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	<title>LCS &#38; GBML Central</title>
	<link>http://lcs-gbml.ncsa.uiuc.edu</link>
	<description>The stop for the LCS and GBML community</description>
	<lastBuildDate>Fri, 20 Nov 2009 18:46:49 +0000</lastBuildDate>
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		<title>Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems</title>
		<description>Evolutionary Computation, Volume 17, Issue 3, Page 307-342, Fall 2009. 
		
	

Related posts:Competitive Coevolutionary Learning of Fuzzy Systems for Job Exchange in Computational GridsAdaptive Cellular Memetic AlgorithmsAn Enhanced Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production </description>
		<link>http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.307?ai=t9&mi=0&af=R</link>
			</item>
	<item>
		<title>Estimating the Ratios of the Stationary Distribution Values for Markov Chains Modeling Evolutionary Algorithms</title>
		<description>Evolutionary Computation, Volume 17, Issue 3, Page 343-377, Fall 2009. 
		
	

Related posts:Locating and Characterizing the Stationary Points of the Extended Rosenbrock FunctionEvolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and TaxonomyA Preference-Based Evolutionary Algorithm for Multi-Objective Optimization </description>
		<link>http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.343?ai=t9&mi=0&af=R</link>
			</item>
	<item>
		<title>A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization</title>
		<description>Evolutionary Computation, Volume 17, Issue 3, Page 411-436, Fall 2009. 
		
	

Related posts:Localization for Solving Noisy Multi-Objective Optimization ProblemsMulti-Objective Optimization with Controlled Model Assisted Evolution StrategiesOn the Use of Problem-Specific Candidate Generators for the Hybrid Optimization of Multi-Objective Production Engineering Problems </description>
		<link>http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.411?ai=t9&mi=0&af=R</link>
			</item>
	<item>
		<title>Locating and Characterizing the Stationary Points of the Extended Rosenbrock Function</title>
		<description>Evolutionary Computation, Volume 17, Issue 3, Page 437-453, Fall 2009. 
		
	

Related posts:Estimating the Ratios of the Stationary Distribution Values for Markov Chains Modeling Evolutionary AlgorithmsAutomated Design of Image Operators that Detect Interest PointsA Preference-Based Evolutionary Algorithm for Multi-Objective Optimization </description>
		<link>http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.437?ai=t9&mi=0&af=R</link>
			</item>
	<item>
		<title>Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy</title>
		<description>Evolutionary Computation, Volume 17, Issue 3, Page 275-306, Fall 2009. 
		
	

Related posts:A Preference-Based Evolutionary Algorithm for Multi-Objective OptimizationEstimating the Ratios of the Stationary Distribution Values for Markov Chains Modeling Evolutionary AlgorithmsLocating and Characterizing the Stationary Points of the Extended Rosenbrock Function </description>
		<link>http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.275?ai=t9&mi=0&af=R</link>
			</item>
	<item>
		<title>Localization for Solving Noisy Multi-Objective Optimization Problems</title>
		<description>Evolutionary Computation, Volume 17, Issue 3, Page 379-409, Fall 2009. 
		
	

Related posts:A Preference-Based Evolutionary Algorithm for Multi-Objective OptimizationOn the Use of Problem-Specific Candidate Generators for the Hybrid Optimization of Multi-Objective Production Engineering ProblemsMulti-Objective Optimization with Controlled Model Assisted Evolution Strategies </description>
		<link>http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.3.379?ai=t9&mi=0&af=R</link>
			</item>
	<item>
		<title>Adaptive ε-Ranking on many-objective problems</title>
		<description>Abstract&#160;&#160;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 ...</description>
		<link>http://www.springerlink.com/content/xl84818652846w7p/</link>
			</item>
	<item>
		<title>Special issue on simulated evolution and learning</title>
		<description>Special issue on simulated evolution and learning
	Content Type Journal ArticleCategory EditorialDOI 10.1007/s12065-009-0033-0Authors
		Michael Kirley, The University of Melbourne Department of Computer Science and Software Engineering Melbourne AustraliaMengjie Zhang, Victoria University School of Engineering and Computer Science Wellington New ZealandXiaodong Li, RMIT University School of Computer Science and Information Technology Melbourne Australia
	

	
		Journal ...</description>
		<link>http://www.springerlink.com/content/8745t8420276p147/</link>
			</item>
	<item>
		<title>Tesi/Stage Disponibili su Sviluppo di Videogiochi</title>
		<description>Sono disponibili tre tesi/stage in collaborazione con Milestone (www.milestone.it) nell&#8217;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 ...</description>
		<link>http://www.pierlucalanzi.net/?p=419</link>
			</item>
	<item>
		<title>On the Use of Problem-Specific Candidate Generators for the Hybrid Optimization of Multi-Objective Production Engineering Problems</title>
		<description>Evolutionary Computation, Volume 0, Issue 0, Page 1-18, Early Access. 
		
	

Related posts:Hybrid Evolutionary Optimization of Two-Stage Stochastic Integer Programming Problems: An Empirical InvestigationMulti-Objective Optimization with Controlled Model Assisted Evolution StrategiesLocalization for Solving Noisy Multi-Objective Optimization Problems </description>
		<link>http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17405?ai=t9&mi=0&af=R</link>
			</item>
	<item>
		<title>Hybrid Evolutionary Optimization of Two-Stage Stochastic Integer Programming Problems: An Empirical Investigation</title>
		<description>Evolutionary Computation, Volume 0, Issue 0, Page 1-16, Early Access. 
		
	

Related posts:On the Use of Problem-Specific Candidate Generators for the Hybrid Optimization of Multi-Objective Production Engineering ProblemsMulti-Objective Optimization with Controlled Model Assisted Evolution StrategiesLocalization for Solving Noisy Multi-Objective Optimization Problems </description>
		<link>http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17404?ai=t9&mi=0&af=R</link>
			</item>
	<item>
		<title>Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms</title>
		<description>Evolutionary Computation, Volume 0, Issue 0, Page 1-17, Early Access. 
		
	

Related posts:Multi-Objective Optimization with Controlled Model Assisted Evolution StrategiesOn the Use of Problem-Specific Candidate Generators for the Hybrid Optimization of Multi-Objective Production Engineering ProblemsIncreasing the Production Accuracy of Profile Bending with Methods of Computational Intelligence </description>
		<link>http://www.mitpressjournals.org/doi/abs/10.1162/evco.2009.17.4.17403?ai=t9&mi=0&af=R</link>
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