On the evaluation of information exchange strategies in dEDAs

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 the Estimation of Distribution Algorithms (EDAs). EDAs constitute a well-known family of Evolutionary Computation techniques, similar to Genetic Algorithms. Due to their inherent parallelism, different research lines have tried to improve EDAs from the point of view of execution time and/or accuracy. Among these proposals, we focus on the so-called island-based models. This approach defines several islands (EDA instances) running independently and exchanging information with a given frequency. The information sent by the islands can be a set of individuals or a probabilistic model. This paper presents a comparative study of both information exchanging techniques for a univariate EDA (UMDAg) 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

Network Inference via the Time-Varying Graphical Lasso

Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability

Novel Genetic Algorithm-Based Evolutionary Support Vector Machine for Optimizing High-Performance Concrete Mixture

An effective method for optimizing high-performance concrete mixtures can significantly benefit the construction industry. However, traditional proportioning methods are not sufficient because of their expensive costs, limitations of use, and inability to address nonlinear relationships among components and concrete properties. Consequently, this research introduces a novel genetic algorithm (GA)–based evolutionary support vector machine (GA-ESIM), which combines the K-means and chaos genetic algorithm (KCGA) with the evolutionary support vector machine inference model (ESIM). This model benefits from both complex input-output mapping in ESIM and global solutions with faster convergence characteristics in KCGA. In total, 1,030 data points from concrete strength experiments are provided to demonstrate the application of GA-ESIM. According to the results, the newly developed model successfully produces the optimal mixture with minimal prediction errors. Furthermore, a graphical user interface is utilized to assist users in performing optimization tasks

Migrating Individuals and Probabilistic Models on DEDAS: a Comparison on Continuous Functions

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 the Estimation of Distribution Algorithms (EDAs). EDAs constitute a well-known family of Evolutionary Computation techniques, similar to Genetic Algorithms. Due to their inherent parallelism, different research lines have been studied trying to improve EDAs from the point of view of execution time and/or accuracy. Among these proposals, we focus on the so-called island-based models. This approach defines several islands (EDA instances) running independently and exchanging information with a given frequency. The information sent by the islands can be a set of individuals or a probabilistic model. This paper presents a comparative study of both information exchanging techniques for a univariate EDA (U M DAg) 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. The study concludes that the configurations based on migrating individuals obtain better result

Visualization of Free Search Process

The article presents visualization of an adaptive method for optimization called Free Search and gives a description of methodology used. Implemented tasks are illustrated with relevant graphics. Benefits of having 3D graphical interface are in opportunity to observe and track the changes and behavior of this algorithm during the search process. Technologies and tools used for visualization are explained and discussed