Generative sound art as poeitic poetry for an information society

This paper considers computer music in relation to broader society and asks what algorithmic composition can learn from the metaphysical shift which is happening in the so-called information societies. This is explored by taking the mapping problem inherent in the use of extra- musical models in generative composition and presenting a simple generative schema which prioritises sound, ex- ploiting the generative potential of digital audio. It is sug- gested that the exploration of such models has more than aesthetic relevance and that the interdisciplinary nature of digital sound art represents a microcosm of an emerging reality, thereby constituting a poietic playground for com- ing to terms with the implications and challenges of the information age

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them

Swarm-Based Spatial Sorting

Purpose: To present an algorithm for spatially sorting objects into an annular structure. Design/Methodology/Approach: A swarm-based model that requires only stochastic agent behaviour coupled with a pheromone-inspired "attraction-repulsion" mechanism. Findings: The algorithm consistently generates high-quality annular structures, and is particularly powerful in situations where the initial configuration of objects is similar to those observed in nature. Research limitations/implications: Experimental evidence supports previous theoretical arguments about the nature and mechanism of spatial sorting by insects. Practical implications: The algorithm may find applications in distributed robotics. Originality/value: The model offers a powerful minimal algorithmic framework, and also sheds further light on the nature of attraction-repulsion algorithms and underlying natural processes.Comment: Accepted by the Int. J. Intelligent Computing and Cybernetic

Numerical evidence for relevance of disorder in a Poland-Scheraga DNA denaturation model with self-avoidance: Scaling behavior of average quantities

We study numerically the effect of sequence heterogeneity on the thermodynamic properties of a Poland-Scheraga model for DNA denaturation taking into account self-avoidance, i.e. with exponent c_p=2.15 for the loop length probability distribution. In complement to previous on-lattice Monte Carlo like studies, we consider here off-lattice numerical calculations for large sequence lengths, relying on efficient algorithmic methods. We investigate finite size effects with the definition of an appropriate intrinsic length scale x, depending on the parameters of the model. Based on the occurrence of large enough rare regions, for a given sequence length N, this study provides a qualitative picture for the finite size behavior, suggesting that the effect of disorder could be sensed only with sequence lengths diverging exponentially with x. We further look in detail at average quantities for the particular case x=1.3, ensuring through this parameter choice the correspondence between the off-lattice and the on-lattice studies. Taken together, the various results can be cast in a coherent picture with a crossover between a nearly pure system like behavior for small sizes N < 1000, as observed in the on-lattice simulations, and the apparent asymptotic behavior indicative of disorder relevance, with an (average) correlation length exponent \nu_r >= 2/d (=2).Comment: Latex, 33 pages with 15 postscript figure