Catálogo de publicaciones - libros

Cooperative coevolution is a recent paradigm in the area of evolutionary computation focused on the evolution of coadapted subcomponents without external interaction. In cooperative coevolution a number of species are evolved together. The cooperation among the individuals is encouraged by rewarding the individuals according to their degree of cooperation in solving a target problem. The work on this paradigm has shown that cooperative coevolutionary models present many interesting features, such as specialization through genetic isolation, generalization and efficiency. Cooperative coevolution approaches the design of modular systems in a natural way, as the modularity is part of the model. Other models need some a priori knowledge to decompose the problem by hand . In most cases, either this knowledge is not available or it is not clear how to decompose the problem.

Evolutionary multiobjective optimization approach to RBF networks structure determination is discussed in this chapter. The candidates of RBF network structure are encoded into the chromosomes in GAs and they evolve toward the Pareto optimal front defined by the several objective functions with regard to model accuracy and model complexity. Then, an ensemble of networks is constructed by using the Pareto optimal networks. We discuss its application to nonlinear system identification. Numerical simulation results indicate that the ensemble network is much more robust for the case of existence of outliers or lack of data, than the one selected based on information criteria.

The main advantage of evolutionary multi-objective optimization (EMO) over classical approaches is that a variety of non-dominated solutions with a wide range of objective values can be simultaneously obtained by a single run of an EMO algorithm. In this chapter, we show how this advantage can be utilized in the design of fuzzy ensemble classifiers. First we explain three objectives in multi-objective formulations of fuzzy rule selection. One is accuracy maximization and the others are complexity minimization. Next we demonstrate that a number of non-dominated rule sets (i.e., fuzzy classifiers) are obtained along the accuracy-complexity tradeoff surface from multi-objective fuzzy rule selection problems. Then we examine the effect of combining multiple non-dominated fuzzy classifiers into a single ensemble classifier. Experimental results clearly show that the combination into ensemble classifiers improves the classification ability of individual fuzzy classifiers for some data sets.

Receiver operating characteristic (ROC) analysis is now a standard tool for the comparison of binary classifiers and the selection operating parameters when the costs of misclassification are unknown.

This chapter discusses the behavioral learning of robots from the viewpoint of multiobjective design. Various coordination methods for multiple behaviors have been proposed to improve the control performance and to manage conflicting objectives. We proposed various learning methods for neuro-fuzzy controllers based on evolutionary computation and reinforcement learning. First, we introduce the supervised learning method and evolutionary learning method for multiobjective design of robot behaviors. Then, the multiobjective design of fuzzy spiking neural networks for robot behaviors is presented. The key point behind these methods is to realize the adaptability and reusability of behaviors through interactions with the environment.

In some environments, mobile robots need to perform tasks in a precise manner. For this reason, we require obtaining good controllers in charge of these control tasks. In this work, we present a real-world application in the domain of multi-objective machine learning, which consists of an Automated Guided Vehicle (AGV), specifically, a fork-lift truck must often perform docking maneuvers to load pallets in conveyor belts. The main purpose is to improve some features of docking task as its duration, accuracy and stability, satisfying determined constraints. We propose a machine learning technique based on a multi-objective evolutionary algorithm in order to find multiple fuzzy logic controllers which optimize specific objectives and satisfy imposed constraints for docking task in charge of following up an online generated trajectory.

Persistent queries are a specific kind of queries used in information retrieval systems to represent a user’s long-term standing information need. These queries can present many different structures, being the “bag of words” that most commonly used. They can be sometimes formulated by the user, although this task is usually difficult for him and the persistent query is then automatically derived from a set of sample documents he provides.

In real-time computer vision, there is a need for classifiers that detect patterns fast and reliably. We apply multi-objective optimization (MOO) to the design of feed-forward neural networks for real-world object recognition tasks, where computational complexity and accuracy define partially conflicting objectives. Evolutionary structure optimization and pruning are compared for the adaptation of the network topology. In addition, the results of MOO are contrasted to those of a single-objective evolutionary algorithm. As a part of the evolutionary algorithm, the automatic adaptation of operator probabilities in MOO is described.