Evolutionary Multi-Criterion Optimization, 1st ed. 2017 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings Theoretical Computer Science and General Issues Series
Coordonnateurs : Trautmann Heike, Rudolph Günter, Klamroth Kathrin, Schütze Oliver, Wiecek Margaret, Jin Yaochu, Grimme Christian
The 33 revised full papers presented together with 13 poster presentations were carefully reviewed and selected from 72 submissions. The EMO 2017 aims to discuss all aspects of EMO development and deployment, including theoretical foundations; constraint handling techniques; preference handling techniques; handling of continuous, combinatorial or mixed-integer problems; local search techniques; hybrid approaches; stopping criteria; parallel EMO models; performance evaluation; test functions and benchmark problems; algorithm selection approaches; many-objective optimization; large scale optimization; real-world applications; EMO algorithm implementations.
Date de parution : 02-2017
Ouvrage de 702 p.
15.5x23.5 cm
Thèmes d’Evolutionary Multi-Criterion Optimization :
Mots-clés :
big data; evolutionary algorithms; machine learning; numeric computing; parallel computing; algorithm analysis and problem complexity; artificial intelligence; cluster analysis; combinatoric problems; computer applications; evolutionary computation; expert knowledge integration; hybrid optimization; model-based optimization; multi-criteria decision making; multi-objective optimization; performance evaluation; quality of service; randomized search heuristics; visualization