Nierhaus Algorithmic Composition

Paradigms of Automated Music Generation
1. Auflage 2009
ISBN: 978-3-211-75540-2
Verlag: Springer Vienna
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 287 Seiten

Algorithmic composition - composing by means of formalizable methods - has a century old tradition not only in occidental music history. This is the first book to provide a detailed overview of prominent procedures of algorithmic composition in a pragmatic way rather than by treating formalizable aspects in single works. In addition to an historic overview, each chapter presents a specific class of algorithm in a compositional context by providing a general introduction to its development and theoretical basis and describes different musical applications. Each chapter outlines the strengths, weaknesses and possible aesthetical implications resulting from the application of the treated approaches. Topics covered are: markov models, generative grammars, transition networks, chaos and self-similarity, genetic algorithms, cellular automata, neural networks and artificial intelligence are covered. The comprehensive bibliography makes this work ideal for the musician and the researcher alike.

Autoren/Hrsg.

Nierhaus, Gerhard

Inhaltsverzeichnis

1;Acknowledgements;5
2;Contents;7
3;Introduction;11
3.1;References;16
4;Historical Development of Algorithmic Procedures;17
4.1;2.1 Interdependencies;17
4.2;2.2 Development of Symbol, Writing System and Numeral System;19
4.3;2.3 Much Ado About Nothing – The Development of the Zero;23
4.4;2.4 The Formalization of Thinking Processes;25
4.5;2.5 A Truth Machine from the 13th Century;27
4.6;2.6 Early Approaches to Algorithmic Composition;31
4.7;2.7 A Utopia of an All-Embracing Representation of Knowledge;36
4.8;2.8 Calculating Machines;38
4.9;2.9 A New Numeral System for Automated Calculations;43
4.10;2.10 Replacing the Mechanistic Determinism;44
4.11;2.11 Language and Music Generators – A Book of Books;46
4.12;2.12 From the Loom to the Analytical Engine”;49
4.13;2.13 The Implementation of Logical Operations;54
4.14;2.14 On Formally Undecidable Propositions;55
4.15;2.15 From Census Collector to ChessWorld Champion;58
4.16;2.16 Automata and Computability;68
4.17;2.17 The Model of a Universal Computer;71
4.18;2.18 Programming;72
4.19;2.19 The Computer in Algorithmic Composition;73
4.20;References;75
5;Markov Models;77
5.1;3.1 Theoretical Basis;78
5.2;3.2 Hidden Markov Models;79
5.3;3.3 Markov Models in Algorithmic Composition;81
5.4;3.4 Hidden Markov Models in Algorithmic Composition;87
5.5;3.5 Synopsis;91
5.6;References;92
6;Generative Grammars;93
6.1;4.1 Generative Grammars as a Model of the Theory of Syntax;94
6.2;4.2 Generative Grammars in Algorithmic Composition;101
6.3;4.3 Synopsis;127
6.4;References;128
7;Transition Networks;131
7.1;5.1 Experiments in Musical Intelligence;132
7.2;5.2 Petri Nets;137
7.3;5.3 Synopsis;139
7.4;References;140
8;Chaos and Self-Similarity;141
8.1;6.1 Chaos Theory;141
8.2;6.2 Strange Attractors;144
8.3;6.3 Fractals;145
8.4;6.4 Lindenmayer Systems;147
8.5;6.5 Chaos and Self-Similarity in Algorithmic Composition;154
8.6;6.6 Lindenmayer Systems in Algorithmic Composition;158
8.7;6.7 Synopsis;163
8.8;References;165
9;Genetic Algorithms;167
9.1;7.1 The Biological Model;167
9.2;7.2 Genetic Algorithms as Stochastic Search Techniques;168
9.3;7.3 Genetic Programming;171
9.4;7.4 Genetic Algorithms in Algorithmic Composition;174
9.5;7.5 Synopsis;192
9.6;References;194
10;Cellular Automata;197
10.1;8.1 Historical Framework and Theoretical Basics;197
10.2;8.2 Types of Cellular Automata;199
10.3;8.3 Cellular Automata in Algorithmic Composition;205
10.4;8.4 Synopsis;211
10.5;References;213
11;Artificial Neural Networks;215
11.1;9.1 Theoretical Basis;216
11.2;9.2 Historical Development of Neural Networks;217
11.3;9.3 The Architecture of Neural Networks;218
11.4;9.4 Artificial Neural Networks in Algorithmic Composition;223
11.5;9.5 Synopsis;231
11.6;References;232
12;Artificial Intelligence;235
12.1;10.1 Algorithmic Composition in AI;238
12.2;10.2 Synopsis;264
12.3;References;265
13;Final Synopsis;269
13.1;11.1 Algorithmic composition as a genuine method of composition;269
13.2;11.2 The dominance of style imitation in algorithmic composition;272
13.3;11.3 Origins and characteristics of the treated procedures;274
13.4;11.4 Strategies of encoding, representation and musical mapping;276
13.5;11.5 The evaluation of generated material;279
13.6;11.6 Limits of algorithmic composition;280
13.7;11.7 Transpersonalization and systems of universal” validity;282
13.8;11.8 Concluding remark;282
13.9;References;283
14;Index;285

