Testing genetic algorithm, recombination strategies and the normalized compression distance for computer-generated music

This is an electronic version of the paper presented at the WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED 2006) held in Madrid (Spain) on 2006This paper analyzes the use of the normalized compression distance as a fitness function for the automatic generation of music by means of genetic algorithms, and tests the effect on performance of several genetic recombination procedures. The minimization of the distance of the generated music to a set of musical guides or targets makes it possible to obtain computer-generated music that reminds the style of a certain human author. In spite of the simplicity of the algorithm, the procedure obtains interesting results. The paper includes some considerations on the use of procedures that improve the performance of heavytailed distribution processes.This work has been sponsored by the Spanish Ministry of Education and Science (MEC), project number TSI2005-08225-C07-06

Grammatical Evolution with Restarts for Fast Fractal Generation

In a previous work, the authors proposed a Grammatical Evolution algorithm to automatically generate Lindenmayer Systems which represent fractal curves with a pre-determined fractal dimension. This paper gives strong statistical evidence that the probability distributions of the execution time of that algorithm exhibits a heavy tail with an hyperbolic probability decay for long executions, which explains the erratic performance of different executions of the algorithm. Three different restart strategies have been incorporated in the algorithm to mitigate the problems associated to heavy tail distributions: the first assumes full knowledge of the execution time probability distribution, the second and third assume no knowledge. These strategies exploit the fact that the probability of finding a solution in short executions is non-negligible and yield a severe reduction, both in the expected execution time (up to one order of magnitude) and in its variance, which is reduced from an infinite to a finite value.Comment: 26 pages, 13 figures, Extended version of the paper presented at ANNIE'0