Daniel Manrique

An instructional model for web‐based e‐learning education with a blended learning process approach

Web-based e-learning education research and development now focuses on the inclusion of new technological features and the exploration of software standards. However, far less effort is going into finding solutions to psychopedagogical problems in this new educational category. This paper proposes a psychopedagogical instructional model based on content structure, the latest research into information processing psychology and social contructivism, and defines a blended approach to the learning process. Technologically speaking, the instructional model is supported by learning objects, a concept inherited from the object-oriented paradigm.

Evolutionary system for automatically constructing and adapting radial basis function networks

Abstract This article presents a new system for automatically constructing and training radial basis function networks based on original evolutionary computing methods. This system, called Genetic Algorithm Radial Basis Function Networks (GARBFN), is based on two cooperating genetic algorithms. The first algorithm uses a new binary coding, called basic architecture coding, to get the neural architecture that best solves the problem. The second, which uses real coding, takes its inspiration from mathematical morphology theory and trains the architectures output by the binary genetic algorithm. This system has been applied to a laboratory problem and to breast cancer diagnosis. The results of these evaluations show that the overall performance of GARBFN is better than other related approaches, whether or not they are based on evolutionary techniques.

Learning objects, learning objectives and learning design

Educational research and development into e‐learning mainly focuses on the inclusion of new technological features without taking into account psycho‐pedagogical concerns that are likely to improve a learner’s cognitive process in this new educational category. This paper presents an instructional model that combines objectivist and constructivist learning theories. The model is based on the concept of a learning objective which is composed of a set of learning objects. A software tool, called the Instruction Aid System (IAS), has been developed to guide instructors through the development of learning objectives and the execution of the analysis and design phases of the proposed instructional model. Additionally, a blended approach to the learning process in Web‐based distance education is also presented. This approach combines various event‐based activities: self‐paced learning, live e‐learning and the use of face‐to‐face contact in classrooms.

Crossover and mutation operators for grammar-guided genetic programming

This paper proposes a new grammar-guided genetic programming (GGGP) system by introducing two original genetic operators: crossover and mutation, which most influence the evolution process. The first, the so-called grammar-based crossover operator, strikes a good balance between search space exploration and exploitation capabilities and, therefore, enhances GGGP system performance. And the second is a grammar-based mutation operator, based on the crossover, which has been designed to generate individuals that match the syntactical constraints of the context-free grammar that defines the programs to be handled. The use of these operators together in the same GGGP system assures a higher convergence speed and less likelihood of getting trapped in local optima than other related approaches. These features are shown throughout the comparison of the results achieved by the proposed system with other important crossover and mutation methods in two experiments: a laboratory problem and the real-world task of breast cancer prognosis.

Initialization method for grammar-guided genetic programming

This paper proposes a new tree-generation algorithm for grammar-guided genetic programming that includes a parameter to control the maximum size of the trees to be generated. An important feature of this algorithm is that the initial populations generated are adequately distributed in terms of tree size and distribution within the search space. Consequently, genetic programming systems starting from the initial populations generated by the proposed method have a higher convergence speed. Two different problems have been chosen to carry out the experiments: a laboratory test involving searching for arithmetical equalities and the real-world task of breast cancer prognosis. In both problems, comparisons have been made to another five important initialization methods.

A moderate constructivist e-learning instructional model evaluated on computer specialists

This paper presents a novel instructional model for e-learning and an evaluation study to determine the effectiveness of this model for teaching Java language programming to information technology specialists working for the Spanish Public Administration. This is a general-purpose model that combines objectivist and constructivist learning theories and is based on the concept of learning objective. The purpose of the evaluation study is to find out whether the results of using this distance learning instructional model to teach this subject are comparable to learning in a traditional face-to-face classroom, with the plus of eliminating travel and maintenance expenses of the public servants attending the course and also saving time. The learners, selected at random to participate in this study, were divided into three groups depending on the type of teaching/learning they received: traditional classroom, distance learning with virtualized course contents and distance learning based on the proposed instructional model. The results indicate that the grades and satisfaction levels were similar for learners taught using the proposed instructional model and learners taught in the traditional classroom. Moreover, they were substantially better than for distance learning with virtualized contents, although the mean course learning time is greater.

Optimisation With Real-Coded Genetic Algorithms Based On Mathematical Morphology

The goal of this work is to propose a novel approach to function optimisation by evolutionary techniques, in particular, real-coded genetic algorithms. A new genetic crossover operator, suitable for real codification, has been designed. This operator is called morphological crossover as it is based on mathematical morphology theory. The morphological crossover includes a new genetic diversity measure that has low computational cost. This operator is presented along with the resolution of a set of optimisation problems, including neural network training. The results are compared to other optimisation approaches as gradient descent methods or binary and real-coded genetic algorithms using different crossover operators. These tests show that the properties exhibited by the proposed operator when using real-coded genetic algorithms give higher convergence speed and less probability of being trapped in a local optimum.

Evolutionary construction and adaptation of intelligent systems

This paper introduces evolutionary techniques for automatically constructing intelligent self-adapting systems, capable of modifying their inner structure in order to learn from experience and self-adapt to a changing environment. These evolutionary techniques comprise an evolutionary system that is engineered by grammar-guided genetic programming, enabling the development of sub-symbolic and symbolic intelligent systems: artificial neural networks and knowledge-based systems, respectively. A context-free-grammar based codification system for artificial neural networks and rules, an initialization method and a crossover operator have been designed to properly balance the exploration and exploitation capabilities of the proposed system. This speeds up the convergence process and avoids trapping in local optima. This system has been applied to a medical domain: the detection of knee injuries from the analysis of isokinetic time series. The results of the evolved symbolic and sub-symbolic intelligent systems have been statistically compared with each other as part of a quantitative and qualitative performance analysis.

Cooperative binary-real coded genetic algorithms for generating and adapting artificial neural networks

This paper describes a genetic system for designing and training feed-forward artificial neural networks to solve any problem presented as a set of training patterns. This system, called GANN, employs two interconnected genetic algorithms that work parallelly to design and train the better neural network that solves the problem. Designing neural architectures is performed by a genetic algorithm that uses a new indirect binary codification of the neural connections based on an algebraic structure defined in the set of all possible architectures that could solve the problem. A crossover operation, known as Hamming crossover, has been designed to obtain better performance when working with this type of codification. Training neural networks is also accomplished by genetic algorithms but, this time, real number codification is employed. To do so, morphological crossover operation has been developed inspired on the mathematical morphology theory. Experimental results are reported from the application of GANN to the breast cancer diagnosis within a complete computer-aided diagnosis system.

A P system and a constructive membrane-inspired DNA algorithm for solving the Maximum Clique Problem.

We present a P system with replicated rewriting to solve the Maximum Clique Problem for a graph. Strings representing cliques are built gradually. This involves the use of inhibitors that control the space of all generated solutions to the problem. Calculating the maximum clique for a graph is a highly relevant issue not only on purely computational grounds, but also because of its relationship to fundamental problems in genomics. We propose to implement the designed P system by means of a DNA algorithm. This algorithm is then compared with two standard papers that addressed the same problem and its DNA implementation in the past. This comparison is carried out on the basis of a series of computational and physical parameters. Our solution features a significantly lower cost in terms of time, the number and size of strands, as well as the simplicity of the biological implementation.