Grammatical Evolution and Applications

Grammatical evolution (GE) is an evolutionary algorithm, similar to genetic programming (GP), where the produced program is produced by a BNF grammar. In GE, the chromosomes are not expressed in the usual form of parse trees but as vectors of integers. Each element of the vector stands for a production rule from the given BNF grammar. The procedure initiates from the start symbol of the grammar and iteratively produces the program string, by replacing non-terminal symbols with the right hand of the selected production rule. GE finds many applications in a variety of scientific fields and practical problems, such as music composition, economics, symbolic regression, robot control, caching algorithms, combinatorial optimization, neural network construction, solution of differential equations, etc. The aim of this Topic is to present recent new ideas in the Grammatical Evolution technique and demonstrate new applications of the method. Also, software specialized in grammatical evolution can be presented and demonstrated.

Topics of interest include, but are not limited to:
• Application of grammatical evolution in signal processing;
• Neural network evolution using grammatical evolution;
• Classification techniques using grammatical evolution;
• Feature selection and construction using grammatical evolution;
• Solution of differential equations using grammatical evolution
• Parallel techniques for grammatical evolution;
• Robotics and grammatical evolution;
• New genetic operators used in Grammatical Evolution;
• Self-organizing maps and grammatical evolution;
• Grammatical evolution techniques used in image and video processing;
• Software specialized in grammatical evolution and its applications;
• Language inference with grammatical evolution;
• Probabilistic Context-Free Grammar (PCFG) and Grammatical Evolution;
• Evolving financial models using grammatical evolution;
• Application of Grammatical Evolution to Global optimization methods;

Keywords: grammatical evolution, algorithm, computer science, genetic programming

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