Automatic composition of music by means of Grammatical Evolution

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in APL Quote Quad, http://dx.doi.org/10.1145/604444.602249Proceedings of the 2002 conference on APL: array processing languages: lore, problems, and applications (Madrid)This work describes how grammatical evolution may be applied to the domain of automatic composition. Our goal is to test this technique as an alternate tool for automatic composition. The AP440 auxiliary processor will be used to play music, thus we shall use a grammar that generates AP440 melodies. Grammar evolution will use fitness functions defined from several well-known single melodies to automatically generate AP440 compositions that are expected to sound like those composed by human musicians.This paper has been sponsored by the Spanish Interdepartmental Commission of Science and Technology (CICYT), project numbers TEL1999-0181 and TIC2001-0685-C02-1

Geometric Semantic Grammatical Evolution

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.Geometric Semantic Genetic Programming (GSGP) is a novel form of Genetic Programming (GP), based on a geometric theory of evolutionary algorithms, which directly searches the semantic space of programs. In this chapter, we extend this framework to Grammatical Evolution (GE) and refer to the new method as Geometric Semantic Grammatical Evolution (GSGE). We formally derive new mutation and crossover operators for GE which are guaranteed to see a simple unimodal fitness landscape. This surprising result shows that the GE genotypephenotype mapping does not necessarily imply low genotype-fitness locality. To complement the theory, we present extensive experimental results on three standard domains (Boolean, Arithmetic and Classifier)

gramEvol: Grammatical Evolution in R

We describe an R package which implements grammatical evolution (GE) for automatic program generation. By performing an unconstrained optimization over a population of R expressions generated via a user-defined grammar, programs which achieve a desired goal can be discovered. The package facilitates the coding and execution of GE programs, and supports parallel execution. In addition, three applications of GE in statistics and machine learning, including hyper-parameter optimization, classification and feature generation are studied

On the Locality of Grammatical Evolution

It is well known that using high-locality representations is important for efficient evolutionary search. This paper investigates the locality of the genotype-phenotype mapping (representation) used in grammatical evolution (GE). The results show that the representation used in GE has problems with locality as many neighboring genotypes do not correspond to neighboring phenotypes. Experiments with a simple local search strategy reveal that the GE representation leads to lower performance for mutationbased search approaches in comparison to standard GP representations. The results suggest that locality issues should be considered for further development of the representation used in GE

Computer-Generated music using grammatical evolution

This is an electronic version of the paper presented at the Middle Eastern Simulation Multiconference (MESM) held in Amman (Jordan) on 2008This paper proposes a new musical notation with Lindenmayer grammars, and describes the use of grammar evolution for the automatic generation of music expressed in this notation, with the normalized compression distance as the fitness function. The computer music generated tries to reproduce the style of a selected pre-existent piece of music. In spite of the simplicity of the algorithm, the procedure obtains interesting results.This work has been partially sponsored by the Spanish Ministry of Science and Technology (MCYT), project number TIC2002-01948

Evolving personalized content for Super Mario Bros using grammatical evolution

Adapting game content to a particular player's needs and expertise constitutes an important aspect in game design. Most research in this direction has focused on adapting game difficultyto keep the player engaged in the game. Dynamic difficulty adjustment, however, focuses on one aspect of the gameplay experience by adjusting the content to increase ordecrease perceived challenge. In this paper, we introduce a method for automatic level generation for the platform game Super Mario Bros using grammatical evolution. The grammatical evolution-based level generator is used to generate player-adapted content by employing an adaptation mechanism as a fitness function in grammatical evolution to optimizethe player experience of three emotional states: engagement, frustration and challenge. The fitness functions used are models of player experience constructed in our previous work from crowd-sourced gameplay data collected from over 1500 game sessions.peer-reviewe