Learning and intelligent optimization : 9th International Conference, LION 9, Lille, France, January 12-15, 2015. Revised selected papers / Clarisse Dhaenens, Laetitia Jourdan, Marie-Eléonore Marmion (eds.).
Material type:
TextSeries: Lecture notes in computer science ; 8994. | LNCS sublibrary. SL 1, Theoretical computer science and general issues.Publisher: Cham : Springer, 2015Description: 1 online resource (xi, 313 pages) : illustrationsContent type: - text
- computer
- online resource
- 9783319190846
- 3319190849
- 3319190830
- 9783319190839
- LION 9
- Machine learning -- Congresses
- Apprentissage automatique -- Congrès
- Computer programming -- software development
- Artificial intelligence
- Mathematical theory of computation
- User interface design & usability
- Computer modelling & simulation
- Algorithms & data structures
- Computers -- Programming -- General
- Computers -- Intelligence (AI) & Semantics
- Computers -- Programming -- Algorithms
- Computers -- Machine Theory
- Computers -- Computer Simulation
- Machine learning
- 006.3/1 23
- Q325.5 L56 2015eb
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|
eBook
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e-Library | eBook LNCS | Available |
Includes author index.
Online resource; title from PDF title page (SpringerLink, viewed June 25, 2015).
Benchmark problems and performance measures -- Tracking moving optima -- Dynamic multiobjective optimization -- Adaptation, learning, and anticipation -- Handling noisy fitness functions -- Using fitness approximations -- Searching for robust optimal solutions -- Comparative studies -- Hybrid approaches -- Theoretical analysis -- Real-world applications.
This book constitutes the thoroughly refereed post-conference proceedings of the 9th International Conference on Learning and Optimization, LION 9, which was held in Lille, France, in January 2015. The 31 contributions presented were carefully reviewed and selected for inclusion in this book. A large variety of topics are covered, such as benchmark problems and performance measures; tracking moving optima; dynamic multiobjective optimization; adaptation, learning, and anticipation; handling noisy fitness functions; using fitness approximations; searching for robust optimal solutions; comparative studies; hybrid approaches; theoretical analysis; and real-world applications.