IWLCS 2009 review
[Aug 6, 2009 | No Comment | Posted by Jaume Bacardit ]
IWLCS 2009 review

By Will Browne, Jan Drugowitsch and Jaume Bacardit
The 12th International Workshop on Learning Classifier Systems (LCS) successfully took place on July 9th, 2009 in Montreal, Canada as part of GECCO 09. Its ’success’ was measured in terms of number of attendees – multiple times the number of presenters, quality of papers, diversity of topics, originality [...]

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Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems
[Nov 20, 2009 | Comments Off | Posted by Community ]

Evolutionary Computation, Volume 17, Issue 3, Page 307-342, Fall 2009.

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Estimating the Ratios of the Stationary Distribution Values for Markov Chains Modeling Evolutionary Algorithms
[Nov 20, 2009 | Comments Off | Posted by Community ]

Evolutionary Computation, Volume 17, Issue 3, Page 343-377, Fall 2009.

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A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization
[Nov 20, 2009 | Comments Off | Posted by Community ]

Evolutionary Computation, Volume 17, Issue 3, Page 411-436, Fall 2009.

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Locating and Characterizing the Stationary Points of the Extended Rosenbrock Function
[Nov 20, 2009 | Comments Off | Posted by Community ]

Evolutionary Computation, Volume 17, Issue 3, Page 437-453, Fall 2009.

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Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy
[Nov 20, 2009 | Comments Off | Posted by Community ]

Evolutionary Computation, Volume 17, Issue 3, Page 275-306, Fall 2009.

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Localization for Solving Noisy Multi-Objective Optimization Problems
[Nov 20, 2009 | Comments Off | Posted by Community ]

Evolutionary Computation, Volume 17, Issue 3, Page 379-409, Fall 2009.

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Adaptive ε-Ranking on many-objective problems
[Nov 18, 2009 | Comments Off | Posted by Community ]

Abstract  This work proposes Adaptive ε-Ranking to enhance Pareto based selection, aiming to develop effective many-objective evolutionary optimization algorithms. ε-Ranking fine grains ranking of solutions after they have been ranked by
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

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Special issue on simulated evolution and learning
[Nov 17, 2009 | Comments Off | Posted by Community ]

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

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Tesi/Stage Disponibili su Sviluppo di Videogiochi
[Nov 17, 2009 | Comments Off | Posted by Pier Luca Lanzi ]

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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