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

Improved Evolutionary Hybrids for Flexible Ligand Docking in Autodock

In this paper we evaluate the design of the hybrid evolutionary algorithms (EAs) that are currently used to perform flexible ligand binding in the Autodock docking software. Hybrid EAs incorporate specialized operators that exploit domain-specific features to accelerate an EA's search. We consider hybrid EAs that use an integrated local search operator to reline individuals within each iteration of the search. We evaluate several factors that impact the efficacy of a hybrid EA, and we propose new hybrid EAs that provide more robust convergence to low-energy docking configurations than the methods currently available in Autodock

Adaptive evolution in static and dynamic environments

This thesis provides a framework for describing a canonical evolutionary system. Populations of individuals are envisaged as traversing a search space structured by genetic and developmental operators under the influence of selection. Selection acts on individuals' phenotypic expressions, guiding the population over an evaluation landscape, which describes an idealised evaluation surface over the phenotypic space. The corresponding valuation landscape describes evaluations over the genotypic space and may be transformed by within generation adaptive (learning) or maladaptive (fault induction) local search. Populations subjected to particular genetic and selection operators are claimed to evolve towards a region of the valuation landscape with a characteristic local ruggedness, as given by the runtime operator correlation coefficient. This corresponds to the view of evolution discovering an evolutionarily stable population, or quasi-species, held in a state of dynamic equilibrium by the operator set and evaluation function. This is demonstrated by genetic algorithm experiments using the NK landscapes and a novel, evolvable evaluation function, The Tower of Babel. In fluctuating environments of varying temporal ruggedness, different operator sets are correspondingly more or less adapted. Quantitative genetics analyses of populations in sinusoidally fluctuating conditions are shown to describe certain well known electronic filters. This observation suggests the notion of Evolutionary Signal Processing. Genetic algorithm experiments in which a population tracks a sinusoidally fluctuating optimum support this view. Using a self-adaptive mutation rate, it is possible to tune the evolutionary filter to the environmental frequency. For a time varying frequency, the mutation rate reacts accordingly. With local search, the valuation landscape is transformed through temporal smoothing. By coevolving modifier genes for individual learning and the rate at which the benefits may be directly transmitted to the next generation, the relative adaptedness of individual learning and cultural inheritance according to the rate of environmental change is demonstrated

Aplicação de Algoritmos Evolucionários à Extracção de Padrões Musicais

Dissertação de Mestrado em Engenharia Informática apresentada á Faculdade de Ciências e Tecnologia da Universidade de Coimbra.A extracção de padrões é um problema que se coloca em várias áreas como, por exemplo, a biologia molecular ou a área financeira, e que pode ser considerado, do ponto de vista da inteligência artificial, como uma forma de aprendizagem não supervisionada. No domínio musical, o problema pode ser definido, informalmente, da seguinte forma: dada uma peça musical (ou várias), identificar as partes dessa peça que se repitam, aproximadamente ou não, e que possuam um significado relevante no contexto dessa peça. O objectivo deste trabalho consistiu em estudar a viabilidade da aplicação de algoritmos evolucionários ao problema da extracção de padrões musicais. Para levar a cabo o estudo proposto desenvolvemos duas abordagens utilizando dois tipos diferentes de algoritmos evolucionários: a programação genética e os algoritmos genéticos. Em cada uma das abordagens o objectivo é essencialmente o mesmo: encontrar uma segmentação de uma peça que permita identificar os padrões mais importantes nela existentes. Devido às características de cada um dos algoritmos, a representação utilizada para os indivíduos é diferente. Assim, enquanto que na abordagem baseada em programação genética cada indivíduo é um programa que produz como resultado uma determinada peça, constituindo ao mesmo tempo uma descrição da sua estrutura de segmentos, na abordagem baseada em algoritmos genéticos cada indivíduo consiste numa sequência de símbolos que representa uma hipótese de segmentação da peça a analisar. Embora as funções de avaliação utilizadas nas duas abordagens também sejam diferentes, ambas beneficiam os indivíduos que apresentem o conjunto dos padrões mais importantes existentes na peça. Para ambas as abordagens foi também desenvolvido um método que permite realizar uma segunda segmentação de uma peça a partir dos segmentos identificados na primeira segmentação. Os resultados experimentais obtidos com a abordagem baseada em programação genética que desenvolvemos permitem-nos verificar que esta abordagem apresenta bastantes dificuldades na resolução deste tipo de problemas. Pelo contrário, a abordagem baseada em algoritmos genéticos permitiu obter resultados que nos levam a considerar que a aplicação desta abordagem a este tipo de problemas é viável.Pattern extraction is a problem that occurs in several areas like, for example, molecular biology and finance, and can be viewed, from the point of view of artificial intelligence, as a kind of unsupervised learning. In the musical domain, the problem can be informally defined in the following way: given a musical piece (or more), identify the meaningful recurrent parts of that piece. The goal of this work is to study the viability of applying evolutionary algorithms to the problem of musical pattern extraction. In order to take this study, we develop two approaches based on two different types of evolutionary algorithms: genetic programming and genetic algorithms. The goal in both approaches is essentially the same: find a segmentation of a musical piece that allows the identification of the most meaningful patterns that exist in that piece. Due to the character of each type of algorithm, the representation used to represent individuals in each approach its different. Hence, while in the genetic programming based approach an individual is a program that produces as a result a musical piece, being at the same time a description of the structure of that piece, in the genetic algorithms based approach each individual is a sequence of symbols that represent a possible segmentation of the musical piece that is being analyzed. The two approaches also use different fitness functions, but both have in common the fact that they give a better fitness value to individuals that present the set of most meaningful patterns. For both approaches we also developed a method to make a second segmentation of a musical piece using the segments identified in the first segmentation. The experimental results obtained with the genetic programming based approach allowed us to verify that this approach has great difficulties in the resolution of this type of problems. On the contrary, with the genetic algorithms based approach we obtained results that allow us to believe that this approach can be useful in the resolution of this type of problems

