EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV
Subtitle: International Conference held at Leiden University, July 10-13, 2013
By: edited by Michael Emmerich, Andre Deutz, Oliver Schuetze, Thomas Bäck, Emilia Tantar, Alexandru-Adrian Tantar, Pierre Del Moral, Pierrick Legrand, Pascal Bouvry, Carlos A. Coello
Contributors: Deutz, Andre; Schuetze, Oliver; Bäck, Thomas; Tantar, Emilia; Tantar, Alexandru-Adrian; Moral, Pierre Del; Legrand, Pierrick; Bouvry, Pascal; Coello, Carlos A.; SpringerLink (Online service)
Series: Advances in Intelligent Systems and Computing -- 227
Publisher: Springer International Publishing; Imprint: Springer
Published: 2013
Place of publication: Heidelberg
Language: eng
Format: [electronic resource] :
Number of pages: 324
ISBN-13: 9783319011288
Subjects: Engineering; Computational intelligence; Artificial intelligence; Artificial Intelligence (incl. Robotics)
Description: Numerical and computational methods are nowadays used in a wide range of contexts in complex systems research, biology, physics, and engineering.  Over the last decades different methodological schools have emerged with emphasis on different aspects of computation, such as nature-inspired algorithms, set oriented numerics, probabilistic systems and Monte Carlo methods. Due to the use of different terminologies and emphasis on different aspects of algorithmic performance there is a strong need for a more integrated view and opportunities for cross-fertilization across particular disciplines. These proceedings feature 20 original publications from distinguished authors in the cross-section of computational sciences, such as machine learning algorithms and probabilistic models, complex networks and fitness landscape analysis, set oriented numerics and cell mapping, evolutionary multiobjective optimization, diversity-oriented search, and the foundations of genetic programming algorithms. By presenting cutting edge results with a strong focus on foundations and integration aspects this work presents a stepping stone towards efficient, reliable, and well-analyzed methods for complex systems management and analysis.
Table of contents: title: Machine Learning and Probabilistic Models; title: Complex Networks and Evolutionary Computation; title: Diversity Oriented Optimization; title: Set-oriented Numerics and Evolutionary Multiobjective Optimization; title: Genetic Programming; title: Robust Optimization.