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Special Section: Genetic Algorithms in Visual Art and Music
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Journal Articles
Publisher: Journals Gateway
Leonardo (2003) 36 (1): 51–54.
Published: 01 February 2003
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This paper describes a system that uses evolutionary computation to provide an interface to a complex sound-synthesis algorithm. The paper then considers a number of general issues to be considered when evolutionary computation is applied in artistic domains and the differences between interactive and non-interactive genetic algorithms.
Journal Articles
Publisher: Journals Gateway
Leonardo (2003) 36 (1): 55–59.
Published: 01 February 2003
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In this article, the author focuses on issues concerning musical composition practices in which emergent behavior is used to generate musical material, musical form or both. The author gives special attention to the potential of cellular automata and adaptive imitation games for music-making. The article begins by presenting two case-study systems, followed by an assessment of their role in the composition of a number of pieces. It then continues with a discussion in which the author suggests that adaptive imitation games may hold the key to fostering more effective links between evolutionary computation paradigms and creative musical processes.
Journal Articles
Publisher: Journals Gateway
Leonardo (2003) 36 (1): 43–45.
Published: 01 February 2003
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GenJam is an interactive genetic algorithm (GA) that models a human jazz improviser and performs regularly as the author's sideman on jazz gigs. GenJam learns to improvise full-chorus solos under the guidance of a human mentor and “trades fours” in real time with a human performer in “chase” choruses. In this article, the author first briefly describes GenJam's architecture, representations, genetic operators and performance characteristics. He then places GenJam in the context of a proposed taxonomy for GA-based music and art systems.
Journal Articles
Publisher: Journals Gateway
Leonardo (2003) 36 (1): 61–64.
Published: 01 February 2003
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The authors present a musical composition model that creates rhythmic patterns through a system based on genetic algorithms, involving the interaction of several artificial musicians. In this environment, various composer systems and human musicians may interact within a system based on artificial life.
Journal Articles
Publisher: Journals Gateway
Leonardo (2003) 36 (1): 47–50.
Published: 01 February 2003
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NEXTPITCH, a learning classifier system (LCS) using genetic algorithms, inductively learns to predict the next note in a musical melody. NEXTPITCH models human music learning by developing the rules that represent actual pitch transitions in the melody. In this article, the author addresses the issues of (1) the impact of the representation of a domain (the encoding of the characteristics of the field of study) on the performance of an LCS and (2) the classification of the input (the melodies to be learned) to an LCS in order to determine the highest percentage of correct next-note predictions.
Journal Articles
Publisher: Journals Gateway
Leonardo (2002) 35 (2): 175–184.
Published: 01 April 2002