Distributed Estimation of Distribution Algorithms for continuous optimization: How does the exchanged information influence their behavior?

Abstract

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.

Adaptive Learning Rate Elitism Estimation of Distribution Algorithm Combining Chaos Perturbation for Large Scale Optimization

“…However, the probability models cannot reflect the problem completely, especially for the increases of number of variables and the number of mixture components, the optimization results become unreliable [31]. Additionally, the computational cost is huge when considering all the possible (in) dependencies among the variables [32]. Therefore, in this paper we only adopt univariate Gaussian model to approximate the solution distribution.…”