market share of each
of the niche is much closer to the expected level in sub-structural niching
when compared to RTS
- …