J.M. Sanchez’s research while affiliated with University of Extremadura and other places

Multi-FPGA systems (MFS) are used for a great variety of applications, for instance, dynamically re-configurable hardware applications, digital circuit emulation, and numerical computation. There are a great variety of boards for MFS implementation. In this paper a methodology for MFS design is presented. The techniques used are evolutionary programs and they solve all of the design tasks (partitioning placement and routing). Firstly a hybrid compact genetic algorithm solves the partitioning problem and then genetic programming is used to obtain a solution for the two other tasks.

This paper presents a set of experiments that have been performed using both benchmark and real life problems. We have studied whether island-based genetic programming can save computational effort by controlling the bloat phenomenon. Results show us that this model allows us to reduce the computation resources required for finding solutions when using genetic programming.

Internet se está convirtiendo en un importante recurso educativo gracias a que permite superar las limitaciones de lugar y tiempo. Además, no debe olvidarse el efecto que la interactividad tiene en el proceso de aprendizaje. Por eso, nos parece importante su aplicación a la enseñanza de la electrónica, y en particular de los sistemas digitales. En esta comunicación presentamos el sistema SD2I, dedicado a la enseñanza, control docente y evaluación del aprendizaje vía Internet de la materia sistemas digitales, impartida en la asignatura Fundamentos de Informática.

In this paper we present results obtained when comparing classic genetic programming (GP) with the isolated multipopulation version. Our first discovery was that sometimes, given a certain number of individuals, it is useful to distribute them among several populations even when no communication is allowed. This consequently lead to research concentrating on three main questions: firstly, how to distribute individuals according to the problem in hand; secondly, how many populations must be employed in proportion to the effort and fitness involved when solving a problem; and finally, how to use isolated multipopulation GP in the classification of problems

Decision support systems have proved to be useful in medical decision making. Here, the authors present a methodology that allow them to capture medical knowledge and develop a decision system for burn diagnosing by means of Genetic Programming. Diagnosing the evolution of a burn is a very difficult task. The authors present a learning classifier system based on multipopulations genetic programming. It uses a set of parameters, obtained by specialist doctors, to predict the evolution of a burn according to its initial stages. The system is first trained with a set of parameters and results of evolution have been recorded over a set of clinic cases. Once the system is trained, it is useful for deciding how new cases will probably evolve. Thanks to the use of Genetic Programming an explicit expression of the input parameter is provided. This explicit expression takes the form of a Decision Tree, which will be incorporated into software tools that help physicians in their everyday work

Genetic programming (GP) has proved useful in optimization problems. The way of representing individuals in this methodology is particularly good when we want to construct decision trees. Decision trees are well suited to representing explicit information and relationships among parameters studied. A set of decision trees could make up a decision support system. In this paper we set out a methodology for developing decision support systems as an aid to medical decision making. Above all, we apply it to diagnosing the evolution of a burn, which is a really difficult task even for specialists. A learning classifier system is developed by means of multipopulation genetic programming (MGP). It uses a set of parameters, obtained by specialist doctors, to predict the evolution of a burn according to its initial stages. The system is first trained with a set of parameters and results of evolutions which have been recorded over a set of clinic cases. Once the system is trained, it is useful for deciding how new cases will probably evolve. Thanks to the use of GP, an explicit expression of the input parameter is provided. This explicit expression takes the form of a decision tree which will be incorporated into software tools that help physicians In their everyday work

The parallel execution of several populations in Evolutionary Algorithms has usually given good results. Nevertheless, researchers have to date drawn conflicting conclusions when using some of the Parallel Genetic Programming models. One aspect of the conflict is population size, since published GP works do not agree about whether to use large or small populations. This paper presents an experimental study of a number of common GP test problems. Via our experiments, we discovered that an optimal range of values exists. This assists us in our choice of population size and in the selection of an appropriate Parallel Genetic Programming model. Finding efficient parameters helps us to speed up our search for solutions. At the same time, it allows us to locate features that are common to Parallel Genetic Programming and the classic Genetic Programming Technique.

We present the results we have obtained after applying techniques on a basis of genetic methodology to the resolution of problems related with the automatic synthesis of digital circuits. We tackle the minimization of the number of states in incompletely specified finite state machines and the optimal state assignment on two level logic. Both class of problems involves the resolution of NP problems. In the first case, we have used a classical genetic algorithm. In the second one have been used new types of operators and ways of representation to avoid the problems that appear. Finally, we try to find the optimal mutation probability which guarantees the exploration of new regions of solution space without search becoming aleatory

In this paper we address the optimization problem of multilevel logical functions using algorithms based on natural process. We show the results found by applying the simulated annealing algorithm and a genetic algorithm to area optimization of multilevel logical networks using the IWLS'93 benchmarks