TY  - BOOK
AU  - Tantar,Alexandru-Adrian
AU  - Tantar,Emilia
AU  - Sun,Jian-Qiao
AU  - Zhang,Wei
AU  - Ding,Qian
AU  - Schütze,Oliver
AU  - Emmerich,Michael
AU  - Legrand,Pierrick
AU  - Del Moral,Pierre
AU  - Coello Coello,Carlos A.
ED  - SpringerLink (Online service)
TI  - EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V
T2  - Advances in Intelligent Systems and Computing,
SN  - 9783319074948
AV  - Q342 
U1  - 006.3 23
PY  - 2014///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Engineering
KW  - Artificial intelligence
KW  - Computational Intelligence
KW  - Artificial Intelligence (incl. Robotics)
N1  - Set Oriented Numerics -- Computational Game Theory -- Machine Learning Applied to Networks -- Complex Networks and Landscape Analysis -- Local Search and Optimization -- Genetic Programming -- Evolutionary Multiobjective Optimization -- Practical Aspects of Evolutionary Algorithms
N2  - This volume encloses research articles that were presented at the EVOLVE 2014 International Conference in Beijing, China, July 1–4, 2014.The book gathers contributions that emerged from the conference tracks, ranging from probability to set oriented numerics and evolutionary computation; all complemented by the bridging purpose of the conference, e.g. Complex Networks and Landscape Analysis, or by the more application oriented perspective. The novelty of the volume, when considering the EVOLVE series, comes from targeting also the practitioner’s view. This is supported by the Machine Learning Applied to Networks and Practical Aspects of Evolutionary Algorithms tracks, providing surveys on new application areas, as in the networking area and useful insights in the development of evolutionary techniques, from a practitioner’s perspective. Complementary to these directions, the conference tracks supporting the volume, follow on the individual advancements of the subareas constituting the scope of the conference, through the Computational Game Theory, Local Search and Optimization, Genetic Programming, Evolutionary Multi-objective optimization tracks
UR  - http://dx.doi.org/10.1007/978-3-319-07494-8
ER  -