Disney meets Darwin : an evolution-based interface for exploration and design of expressive animated behavior

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1994.Includes bibliographical references (leaves 68-70).by Jeffrey John Ventrella.M.S

Conflict resolution and a framework for collaborative interactive evolution

Abstract Interactive evolutionary computation (IEC) has proven useful in a variety of applications by combining the subjective evaluation of a user with the massive parallel search power of the genetic algorithm (GA). Here, we articulate a framework for an extension of IEC into collaborative interactive evolution, in which multiple users guide the evolutionary process. In doing so, we introduce the ability for users to combine their efforts for the purpose of evolving effective solutions to problems. This necessarily gives rise to the possibility of conflict between users. We draw on the salient features of the GA to resolve these conflicts and lay the foundation for this new paradigm to be used as a tool for conflict resolution in complex group-wise human-computer interaction tasks

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

Eye evolution simulation with a genetic algorithm based on the hypothesis of Nilsson and Pelger

The present work addresses for the first time the simulation of the evolution of an elemental eye by means of a simple genetic algorithm. The problem of the gradual evolution of a structure as complex as the eye was raised by Darwin, being still at the beginning of the 21st century a source of controversy between creationists and evolutionists. Taking as a starting point the paper of Nilsson and Pelger and their hypothesis that the evolution of the eye can be studied if we limit ourselves to its optical geometry, we show how eye evolution could take place gradually applying the principle of natural selection. Our model is limited to studying how an array of photosensitive epithelial cells is bent gradually to achieve a camera obscura

Multi-agent evolutionary systems for the generation of complex virtual worlds

Modern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex models are a requirement for the successful delivery of many scenes and environments. While workflows such as rendering, compositing and animation have been streamlined to accommodate increasing demands, modelling complex models is still a laborious task. This paper introduces the computational benefits of an Interactive Genetic Algorithm (IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive Evolutionary Computation. An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the effects of user fatigue by learning from the choices made by the human designer and directing the search accordingly. This workflow accelerates the layout and distribution of basic elements to form complex models. It captures the designer's intent through interaction, and encourages playful discovery

Kommunizierende Genetische Algorithmen: Durch Evolution zur Kooperation

Die Kooperation zwischen Menschen und Computern gewinnt in zahlreichen Problemstellungen mehr und mehr an Bedeutung. Ein wesentlicher Grund hierfür ist die ständig wachsende Komplexität relevanter Problemstellungen. Dadurch bedingt sind weder der Mensch noch der Computer alleine in der Lage, zufriedenstellende Lösungen zu entwickeln. Die Kombination der individuellen Fähigkeiten hat sich in vielen Bereichen als gewinnbringend erwiesen. Genetische Algorithmen (GA) als Repräsentanten der >Evolutionary Computation< stellen einen Ansatz zur Lösung hochkomplexer Optimierungsaufgaben dar, der sich an den Vorgängen der Evolution orientiert. Im Gegensatz zu vielen anderen Optimierungsverfahren bringen sie einige Eigenarten mit, die kooperative Erweiterungen einfach und erfolg-versprechend machen. Der vorgestellte kommunizierende Genetische Algorithmus kombiniert die Vorteile der GA mit der Fähigkeit zur Kooperation. Es gelingt bei seiner Verwendung, gute externe Vorschläge aufzunehmen, während schlechte Vorschläge keinerlei negative Auswirkungen zeigen. Diese Robustheit gegen Irrtümer und Fehleingaben macht den KGA zu einer idealen Basis für Programme zur kooperativen Problemlösung

Webpage design optimization using genetic algorithm driven CSS

In the rapid emergence of globalization, e-commerce, and internet accessibility in remote parts of the world, ongoing feedback and participation from site visitors are essential for attaining clear and effective communication on a web site. This thesis presents a computational experiment for optimizing design of a webpage in an evolutionary manner. Webpage personalization is viewed as a configuration problem whose goal is to determine the optimal presentation of a webpage while taking into account the preference of the web author (designer), layout constraints (web design/editing language: HTML, CSS), and viewer interaction with the browser. The study proposes use of genetic algorithm-driven Cascading Style Sheets (CSS) to assist the process of webpage design optimization. This method will engage visitors to remotely modify and enhance the style (type, layout and color) of web site to fit their aesthetic and functional representation of well-received design. The preference feedback from user will be stored in an application server for automated evolutionary selection process and reinitialized for the next generation of users. Through the experimentation of web prototype and user evaluation test, the implementation of this method is examined and the derived design solutions are analyzed based on web aesthetics, standards, and accessibility