Marina de la Cruz Echeandía

Attribute grammar evolution

This paper describes Attribute Grammar Evolution (AGE), a new Automatic Evolutionary Programming algorithm that extends standard Grammar Evolution (GE) by replacing context-free grammars by attribute grammars. GE only takes into account syntactic restrictions to generate valid individuals. AGE adds semantics to ensure that both semantically and syntactically valid individuals are generated. Attribute grammars make it possible to semantically describe the solution. The paper shows empirically that AGE is as good as GE for a classical problem, and proves that including semantics in the grammar can improve GE performance. An important conclusion is that adding too much semantics can make the search difficult.

Parallel Simulation of NEPs on Clusters

This paper compares two different approaches, followed by our research group, to efficiently run NEPs on parallel platforms, as general and transparent as possible. The vague results of jNEP (our multithreaded Java simulator for multicore desktop computers) suggests the use of massively parallel platforms (clusters of computers). The good results obtained show the scalability and viability of this last approach.

A Christiansen Grammar for Universal Splicing Systems

The main goal of this work is to formally describe splicing systems. This is a necessary step to subsequently apply Christiansen Grammar Evolution (an evolutionary tool developed by the authors) for automatic designing of splicing systems. Their large number of variants suggests us a decisions: to select a family as simple as possible of splicing systems equivalent to Turing machines. This property ensures that the kind of systems our grammar can generate is able to solve any arbitrary problem. Some components of these universal splicing systems depend on other components. So, a formal representation able to handle context dependent constructions is needed. Our work uses Christiansen grammars to describe splicing systems.

GE and Semantics

The main goal of this chapter is to explain in a comprehensible way the semantic context in formal language theory. This is necessary to properly understand the attempts to extend Grammatical Evolution (GE) to include semantics. Several approaches from different researchers to handle semantics, both directly and indirectly, will be briefly introduced. Finally, previous works by the authors will be described in depth.

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 2013

Model Regularization in Coevolutionary Architectures Evolving Straight Line Code

Frequently, when an evolutionary algorithm is applied to a population of symbolic expressions, the shapes of these symbolic expressions are very different at the first generations whereas they become more similar during the evolving process. In fact, when the evolutionary algorithm finishes most of the best symbolic expressions only differ in some of its coefficients. In this paper we present several coevolutionary strategies of a genetic program that evolves symbolic expressions represented by straight line programs and an evolution strategy that searches for good coefficients. The presented methods have been applied to solve instances of symbolic regression problem, corrupted by additive noise. A main contribution of the work is the introduction of a fitness function with a penalty term, besides the well known fitness function based on the empirical error over the sample set. The results show that in the presence of noise, the coevolutionary architecture with penalized fitness function outperforms the strategies where only the empirical error is considered in order to evaluate the symbolic expressions of the population.