LCS & GBML Central http://lcs-gbml.ncsa.uiuc.edu The stop for the LCS and GBML community Fri, 20 Nov 2009 18:46:49 +0000 http://wordpress.org/?v=2.8.4 en hourly 1 Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.307?ai=t9&mi=0&af=R http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.307?ai=t9&mi=0&af=R#comments Fri, 20 Nov 2009 18:46:49 +0000 Community http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.307?ai=t9&mi=0&af=R Related posts:
  1. Competitive Coevolutionary Learning of Fuzzy Systems for Job Exchange in Computational Grids
  2. Adaptive Cellular Memetic Algorithms
  3. An Enhanced Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production
]]> ]]> http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.307?ai=t9&mi=0&af=R/feed/ 0 Estimating the Ratios of the Stationary Distribution Values for Markov Chains Modeling Evolutionary Algorithms http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.343?ai=t9&mi=0&af=R http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.343?ai=t9&mi=0&af=R#comments Fri, 20 Nov 2009 18:46:43 +0000 Community http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.343?ai=t9&mi=0&af=R Related posts:
  1. Locating and Characterizing the Stationary Points of the Extended Rosenbrock Function
  2. Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy
  3. A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization
]]> ]]> http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.343?ai=t9&mi=0&af=R/feed/ 0 A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.411?ai=t9&mi=0&af=R http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.411?ai=t9&mi=0&af=R#comments Fri, 20 Nov 2009 18:46:33 +0000 Community http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.411?ai=t9&mi=0&af=R Related posts:
  1. Localization for Solving Noisy Multi-Objective Optimization Problems
  2. Multi-Objective Optimization with Controlled Model Assisted Evolution Strategies
  3. On the Use of Problem-Specific Candidate Generators for the Hybrid Optimization of Multi-Objective Production Engineering Problems
]]> ]]> http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.411?ai=t9&mi=0&af=R/feed/ 0 Locating and Characterizing the Stationary Points of the Extended Rosenbrock Function http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.437?ai=t9&mi=0&af=R http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.437?ai=t9&mi=0&af=R#comments Fri, 20 Nov 2009 18:46:25 +0000 Community http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.437?ai=t9&mi=0&af=R Related posts:
  1. Estimating the Ratios of the Stationary Distribution Values for Markov Chains Modeling Evolutionary Algorithms
  2. Automated Design of Image Operators that Detect Interest Points
  3. A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization
]]> ]]> http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.437?ai=t9&mi=0&af=R/feed/ 0 Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.275?ai=t9&mi=0&af=R http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.275?ai=t9&mi=0&af=R#comments Fri, 20 Nov 2009 18:46:16 +0000 Community http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.275?ai=t9&mi=0&af=R Related posts:
  1. A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization
  2. Estimating the Ratios of the Stationary Distribution Values for Markov Chains Modeling Evolutionary Algorithms
  3. Locating and Characterizing the Stationary Points of the Extended Rosenbrock Function
]]> ]]> http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.275?ai=t9&mi=0&af=R/feed/ 0 Localization for Solving Noisy Multi-Objective Optimization Problems http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.379?ai=t9&mi=0&af=R http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.379?ai=t9&mi=0&af=R#comments Fri, 20 Nov 2009 18:46:01 +0000 Community http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.379?ai=t9&mi=0&af=R Related posts:
  1. A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization
  2. On the Use of Problem-Specific Candidate Generators for the Hybrid Optimization of Multi-Objective Production Engineering Problems
  3. Multi-Objective Optimization with Controlled Model Assisted Evolution Strategies
]]> ]]> http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.379?ai=t9&mi=0&af=R/feed/ 0 Adaptive ε-Ranking on many-objective problems http://www.springerlink.com/content/xl84818652846w7p/ http://www.springerlink.com/content/xl84818652846w7p/#comments Wed, 18 Nov 2009 09:24:03 +0000 Community http://www.springerlink.com/content/xl84818652846w7p/
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
Related posts:
  1. Rule base and adaptive fuzzy operators cooperative learning of Mamdani fuzzy systems with multi-objective genetic algorithms
  2. Localization for Solving Noisy Multi-Objective Optimization Problems
  3. Multi-objective evolutionary learning of granularity, membership function parameters and rules of Mamdani fuzzy systems
]]>
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
]]> http://www.springerlink.com/content/xl84818652846w7p/feed/ 0 Special issue on simulated evolution and learning http://www.springerlink.com/content/8745t8420276p147/ http://www.springerlink.com/content/8745t8420276p147/#comments Tue, 17 Nov 2009 23:50:44 +0000 Community http://www.springerlink.com/content/8745t8420276p147/ 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
Related posts:
  1. The effects of time-varying rewards on the evolution of cooperation
  2. Numerical simplification for bloat control and analysis of building blocks in genetic programming
  3. Special issue on genetic fuzzy systems: new advances
]]> 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
]]> http://www.springerlink.com/content/8745t8420276p147/feed/ 0 Tesi/Stage Disponibili su Sviluppo di Videogiochi http://www.pierlucalanzi.net/?p=419 http://www.pierlucalanzi.net/?p=419#comments Tue, 17 Nov 2009 19:29:55 +0000 Pier Luca Lanzi http://www.pierlucalanzi.net/?p=419
  • Design e Sviluppo di Videogiochi: UBISOFT al Politecnico di Milano 5 Novembre ore 13:00
  • The main event in Singapore: John Holland takes the stage
  • Hybrid Evolutionary Optimization of Two-Stage Stochastic Integer Programming Problems: An Empirical Investigation
    • ]]>     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 comportamenti dei piloti in competizioni automobilistiche su circuito e lo sviluppo di una intelligenza artificiale in grado di imitare i comportamenti di un vero mevero pilota. In particolare, il lavoro di tesi si concentrerà su comportamenti di gruppo come i sorpassi, le manovre per evitare collisioni con altri veicoli, il recupero da situazioni di emergenza, ecc. Terminatala fase di analisi e l’implementazione di un prototipo, verrà valutata la possibilità di inserire tale implementazione all’interno di un videogioco di ultima generazione.

