Punctuated equilibria: a parallel genetic algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Structure driven image database retrieval
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Improving flexibility and efficiency by adding parallelism to genetic algorithms
Statistics and Computing
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
A Theoretical Investigation of a Parallel Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Initial Approaches to the Application of Islands-Based Parallel EDAs in Continuous Domains
ICPPW '05 Proceedings of the 2005 International Conference on Parallel Processing Workshops
Initial approaches to the application of islands-based parallel EDAs in continuous domains
Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Towards billion-bit optimization via a parallel estimation of distribution algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The equation for response to selection and its use for prediction
Evolutionary Computation
Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms
Information Sciences: an International Journal
Visual exploration of algorithm parameter space
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Parallel BMDA with an aggregation of probability models
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Information Sciences: an International Journal
Information Sciences: an International Journal
Combinatorial optimization by learning and simulation of Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
IEEE Transactions on Evolutionary Computation
Parallel Implementation of EDAs Based on Probabilistic Graphical Models
IEEE Transactions on Evolutionary Computation
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One of the most promising areas in which probabilistic graphical models have shown an incipient activity is the field of heuristic optimization and, in particular, in Estimation of Distribution Algorithms. Due to their inherent parallelism, different research lines have been studied trying to improve Estimation of Distribution Algorithms from the point of view of execution time and/or accuracy. Among these proposals, we focus on the so-called distributed or island-based models. This approach defines several islands (algorithms instances) running independently and exchanging information with a given frequency. The information sent by the islands can be either a set of individuals or a probabilistic model. This paper presents a comparative study for a distributed univariate Estimation of Distribution Algorithm and a multivariate version, paying special attention to the comparison of two alternative methods for exchanging information, over a wide set of parameters and problems - the standard benchmark developed for the IEEE Workshop on Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems of the ISDA 2009 Conference. Several analyses from different points of view have been conducted to analyze both the influence of the parameters and the relationships between them including a characterization of the configurations according to their behavior on the proposed benchmark.