João Correia

Evolutionary and Biologically Inspired Music, Sound, Art and Design

Generative Music with Stochastic Diffusion Search.- Music with Unconventional Computing: Towards a Step Sequencer from Plasmodium of Physarum Polycephalum.- Feature Discovery by Deep Learning for Aesthetic Analysis of Evolved Abstract Images.- FuXi: A Fish-Driven Instrument for Real-Time Music Performance.- Chorale Music Splicing System: An Algorithmic Music Composer Inspired by Molecular Splicing.- Towards an Evolutionary Computational Approach to Articulatory Vocal Synthesis with PRAAT.- The Sound Digestive System: A Strategy for Music and Sound Composition.- Avoidance Drawings Evolved Using Virtual Drawing Robots.- A Genetic Programming Approach to Generating Musical Compositions.- AudioInSpace: Exploring the Creative Fusion of Generative Audio, Visuals and Gameplay.- Toward Certain Sonic Properties of an Audio Feedback System by Evolutionary Control of Second-Order Structures.- Echo.- Evotype: Evolutionary Type Design.- Interior Illumination Design Using Genetic Programming.- Lichtsuchende: Exploring the Emergence of a Cybernetic Society.- Automatic Generation of Chord Progressions with an Artificial Immune System.- Evolving Diverse Design Populations Using Fitness Sharing and Random Forest Based Fitness Approximation.- Moody Music Generator: Characterising Control Parameters Using Crowdsourcing.- Schemographe: Application for a New Representation Technique and Methodology of Analysis in Tonal Harmony.- Biological Content Generation: Evolving Game Terrains Through Living Organisms.- Interpretability of Music Classification as a Criterion for Evolutionary Multi-objective Feature Selection.- On the Stylistic Evolution of a Society of Virtual Melody Composers.- DrawCompileEvolve: Sparking Interactive Evolutionary Art with Human Creations.

Computerized measures of visual complexity.

Visual complexity influences peoples perception of, preference for, and behaviour toward many classes of objects, from artworks to web pages. The ability to predict peoples impression of the complexity of different kinds of visual stimuli holds, therefore, great potential for many domains, basic and applied. Here we use edge detection operations and several image metrics based on image compression error and Zipfs law to estimate the visual complexity of images. The experiments involved 800 images, each previously rated by thirty participants on perceived complexity. In a first set of experiments we analysed the correlation of individual features with the average human response, obtaining correlations up to rs = .771. In a second set of experiments we employed Machine Learning techniques to predict the average visual complexity score attributed by humans to each stimuli. The best configurations obtained a correlation of rs = .832. The average prediction error of the Machine Learning system over the set of all stimuli was .096 in a normalized 0 to 1 interval, showing that it is possible to predict, with high accuracy human responses. Overall, edge density and compression error were the strongest predictors of human complexity ratings.

Computing Aesthetics with Image Judgement Systems

The ability of human or artificial agents to evaluate their works, as well as the works of others, is an important aspect of creative behaviour, possibly even a requirement. In artistic fields such as visual arts and music, this evaluation capacity relies, at least partially, on aesthetic judgement. This chapter analyses issues regarding the development of computational systems that perform aesthetic judgements focusing on their validation. We present several alternatives, as follows: the use of psychological tests related to aesthetic judgement; the testing of these systems in style recognition tasks; and the assessment of the system’s ability to predict the users’ valuations or the popularity of a given work. An adaptive system is presented and its performance assessed using the above-mentioned validation methodologies.

Expression-Based evolution of faces

The combination of a classifier system with an evolutionary image generation engine is explored. The framework is instantiated using an off-the-shelf face detection system and a general purpose, expression-based, genetic programming engine. By default, the classifier returns a binary output, which is inadequate to guide evolution. By retrieving information provided by intermediate results of the classification task, it became possible to develop a suitable fitness function. The experimental results show the ability of the system to evolve images that are classified as faces. A subjective analysis also reveals the unexpected nature and artistic potential of the evolved images.

Fitness and Novelty in Evolutionary Art

In this paper the effects of introducing novelty search in evolutionary art are explored. Our algorithm combines fitness and novelty metrics to frame image evolution as a multi-objective optimisation problem, promoting the creation of images that are both suitable and diverse. The method is illustrated by using two evolutionary art engines for the evolution of figurative objects and context free design grammars. The results demonstrate the ability of the algorithm to obtain a larger set of fit images compared to traditional fitness-based evolution, regardless of the engine used.

Evotype: Evolutionary Type Design

An evolutionary generative system for type design, Evotype, is described. The system uses a Genetic Algorithm to evolve a set of individuals composed of line segments, each encoding the shape of a specific character, i.e. a glyph. To simultaneously evolve glyphs for the entire alphabet, an island model is adopted. To assign fitness we resort to a scheme based on Optical Character Recognition. We study the evolvability of the proposed approach as well as the impact of the migration in the evolutionary process. The migration mechanism is explored through three experimental setups: fitness guided migration, random migration, and no migration. We analyse the experimental results in terms of fitness, migration paths, and appearance of the glyphs. The results show the ability of the system to find suitable glyphs and the impact of the migration strategy in the evolutionary process.

Improving face detection

A novel Genetic Programming approach for the improvement of the performance of classifier systems through the synthesis of new training instances is presented. The approach relies on the ability of the Genetic Programming engine to identify and exploit shortcomings of classifier systems, and generate instances that are misclassified by them. The addition of these instances to the training set has the potential to improve classifiers performance. The experimental results attained with face detection classifiers are presented and discussed. Overall they indicate the success of the approach.

Graph-Based Evolutionary Art

A graph-based approach for the evolution of Context Free Design Grammars is presented. Each genotype is a directed hierarchical graph and, as such, the evolutionary engine employs graph-based crossover and mutation. We introduce six different fitness functions based on evolutionary art literature and conduct a wide set of experiments. We begin by assessing the adequacy of the system and establishing the experimental parameters. Afterwards, we conduct evolutionary runs using each fitness function individually. Finally, experiments where a combination of these functions is used to assign fitness are performed. Overall, the experimental results show the ability of the system to optimize the considered functions, individually and combined, and to evolve images that have the desired visual characteristics.

Semantic aware methods for evolutionary art

In the past few years the use of semantic aware crossover and mutation has become a hot topic of research within the Genetic Programming community. Unlike traditional genetic operators that perform syntactic manipulations of programs regardless of their behavior, semantic driven operators promote direct search on the underlying behavioral space. Based on previous work on semantic Genetic Programming and Genetic Morphing, we propose and implement semantic driven crossover and mutation operators for evolutionary art. The experimental results focus on assessing how these operators compare with traditional ones.

Feature selection and novelty in computational aesthetics

An approach for exploring novelty in expression-based evolutionary art systems is presented. The framework is composed of a feature extractor, a classifier, an evolutionary engine and a supervisor. The evolutionary engine exploits shortcomings of the classifier, generating misclassified instances. These instances update the training set and the classifier is re-trained. This iterative process forces the evolutionary algorithm to explore new paths leading to the creation of novel imagery. The experiments presented and analyzed herein explore different feature selection methods and indicate the validity of the approach.