This paper introduces three approaches to using Evolutionary Computation (EC) in Music (namely, engineering, creative and musicological approaches) and discusses examples of representative systems that have been developed within the last decade, with emphasis on more recent and innovative works. We begin by reviewing engineering applications of EC in Music Technology such as Genetic Algorithms and Cellular Automata sound synthesis, followed by an introduction to applications where EC has been used to generate musical compositions. Next, we introduce ongoing research into EC models to study the origins of music and detail our own research work on modelling the evolution of melody.

Digital sound synthesizers, ubiquitous today in sound cards, software and dedicated hardware, use algorithms (Sound Synthesis Techniques, SSTs) capable of generating sounds similar to those of acoustic instruments and even totally novel sounds. The design of SSTs is a very hard problem. It is usually assumed that it requires human ingenuity to design an algorithm suitable for synthesizing a sound with certain characteristics. Many of the SSTs commonly used are the fruit of experimentation and a long refinement processes. A SST is determined by its “functional form ” and “internal parameters”. Design of SSTs is usually done by selecting a fixed functional form from a handful of commonly used SSTs, and performing a parameter estimation technique to find a set of internal parameters that will best emulate the target sound. A new approach for automating the design of SSTs is proposed. It uses a set of examples of the desired behavior of the SST in the form of “inputs + target sound”. The approach is capable of suggesting novel functional forms and their internal parameters, suited to follow closely the given examples.

A mathematical model for interactive sound synthesis based on the application of Genetic Algorithms (GA) is presented. The Evolutionary Sound Synthesis Method (ESSynth) generates sequences of waveform variants by the application of genetic operators on an initial population of waveforms. We describe how the waveforms can be treated as genetic code, the fitness evaluation methodology and how genetic operations such as crossover and mutation are used to produce generations of waveforms. Finally, we discuss the results evaluating the generated sounds.

We describe a system that maps the interaction between two people to control a genetic process for generating music. We start with a population of melodies encoded genetically. This population is allowed to breed every biological cycle creating new members of the population based upon the semantics of the spatial relationship between two people moving in a large, physical space. A pre-specified hidden melody is used to select a melody from the population to play every musical cycle. The overlapping of selected melodies provides an intriguing textured musical space.

Digital sound synthesizers, ubiquitous today in sound cards, software and dedicated hardware, use algorithms (Sound Synthesis Techniques, SSTs) capable of generating sounds similar to those of acoustic instruments and even totally novel sounds. The design of SSTs is a very hard problem. It is usually assumed that it requires human ingenuity to design an algorithm suitable for synthesizing a sound with certain characteristics. Many of the SSTs commonly used are the fruit of experimentation and a long refinement processes. A SST is determined by its functional form and internal parameters. Design of SSTs is usually done by selecting a fixed functional form from a handful of commonly used SSTs, and performing a parameter estimation technique to find a set of internal parameters that will best emulate the target sound. A new approach for automating the design of SSTs is proposed. It uses a set of examples of the desired behavior of the SST in the form of inputs + target sound. The approach is capable of suggesting novel functional forms and their intemal parameters, suited to follow closely the given examples. Design of a SST is stated as a search problem in the SST space (the space spanned by all the possible valid functional forms and intemal parameters, within certain limits to make it practical). This search is done using evolutionary methods; specifically, Genetic Programming (GP).

Many complex systems have the characteristic that we can classify objects in the system in some way, but that these classi cations are distributed through a parameter space in some complex fashion. In order for a human to get an understanding of the system, we would like to present this user with one example of an object for each class. Examples of such problems can be found in information retrieval, bioinformatics, computational geometry, computer-aided design, software testing and cellular automata. In this paper we will show how problems in all these areas can be put into a general framework of nding qualitative examples, and argue that general heuristic approaches to this type of problem are an important and neglected area of machine learning. We contrast this with some other well-studied problems, showing how this problem is distinct and investigating what we can learn from these problems. We then discuss some of the requirements for a heuristic to solve these problems,...

This paper explores the application of evolutionary techniques to the design of novel sounds and their characteristics during performance. It is based on the “selective breeding” paradigm and as such dispensing with the need for detailed knowledge of the Sound Synthesis Techniques involved, in order to design sounds that are novel and of musical interest. This approach has been used successfully on several SSTs therefore validating it as an Adaptive Sound Meta-synthesis Technique. Additionally, mappings between the control and the parametric space are evolved as part of the sound setup. These mappings are used during performance.

This paper describes the use of evolutionary and artificial life techniques in sound design and the development of performance mapping to facilitate the real-time manipulation of such sounds through some input device controlled by the performer. A concrete example of such a system is briefly described which allows musicians without detailed knowledge and experience of sound synthesis techniques to develop new sounds and performance manipulation mappings interactively according to their own aesthetic judgments.

Abstract not found

A wide variety of problem areas require algorithms that can find an representative example of each of a set of qualitative classes. Such problems are typically tackled by ad hoc heuristics. In this paper we discuss some examples of this kind of problem, and give a general metaheuristic based on a genetic algorithm with endogenous fitness for finding solutions to this kind of problem. This algorithm works by recognizing when the population is currently searching a fecund area of the search space and exploiting it, and moving rapidly on to other regions if the current area is barren or mined out. To test this algorithm we apply it to three test problems, and sketch how it can be applied to some real world applications.