Cagnoni / Mirolli / Villani Evolution, Complexity and Artificial Life

2014
ISBN: 978-3-642-37577-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 280 Seiten, eBook

Evolution and complexity characterize both biological and artificial life – by direct modeling of biological processes and the creation of populations of interacting entities from which complex behaviors can emerge and evolve. This edited book includes invited chapters from leading scientists in the fields of artificial life, complex systems, and evolutionary computing. The contributions identify both fundamental theoretical issues and state-of-the-art real-world applications. The book is intended for researchers and graduate students in the related domains.

Zielgruppe

Research

Autoren/Hrsg.

Cagnoni, Stefano

Mirolli, Marco

Villani, Marco

Inhaltsverzeichnis

One Artefact – Many Phenomena.- Taming the Complexity of Natural and Artificial Evolutionary Dynamics.- Models of Gene Regulation: Integrating Modern Knowledge into the Random Boolean Network Framework.- Attractors Perturbations in Biological Modelling: Avalanches and Cellular Differentiation.- Automatic Design of Boolean Networks for Modeling Cell Differentiation.- Towards the Engineering of Chemical Communication Between Semi-synthetic and Natural Cells.- Cumulative Learning Through Intrinsic Reinforcements.- Development of Categorization Abilities in Evolving Embodied Agents: A Study of Internal Representations with External Social Inputs.- Regulatory Traits: Cultural Influences on Cultural Evolution.- Building up Serious Games with an Artificial Life Approach: Two Case Studies.- The Effects of Multivalency and Kinetics in Nanoscale Search by Molecular Spiders.- Towards the Use of Genetic Programming for the Prediction of Survival in Cancer.- A Neuro-evolutionary Approach to Electrocardiographic Signal Classification.- Self-organisation and Evolution for Trust-Adaptive Grid Computing Agents.- Honest vs. Cheating Bots in PATROL-Based Real-Time Strategy MMOGs.- Distribution Search on Evolutionary Many-Objective Optimization: Selection Mappings and Recombination Rate.- Concurrent Implementation Techniques Using Differential Evolution for Multi-core CPUs: A Comparative Study Using Statistical Tests.