Environments for sonic ecologies

This paper outlines a current lack of consideration for the environmental context of Evolutionary Algorithms used for the generation of music. We attempt to readdress this balance by outlining the benefits of developing strong coupling strategies between agent and en- vironment. It goes on to discuss the relationship between artistic process and the viewer and suggests a placement of the viewer and agent in a shared environmental context to facilitate understanding of the artistic process and a feeling of participation in the work. The paper then goes on to outline the installation ‘Excuse Me and how it attempts to achieve a level of Sonic Ecology through the use of a shared environmental context

Picbreeder: A Case Study in Collaborative Evolutionary Exploration of Design Space

For domains in which fitness is subjective or difficult to express formally, interactive evolutionary computation (IEC) is a natural choice. It is possible that a collaborative process combining feedback from multiple users can improve the quality and quantity of generated artifacts. Picbreeder, a large-scale online experiment in collaborative interactive evolution (CIE), explores this potential. Picbreeder is an online community in which users can evolve and share images, and most importantly, continue evolving others\u27 images. Through this process of branching from other images, and through continually increasing image complexity made possible by the underlying neuroevolution of augmenting topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC system. This paper discusses not only the strengths of the Picbreeder approach, but its challenges and shortcomings as well, in the hope that lessons learned will inform the design of future CIE systems

Biomorpher: interactive evolution for parametric design

Combining graph-based parametric design with metaheuristic solvers has to date focussed solely on performance based criteria and solving clearly defined objectives. In this paper, we outline a new method for combining a parametric modelling environment with an interactive Cluster-Orientated Genetic Algorithm (COGA). In addition to performance criteria, evolutionary design exploration can be guided through choice alone, with user motivation that cannot be easily defined. As well as numeric parameters forming a genotype, the evolution of whole parametric definitions is discussed through the use of genetic programming. Visualisation techniques that enable mixing small populations for interactive evolution with large populations for performance-based optimisation are discussed, with examples from both academia and industry showing a wide range of applications

ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game Design

Large language models (LLMs) have taken the scientific world by storm, changing the landscape of natural language processing and human-computer interaction. These powerful tools can answer complex questions and, surprisingly, perform challenging creative tasks (e.g., generate code and applications to solve problems, write stories, pieces of music, etc.). In this paper, we present a collaborative game design framework that combines interactive evolution and large language models to simulate the typical human design process. We use the former to exploit users' feedback for selecting the most promising ideas and large language models for a very complex creative task - the recombination and variation of ideas. In our framework, the process starts with a brief and a set of candidate designs, either generated using a language model or proposed by the users. Next, users collaborate on the design process by providing feedback to an interactive genetic algorithm that selects, recombines, and mutates the most promising designs. We evaluated our framework on three game design tasks with human designers who collaborated remotely.Comment: (Submitted

A Machine Learning Application Based on Giorgio Morandi Still-Life Paintings to Assist Artists in the Choice of 3D Compositions

The authors present a system built to generate arrangements of threedimensional models for aesthetic evaluation, with the aim being to support an artist in their creative process. The authors explore how this system can automatically generate aesthetically pleasing content for use in the media and design industry, based on standards originally developed in master artworks. They then demonstrate the effectiveness of their process in the context of paintings using a collection of images inspired by the work of the artist Giorgio Morandi (Bologna, 1890–1964). Finally, they compare the results of their system with the results of a well-known Generative Adversarial Network (GAN)

Feature selection and novelty in computational aesthetics

[Abstract] 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.Portugal. Fundação para a Ciência e a Tecnologia; PTDC/EIA–EIA/115667/2009Galicia.Consellería de Innovación, Industria e Comercio ; PGIDIT10TIC105008P

Evolutionary Decomposition of Complex Design Spaces

This dissertation investigates the support of conceptual engineering design through the decomposition of multi-dimensional search spaces into regions of high performance. Such decomposition helps the designer identify optimal design directions by the elimination of infeasible or undesirable regions within the search space. Moreover, high levels of interaction between the designer and the model increases overall domain knowledge and significantly reduces uncertainty relating to the design task at hand. The aim of the research is to develop the archetypal Cluster Oriented Genetic Algorithm (COGA) which achieves search space decomposition by using variable mutation (vmCOGA) to promote diverse search and an Adaptive Filter (AF) to extract solutions of high performance [Parmee 1996a, 1996b]. Since COGAs are primarily used to decompose design domains of unknown nature within a real-time environment, the elimination of apriori knowledge, speed and robustness are paramount. Furthermore COGA should promote the in-depth exploration of the entire search space, sampling all optima and the surrounding areas. Finally any proposed system should allow for trouble free integration within a Graphical User Interface environment. The replacement of the variable mutation strategy with a number of algorithms which increase search space sampling are investigated. Utility is then increased by incorporating a control mechanism that maintains optimal performance by adapting each algorithm throughout search by means of a feedback measure based upon population convergence. Robustness is greatly improved by modifying the Adaptive Filter through the introduction of a process that ensures more accurate modelling of the evolving population. The performance of each prospective algorithm is assessed upon a suite of two-dimensional test functions using a set of novel performance metrics. A six dimensional test function is also developed where the areas of high performance are explicitly known, thus allowing for evaluation under conditions of increased dimensionality. Further complexity is introduced by two real world models described by both continuous and discrete parameters. These relate to the design of conceptual airframes and cooling hole geometries within a gas turbine. Results are promising and indicate significant improvement over the vmCOGA in terms of all desired criteria. This further supports the utilisation of COGA as a decision support tool during the conceptual phase of design.British Aerospace plc, Warton and Rolls Royce plc, Filto