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Training artificial neural networks is a complex task of great practical importance. Besides classical ad-hoc algorithms such as backpropagation, this task can be approached by using evolutionary computation, a highly configurable and effective optimization paradigm. This chapter provides a brief overview of these techniques, and shows how they can be(More)
The mallba project tackles the resolution of combinatorial optimization problems using algorithmic skeletons implemented in C++ . mallba offers three families of generic resolution methods: exact, heuristic and hybrid. Moreover, for each resolution method, mallba provides three different implementations: sequential, parallel for local area networks, and(More)
The MALLBA project tackles the resolution of combinatorial optimization problems using generic algorithmic skeletons implemented in C++. A skeleton in the MALLBA library implements an optimization method in one of the three families of generic optimization techniques offered: exact, heuristic and hybrid. Moreover, for each of those methods, MALLBA provides(More)
This work explores different evolutionary approaches to Protein Structure Prediction (PSP), a highly constrained problem. These are the utilization of a repair procedure, and the use of evolutionary operators whose functioning is closed in feasible space. Both approaches rely on hybridizing the evolutionary algorithm (EA) with a backtracking algorithm. The(More)
The generic denomination of ‘Memetic Algorithms’ (MAs) is used to encompass a broad class of metaheuristics (i.e. general purpose methods aimed to guide an underlying heuristic). The method is based on a population of agents and proved to be of practical success in a variety of problem domains and in particular for the approximate solution of NP(More)
An evolutionary approach to the design of fuzzy logic controllers is presented in this paper. We propose the use of the genetic programming paradigm to evolve fuzzy rule-bases (internally represented as type-constrained syntactic trees). This model has been applied to the cart-centering problem, although it can be readily extended to other problems. The(More)
Branch-and-bound (BnB) and memetic algorithms represent two very different approaches for tackling combinatorial optimization problems. However, these approaches are compatible. In this correspondence, a hybrid model that combines these two techniques is considered. To be precise, it is based on the interleaved execution of both approaches. Since the(More)
Bayesian networks (BNs) constitute a useful tool to model the joint distribution of a set of random variables of interest. This paper is concerned with the network induction problem. We propose a number of hybrid recombination operators for extracting BNs from data. These hybrid operators make use of phenotypic information in order to guide the processing(More)