Leseproben

Chapter 7 Genetic Algorithms (p. 157-158)

Genetic algorithms as a particular class of evolutionary algorithms, i.e. strategies modeled on natural systems, are stochastic search techniques. The basic models were inspired by Darwin’s theory of evolution. Problem solving strategies result from the application of quasi-biological procedures in evolutionary processes. The terminology of genetic algorithms including “selection,” “mutation,” “survival of the fittest,” etc. illustrates the principles of these algorithms as well as their conceptual proximity to biological selection processes.

In the initial stages of their development, these principles took shape in two different models: From the 1960s on, Ingo Rechenberg and Hans-Paul Schwefel introduced the evolution strategies at the Technical University of Berlin, and in the 1970s, the Americans John H. Holland and David E. Goldberg developed genetic algorithms. Rechenberg and Schwefel’s models are based upon a graphic notation and were modeled on biological procedures for the development of technical optimization techniques. Holland and Goldberg’s genetic algorithms use the principles of coding and transmission of data in biological systems for modeling search strategies. These two approaches developed, to a great extent, separately from each other. For application in music, the problem solving strategies of the “American school” are applied, and for this reason, Rechenberg’s model will not be explained here in detail.

7.1 The Biological Model

DNA in a cell consists of chromosomes that are made up of genes. Genes describe amino acid sequences of proteins and are responsible for the development of different traits that become manifest in different ways by transferring genetic information. The total complement of genes is referred to as a genome. The entirety of an individual’s hereditary information is known by the term genotype and the specific manifestation of his or her features called a phenotype. Genetic variability is ensured by a population with differing genetic characteristics as well as a continuous adaptation to changing environmental conditions. Genetic variations are caused by a process called meiosis, by which the hereditary disposition of the parents is allocated differently to the cells off the offspring, as well as by mutation of the genes, chromosomes or the whole genome. According to Darwin’s theory of evolution, the competing behavior of living organisms promotes the passing on of the genetic information of the fittest, meaning those organisms best able to survive in a particular environment. Consequently, this leads to the survival of the fittest, a term which can also be found in the terminology of genetic algorithms as the fitness function.

7.2 Genetic Algorithms as Stochastic Search Techniques

Genetic algorithms, which model the evolutionary processes in computer simulation, are methods that are used to solve search and optimization problems. For the application of a genetic algorithm, domain-specific knowledge of the problem to be solved is not necessary. Therefore, this class of algorithms is especially suitable for tasks that are difficult to model mathematically or for problem domains that do not have an explicit superior rule system.

By analogy to the biological model, the respective computer program serves as the habitat that provides particular conditions for surviving and heredity. In this artificial living space, populations of individuals, or chromosomes, are produced whose adaptation to an objective, referred to as objective score, is examined by means of a fitness function.