His primary scientific interests are in Artificial intelligence, Machine learning, Classifier, Learning classifier system and Evolutionary algorithm. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Fitness function and Pattern recognition. His work in Machine learning tackles topics such as Fuzzy logic which are related to areas like Data analysis and Cluster analysis.
His Classifier study combines topics from a wide range of disciplines, such as Internal memory and Knowledge extraction. The study incorporates disciplines such as Semi-supervised learning, Genetic algorithm, Function approximation and Algorithm in addition to Learning classifier system. His biological study spans a wide range of topics, including Theoretical computer science, Set, Java, Multi-objective optimization and Code coverage.
Pier Luca Lanzi mostly deals with Artificial intelligence, Machine learning, Classifier, Learning classifier system and Evolutionary computation. His Artificial intelligence study combines topics in areas such as Genetic algorithm and Pattern recognition. Pier Luca Lanzi regularly ties together related areas like Process in his Machine learning studies.
His research in Classifier intersects with topics in Algorithm and Classifier. The concepts of his Learning classifier system study are interwoven with issues in Perceptron, Hyper-heuristic, Generalization error and Spiking neural network. His Evolutionary computation research incorporates elements of Theoretical computer science and Computational intelligence.
His primary areas of study are Artificial intelligence, Rehabilitation, Machine learning, Set and Multimedia. His study in the field of Learning classifier system, Reinforcement learning and Classifier also crosses realms of Structure. His biological study deals with issues like Spiking neural network, which deal with fields such as Temporal logic, Genetic algorithm and Chaining.
His studies in Rehabilitation integrate themes in fields like Phase, Computational intelligence and Adaptation. His work in the fields of Machine learning, such as Active learning, overlaps with other areas such as Multiplexer. His Multimedia research incorporates themes from Evolutionary computation, Evolutionary algorithm and World Wide Web.
His main research concerns Multimedia, Artificial intelligence, Game design, Rehabilitation and Rhythm. Pier Luca Lanzi combines subjects such as Evolutionary algorithm, Evolutionary computation, World Wide Web and Adaptation with his study of Multimedia. His Artificial intelligence research includes elements of Monte Carlo tree search and State.
His work on Game Developer, Video game development, Game design document and Video game design as part of general Game design research is often related to Screening game, thus linking different fields of science. His research in Video game development intersects with topics in Level design and Video game. The study incorporates disciplines such as Information and Communications Technology, Phase, Virtual machine and Identification in addition to Rehabilitation.
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Mining interesting knowledge from weblogs: a survey
Federico Michele Facca;Pier Luca Lanzi.
data and knowledge engineering (2005)
An analysis of generalization in the xcs classifier system
Pier Luca Lanzi.
Evolutionary Computation (1999)
Architectures for an Event Notification Service Scalable to Wide-area Networks
Antonio Carzaniga;Luciano Baresi;Gino Biondini;Fabiano Cattaneo.
Toward a theory of generalization and learning in XCS
M.V. Butz;T. Kovacs;P.L. Lanzi;S.W. Wilson.
IEEE Transactions on Evolutionary Computation (2004)
Learning Classifier Systems, From Foundations to Applications
Pier Luca Lanzi;Wolfgang Stolzmann;Stewart W. Wilson.
Springer US (2000)
Learning Classifier Systems
Larry Bull;Pier Luca Lanzi;Wolfgang Stolzmann.
soft computing (2002)
Ant Colony Heuristic for Mapping and Scheduling Tasks and Communications on Heterogeneous Embedded Systems
Fabrizio Ferrandi;Pier Luca Lanzi;Christian Pilato;Donatella Sciuto.
networks on chips (2010)
Extending the representation of classifier conditions part I: from binary to messy coding
Pier Luca Lanzi.
genetic and evolutionary computation conference (1999)
What Is a Learning Classifier System
John H. Holland;Lashon B. Booker;Marco Colombetti;Marco Dorigo.
Lecture Notes in Computer Science (2000)
Fast feature selection with genetic algorithms: a filter approach
ieee international conference on evolutionary computation (1997)
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