Articles in the Community Feeds Category
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Evolutionary Computation, Volume 17, Issue 3, Page 307-342, Fall 2009.
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Evolutionary Computation, Volume 17, Issue 3, Page 343-377, Fall 2009.
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Evolutionary Computation, Volume 17, Issue 3, Page 411-436, Fall 2009.
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Evolutionary Computation, Volume 17, Issue 3, Page 437-453, Fall 2009.
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Evolutionary Computation, Volume 17, Issue 3, Page 275-306, Fall 2009.
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Evolutionary Computation, Volume 17, Issue 3, Page 379-409, Fall 2009.
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Pareto dominance, using a randomized sampling procedure combined with ε-dominance to favor a good distribution of the samples.
In the proposed method, sampled solutions keep their initial rank and solutions located within the virtually expanded ε-dominance
regions of the sampled solutions are demoted to an inferior rank. The parameter ε that determines the expanded regions of
dominance of the sampled solutions is adapted at each generation so that the number of best-ranked solutions is kept close
to a desired number that is expressed as a fraction of the population size. We enhance NSGA-II with the proposed method and
analyze its performance on MNK-Landscapes, showing that the adaptive method works effectively and that compared to NSGA-II
convergence and diversity of solutions can be improved remarkably on MNK-Landscapes with 3 ≤ M ≤ 10 objectives. Also, we compare the performance of Adaptive ε-Ranking with two representative many-objective evolutionary
algorithms on DTLZ continuous functions. Results on DTLZ functions with 3 ≤ M ≤ 10 objectives suggest that the three many-objective approaches emphasize different areas of objective space and could be
used as complementary strategies to produce a better approximation of the Pareto front.
- Content Type Journal Article
- Category Special Issue
- DOI 10.1007/s12065-009-0031-2
- Authors
- Hernán Aguirre, Shinshu University International Young Researcher Empowerment Center, Faculty of Engineering 4-17-1 Wakasato Nagano 380-8553 Japan
- Kiyoshi Tanaka, Shinshu University Faculty of Engineering 4-17-1 Wakasato Nagano 380-8553 Japan
- Journal Evolutionary Intelligence
- Online ISSN 1864-5917
- Print ISSN 1864-5909
- Journal Volume Volume 2
- Journal Issue Volume 2, Number 4 / December, 2009
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Special issue on simulated evolution and learning
- Content Type Journal Article
- Category Editorial
- DOI 10.1007/s12065-009-0033-0
- Authors
- Michael Kirley, The University of Melbourne Department of Computer Science and Software Engineering Melbourne Australia
- Mengjie Zhang, Victoria University School of Engineering and Computer Science Wellington New Zealand
- Xiaodong Li, RMIT University School of Computer Science and Information Technology Melbourne Australia
- Journal Evolutionary Intelligence
- Online ISSN 1864-5917
- Print ISSN 1864-5909
- Journal Volume Volume 2
- Journal Issue Volume 2, Number 4 / December, 2009
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Sono disponibili tre tesi/stage in collaborazione con Milestone (www.milestone.it) nell’ambito dei videogiochi di corse automobilistiche e motociclistiche. I candidati ideali devono avere una forte motivazione personale, passione per i videogiochi e conoscenza del C++
Per maggiori informazioni, inviare una mail a lanzi@elet.polimi.it
Comportamenti di Gruppo in Videogiochi di Corse Automobilistiche
Questa tesi prevede lo studio delle dinamiche dei [...]
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Evolutionary Computation, Volume 0, Issue 0, Page 1-18, Early Access.
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Evolutionary Computation, Volume 0, Issue 0, Page 1-16, Early Access.
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Evolutionary Computation, Volume 0, Issue 0, Page 1-17, Early Access.