    Comportamenti di Gruppo in Videogiochi di Corse Motociclistiche
    Questa tesi prevede lo studio delle dinamiche dei comportamenti dei piloti in competizioni motociclistiche su circuito e lo sviluppo di una intelligenza artificiale in grado di imitare i comportamenti di un vero vero pilota. In particolare, il lavoro di tesi si concentrerà su comportamenti di gruppo come i sorpassi, le manovre per evitare collisioni con altre moto, il recupero da situazioni di emergenza, ecc. Terminata la fase di analisi e l’implementazione di un prototipo, verrà valutata la possibilità di inserire tale implementazione all’interno di un videogioco di ultima generazione.

    Intelligenza Artificiale in Videogiochi di Corse Motociclistiche
    La tesi prevede l’analisi e l’implementazione dell’intelligenza artificiale per un videogioco di corse motociclistiche. Il sistema dovrà essere in grado di eseguire in modo corretto ed efficace un insieme di manovre complesse quali: l’accelerazione da fermo, la frenata in situazioni limite, l’approccio ad una curva che preveda la fase di stacco, di impostazione, di percorrenza e di uscita, l’approccio a serie di curve, ecc. Si valuterà la possibilità di inserire tale implementazione all’interno di un videogioco di ultima generazione.

    ]]>     http://www.pierlucalanzi.net/?p=419/feed/ 0     On the Use of Problem-Specific Candidate Generators for the Hybrid Optimization of Multi-Objective Production Engineering Problems http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17405?ai=t9&mi=0&af=R http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17405?ai=t9&mi=0&af=R#comments Mon, 16 Nov 2009 16:52:54 +0000 Community http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17405?ai=t9&mi=0&af=R Related posts:
    1. Hybrid Evolutionary Optimization of Two-Stage Stochastic Integer Programming Problems: An Empirical Investigation
    2. Multi-Objective Optimization with Controlled Model Assisted Evolution Strategies
    3. Localization for Solving Noisy Multi-Objective Optimization Problems
    ]]>     ]]> http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17405?ai=t9&mi=0&af=R/feed/ 0     Hybrid Evolutionary Optimization of Two-Stage Stochastic Integer Programming Problems: An Empirical Investigation http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17404?ai=t9&mi=0&af=R http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17404?ai=t9&mi=0&af=R#comments Mon, 16 Nov 2009 16:52:51 +0000 Community http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17404?ai=t9&mi=0&af=R Related posts:
    1. On the Use of Problem-Specific Candidate Generators for the Hybrid Optimization of Multi-Objective Production Engineering Problems
    2. Multi-Objective Optimization with Controlled Model Assisted Evolution Strategies
    3. Localization for Solving Noisy Multi-Objective Optimization Problems
    ]]>     ]]> http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17404?ai=t9&mi=0&af=R/feed/ 0     Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17403?ai=t9&mi=0&af=R http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17403?ai=t9&mi=0&af=R#comments Mon, 16 Nov 2009 16:52:49 +0000 Community http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17403?ai=t9&mi=0&af=R Related posts:
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    3. Increasing the Production Accuracy of Profile Bending with Methods of Computational Intelligence
    ]]>     ]]> http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17403?ai=t9&mi=0&af=R/feed/ 0