Abstract. An approach to automate the design of sound synthesis

Genetic programming (Koza 1992) is a method of inducing behaviors represented as executable programs. The generality of the approach has spawned a proliferation of work in the evolution of executable structures that is unmatched in the history of the subject. This paper describes the standard approach to genetic programming, as defined in Koza (1992), and then presents the significant studies that preceded its inception as well as the diversification of techniques evolving executable structures that is currently underway in the field.

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).

This paper demonstrates the correctness of John Holland’s expectation that the genetic algorithm would have "applications to... artificial intelligence" by showing examples of the automatic creation of human-competitive computer programs from a high-level statement of a problem’s requirements. The paper argues that the field of design is a useful testbed for determining whether an automated technique can produce results that are competitive with humanproduced results and then presents several results that are competitive with the products of human creativity and inventiveness.

The design (synthesis) of analog electrical circuits entails the creation of both the topology and sizing (numerical values) of all of the circuit's components. There has previously been no general automated technique for automatically designing an analog electrical circuit from a high-level statement of the circuit's desired behavior. This paper shows how genetic programming can be used to automate the design of both the topology and sizing of a suite of five prototypical analog circuits, including a lowpass filter, a tri-state frequency discriminator circuit, a 60 dB amplifier, a computational circuit for the square root, and a timeoptimal robot controller circuit. All five of these genetically evolved circuits constitute instances of an evolutionary computation technique solving a problem that is usually thought to require human intelligence.

This paper describes a unified and automated design method for synthesizing designs for multi-domain systems, such as mechatronic systems. A multi-domain dynamic system, of which a mechatronic system is a popular example, includes a combination of components drawn from electrical, mechanical, hydraulic, pneumatic, and/or thermal systems, making it difficult to design a system to meet specified performance goals with a single design tool. The multi-domain design approach is not only efficient for mixed-domain problems, but is also useful for addressing separate single-domain design problems with a single tool. Bond graphs are domain independent, allow free composition, and allow efficient classification and analysis of models, permitting rapid determination of various types of acceptability or feasibility of candidate designs. This can sharply reduce the time needed for analysis of designs that are infeasible or otherwise unattractive. Genetic programming is well recognized as a powerful tool for topologically open-ended search. The combination of these two methods is therefore an appropriate target for a better system for synthesis of complex multi-domain systems. The approach described here will evolve new designs (represented as bond graphs) with ever-improving performance, in an iterative loop of synthesis, analysis, and feedback to the synthesis process. The suggested design methodology has been applied here to three design examples. The first is domain independent – an eigenvalues-placement design problem which is tested for some sample target sets of eigenvalues. The second is in the electrical domain – namely, design of analog filters to achieve specified performance over a given frequency range. The third is in the mechanical domain – design of an electric typewriter drive system to obtain the required steady state rotational position of the printing head for each character to be printed. 1.

This dissertation concerns the principles and techniques for scalable evolutionary computation to achieve better solutions for larger problems with more computational resources. It suggests that many of the limitations of existent evolutionary algorithms, such as premature convergence, stagnation, loss of diversity, lack of reliability and efficiency, are derived from the fundamental convergent evolution model, the oversimplified “survival of the fittest” Darwinian evolution model. Within this model, the higher the fitness the population achieves, the more the search capability is lost. This is also the case for many other conventional search techniques. The main result of this dissertation is the introduction of a novel sustainable evolution model, the Hierarchical Fair Competition (HFC) model, and corresponding five sustainable evolutionary algorithms (EA) for evolutionary search. By maintaining individuals in hierarchically organized fitness levels and keeping evolution going at all fitness levels, HFC transforms the conventional convergent evolutionary computation model into a sustainable search framework by ensuring a continuous supply and incorporation of low-level building blocks and by culturing and maintaining building blocks of intermediate levels with its

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Today’s reconfigurable technology provides vast parallelism that may be exploited in the design of a cellular computing machine. In this work a virtual Sblock FPGA is implemented on an existing FPGA, achieving not only an architecture in keeping with cellular computing principles but also suited to biologically inspired design methods. The design method proposed is a combination of evolution and development and results of running a developmental model on the cellular computing machine are presented.