Buch, Englisch, Band 674, 226 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 371 g
Buch, Englisch, Band 674, 226 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 371 g
Reihe: Advances in Intelligent Systems and Computing
ISBN: 978-3-319-69708-6
Verlag: Springer International Publishing
This book comprises selected research papers from the 2015 edition of the EVOLVE conference, which was held on June 18–June 24, 2015 in Ia?i, Romania. It presents the latest research on Probability, Set Oriented Numerics, and Evolutionary Computation. The aim of the EVOLVE conference was to provide a bridge between probability, set oriented numerics and evolutionary computation and to bring together experts from these disciplines. The broad focus of the EVOLVE conference made it possible to discuss the connection between these related fields of study computational science. The selected papers published in the proceedings book were peer reviewed by an international committee of reviewers (at least three reviews per paper) and were revised and enhanced by the authors after the conference. The contributions are categorized into five major parts, which are:
Multicriteria and Set-Oriented Optimization; Evolution in ICT Security; Computational Game Theory; Theory on Evolutionary Computation; Applications of Evolutionary Algorithms.
The 2015 edition shows a major progress in the aim to bring disciplines together and the research on a number of topics that have been discussed in previous editions of the conference matured over time and methods have found their ways in applications. In this sense the book can be considered an important milestone in bridging and thereby advancing state-of-the-art computational methods.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Aggregate Selection in Multi-Objective Biochemical Optimization via the Average Cuboid Volume Indicator. - On Gradient-based and Swarm-based Algorithms for Set-oriented Bicriteria Optimization. - Quadcriteria Optimization of Binary Classifiers: Error Rates, Coverage, and Complexity. - Parameter Identification of Stochastic Gene Regulation Models by Indicator-based Evolutionary Level Set Approximation
