Observatorio de I+D+i UPM

Descripción
This paper presents a grammar-guided evolutionary automatic system (GGEAS) that is capable of autonomously building special-purpose problem-solving programs. GGEAS uses a grammar-guided genetic programming (GGGP) core that generates solutions to a given problem from scratch, evolving them via selection, crossover and replacement to obtain the near-optimal solution to that problem. The GGGP core solves the closure problem and avoids code bloat. This core only outputs valid solutions and is able to freely determine their size and architecture. GGEAS is supplemented by three external modules that can be configured for any application domain: context-free grammar (CFG) generator, semantic checker and fitness module. The context-free grammar (CFG) generator creates the context-free grammar used by the GGEAS core to formalize the problem constraints. The semantic checker ensures the validity of the solutions created. Finally, the fitness module directs the population evolution towards an optimal solution to the problem. In order to test the effectiveness and the scope of the system, GGEAS has been applied to generate oscillatory biological programs codified in the BlenX language. The results show that GGEAS is effective at creating biological oscillators in silico from scratch without any prior knowledge about the solution and under a range of environmental conditions.
Internacional	Si
Nombre congreso	WCCI 2010, IEEE World Congress on Computational Intelligence
Tipo de participación	960
Lugar del congreso	Barcelona, España
Revisores	Si
ISBN o ISSN	978-1-4244-6909-3
DOI
Fecha inicio congreso	18/07/2010
Fecha fin congreso	23/07/2010
Desde la página	2742
Hasta la página	2748
Título de las actas	Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010

Esta actividad pertenece a memorias de investigación

• Autor: Daniel Manrique Gamo (UPM)
• Autor: José María Font Fernández (UPM)

• Creador: Grupo de Investigación: Grupo de Inteligencia Artificial (LIA)