Agent-based Population Research Articles

AGENT-BASED APPROACH TO THE DESIGN OF RBF NETWORKS

This article proposes a novel approach to the radial basis function network (RBFN) design. Its main idea is to apply the agent-based population learning algorithm to the task of initialization and training RBFNs. The approach allows for an effective network initialization and estimation of its output weights. The initialization involves two stages, where in the first one initial clusters are produced using the similarity-based procedure and next, in the second stage, prototypes (centroids) from the thus-obtained clusters are selected. The agent-based population learning algorithm is used to select prototypes. In the proposed implementation of the algorithm, both tasks—RBFN initialization and RBFN training—are carried out by a team of agents executing various local search procedures and cooperating with a view to determine the solution to the RBFN design problem at hand. The performance of the RBFN constructed using the proposed agent-based approach is analyzed and evaluated. The proposed approach is also compared with different RBFN initialization and training procedures in the literature.

SELECTING A REPRESENTATIVE DATA SET OF THE REQUIRED SIZE USING THE AGENT-BASED POPULATION LEARNING ALGORITHM

The aim of this article is to propose and evaluate an agent-based population learning algorithm generating, through prototype selection, a representative training data set of the required size. The main feature of the proposed approach is selection of the representative feature vectors, called prototypes, from clusters of feature vectors constructed over the original training data set under the assumption that from each cluster a single prototype is obtained. Thus, the number of clusters produced from the original training data set has a direct influence on the size of the reduced data set. The process of selection is executed by a team of agents, which execute various local search procedures and cooperate to determine a solution to the instance reduction problem, aiming at obtaining a compact representation of the data set. Rules for agent cooperation during the clustering and selection processes are defined within the so-called working strategy used by the team of agents (A-Team) in question. The article proposes a set of procedures that are used by agents to produce clusters and select prototypes. The approach is validated experimentally using well-known benchmark data sets. In addition to the computational experiment used to validate the model, the article investigates the efficiency and performance of two different working strategies used by the proposed A-Team. Because the proposed approach is based on the population learning algorithm, which belongs to the class of the population-based methods, an evaluation of the influence of the population of solution size on the performance of the algorithm is also included.

Implementation and performance evaluation of the agent-based algorithm for ANN training

The paper contains a description of the implementation and performance evaluation of the agent-based population learning algorithm used to train the feed-forward artificial neural networks. The goal of the research was to evaluate efficiency of the a

Prototype selection algorithms for distributed learning

Distributed learning from data is one of the typical tasks solved by distributed data-mining techniques and is seen as a fundamental computational problem. One of the approaches suitable for distributed learning is to select, by data reduction, relevant local patterns, called also prototypes, from geographically distributed databases. Next, locally selected prototypes can be moved to other sites and merged into the global knowledge model. The paper presents three agent-based population learning algorithms for distributed learning. The proposed algorithms are based on agent collaborations in distributed prototype selection processes and on agent collaborations when the learning global model is created. The basic property of the presented algorithms is that the prototypes are selected by agent-based population learning algorithm from data clusters induced at distributed sites. The main goal of the paper is to empirically compare how the way of inducing such clusters can influence the distributed learning performance. The paper investigates the agent-based population learning algorithms used to solve distributed data reduction and gives a brief discussion of the procedures for clusters initialization. Finally, computational experiment results are shown.