Evolución gramatical y semántica

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, junio de 201

Parametric 2-dimensional L systems and recursive fractal images: Mandelbrot set, Julia sets and biomorphs

This is the author’s version of a work that was accepted for publication in Computers & Graphics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Graphics 26, 1, (2002) DOI: 10.1016/S0097-8493(01)00162-5L Systems have proved their expressive power. They have been used to represent the class of the initiator/iterator fractal curves (such as Sierpinski's gasket and von Koch's snowflake curve). Parametric L Systems, introduced by Prusinkiewicz and Lindenmayer, link real valued parameters to the symbols. In this paper, parametric 0L systems are extended to n dimensions and used to represent a different class of classic fractals that includes objects such the Mandelbrot and Julia sets, or Pickover’s biomorphs

Different approaches for development tools for natural computers: grammar driven vs. model driven approaches

This is an electronic version of the paper presented at the Special Session on Learning, Agents and Formal Languages (LAFLang 2013), during the International Conference on Agents and Artificial Intelligence (ICAART 2013), held in Barcelona (Spain) on 2013In this paper we will compare our first steps in two different approaches to define programming languages for NEPs (one bio-inspired model of computation in which our research group is interested). The classic approach proposed by the literature several decades ago is focused on the grammar of the syntax of the language being defined. Recently the focus is moved to a formal description (model) of the model of computation for which the programming language is being designed. This approach is called model driven. The designer simply adds syntax, semantics checks and translation routines to the different elements of the model that are applied. Programming is usually understood as instantiating the model. After introducing the main characteristics of each model for this particular case some conclusions and further research tasks are discussed.Work partially supported by the Spanish Ministry of Science and Innovation under coordinated research project TIN2011-28260-C03-00 and research project TIN2011-28260-C03-02 and by the Comunidad Autónoma de Madrid under research project e-madrid S2009/TIC-165

Christiansen Grammar for Some P Systems

The main goal of this work is to formally describe P systems. This is a necessary step to subsequently apply Christiansen grammar evolution (an evolutionary tool developed by the authors) for automatic designing of P systems. Their complex structure suggests us two decisions: to restrict our study to a subset of P systems that ease the representation while keeping a suitable complexity and to select a powerful enough formal tool. Our work is restricted to a kind of P system that can simulate any logical function by means of delay symbols and two mobile catalysts. Like in general P systems, some components of these "logical" P systems depend on other components (for example, the number of axioms and regions and the set of possible indexes for the symbols in their rules depend on the membrane structure). So, a formal representation able to handle context dependent constructions is needed. Our work uses Christiansen grammars to describe P systems

Christiansen grammar evolution for the modelling of psychological processes

This is an electronic version of the paper presented at the International Industrial Simulation Conference (ISC 2007), held in Delft (The Netherlands)Psychologists have developed models of associative learning for more than 30 years. Despite the strong efforts made, they still suffer many shortcomings. We have tried to build an integral model of habituation, the simplest type of learning within the area of associative learning and the basic support for other types. To overcome the deficiencies of traditional models, we have made used of Christiansen Grammar Evolution. This evolutionary technique is capable of automatically search for a target expression (the model) in a given formal language (the formalism of the model). Under this perspective, that we call Automatic Modelling, we have found models of habituation with interesting characteristics.This work has been partially sponsored by the Spanish Ministry of Education and Science (MEC), project number TSI2005-08225- C07-06

Coevolutionary architectures with straight line programs for solving the symbolic regression problem

This is an electronic version of the paper presented at the International Conference on Evolutionary Computation (ICEC), held in Valencia (Spain) on 2010To successfully apply evolutionary algorithms to the solution of increasingly complex problems we must develop effective techniques for evolving solutions in the form of interacting coadapted subcomponents. In this paper we present an architecture which involves cooperative coevolution of two subcomponents: a genetic program and an evolution strategy. As main difference with work previously done, our genetic program evolves straight line programs representing functional expressions, instead of tree structures. The evolution strategy searches for good values for the numerical terminal symbols used by those expressions. Experimentation has been performed over symbolic regression problem instances and the obtained results have been compared with those obtained by means of Genetic Programming strategies without coevolution. The results show that our coevolutionary architecture with straight line programs is capable to obtain better quality individuals than traditional genetic programming using the same amount of computational effort.This work is partially supported by spanish grants TIN2007-67466-C02-02, MTM2004-01167 and S2009/TIC-165

The role of keeping "semantic blocks" invariant: effects in linear genetic programming performance

This paper is focused on two different approaches (previously proposed by the authors) that perform better than Genetic Programming in typical symbolic regression problems: straight-line program genetic programming (SLP-GP) and evolution with attribute grammars (AGE). Both approaches have different characteristics. One of themost important is that SLP-GP keeps semantic blocks invariant (the crossover operator always exchanges complete subexpressions). In this paper we compare both methods and study the possible effect on their performance of keeping these blocks invariant.This work was partially supported by the R&D program of the Community of Madrid (S2009/TIC-1650, project “e-Madrid”) as well as by the Spanish Ministry of Science and Innovation (TIN2007-67466-C02-02). The authors thank Dr. Manuel Alfonseca for his help to prepare this document

Developing Tools for Networks of Processors

A great deal of research eort is currently being made in the realm of so called natural computing. Natural computing mainly focuses on the denition, formal description, analysis, simulation and programming of new models of computation (usually with the same expressive power as Turing Machines) inspired by Nature, which makes them particularly suitable for the simulation of complex systems.Some of the best known natural computers are Lindenmayer systems (Lsystems, a kind of grammar with parallel derivation), cellular automata, DNA computing, genetic and evolutionary algorithms, multi agent systems, arti- cial neural networks, P-systems (computation inspired by membranes) and NEPs (or networks of evolutionary processors). This chapter is devoted to this last model

Preface to the volume Languages: Bionspired Approaches

This volume aims to provide a state-of-the-art of the work recently done, by some relevant Spanish Research Groups, in the area of nets of processors

NEPs Applied to Solve Specific Problems

In the following pages we will use NEPS to solve several small instances of well known NP problems. We will show computational implementations of NEPs