Synthetic social relationships for computational entities

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (p. 179-189).Humans and many other animals form long term social relationships with each other. These relationships confer a variety of benefits upon us, both as individuals and as groups. Computational systems that can form social relationships like those formed by animals could reap many of the benefits of sociality, both within their own groups and in their interactions with people. This dissertation explores two main questions: *What kinds of internal and external representations are necessary for computational entities to form social relationships like those formed by animals? *How can people participate in and direct the relationships of these entities? To explore these questions, I designed and implemented a system by which computational entities may form simple social relationships. In particular, these synthetic social relationships are modeled after the social behavior of the gray wolf (Canis lupus). The system comprises a novel combination of simple models of emotion, perception and learning in an emotional memory-based mechanism for social relationship formation. The system also includes supporting technologies through which people may participate in and direct the relationships. The system was presented as an interactive installation entitled AlphaWolf in the Emerging Technologies program at SIGGRAPH 2001. This installation featured a pack of six virtual wolves - three fully autonomous adults and three semi-autonomous pups whom people could direct by howling, growling, whining or barking into microphones.(cont.) In addition to observing the interactions of several hundred SIGGRAPH participants, I performed two main evaluations of the AlphaWolf system - a 32-subject human user study and a set of simulations of resource exploitation among the virtual wolves. Results from these evaluations support the hypothesis that the AlphaWolf system enables the formation of social relationships among groups of computational entities and people, and that these relationships are beneficial to both the inter-machine interactions and the human-machine interactions in a variety of ways. This research represents one of many possible steps towards synthetic social relationships with the complexity of the relationships found in real wolves, let alone in humans. Much further work will be necessary to create entities who can fully engage us in our own social terms. The system presented here provides a basic scaffolding on which such entities may be built, including an implemented, real-time example; new ideas in directable characters and character-based interactive installations; a simple, ethologically plausible model of computational social relationships; and statistically significant support for these claims.by William Michael Tomlinson, Jr.Ph.D

The Role of Development in Genetic Algorithms

The developmental mechanisms transforming genotypic to phenotypic forms are typically omitted in formulations of genetic algorithms (GAs) in which these two representational spaces are identical. We argue that a careful analysis of developmental mechanisms is useful when understanding the success of several standard GA techniques, and can clarify the relationships between more recently proposed enhancements. We provide a framework which distinguishes between two developmental mechanisms --- learning and maturation --- while also showing several common effects on GA search. This framework is used to analyze how maturation and local search can change the dynamics of the GA. We observe that in some contexts, maturation and local search can be incorporated into the fitness evaluation, but illustrate reasons for considering them seperately. Further, we identify contexts in which maturation and local search can be distinguished from the fitness evaluation. The Role of Development in Geneti..