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Genetic Programming [[electronic resource] ] : 17th European Conference, EuroGP 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers / / edited by Miguel Nicolau, Krzysztof Krawiec, Malcolm I. Heywood, Mauro Castelli, Pablo García-Sánchez, Juan J. Merelo, Victor Manuel Rivas Santos, Kevin Sim
Genetic Programming [[electronic resource] ] : 17th European Conference, EuroGP 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers / / edited by Miguel Nicolau, Krzysztof Krawiec, Malcolm I. Heywood, Mauro Castelli, Pablo García-Sánchez, Juan J. Merelo, Victor Manuel Rivas Santos, Kevin Sim
Edizione [1st ed. 2014.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Descrizione fisica 1 online resource (XII, 247 p. 78 illus.)
Disciplina 006.31
Collana Theoretical Computer Science and General Issues
Soggetto topico Algorithms
Computer science
Artificial intelligence
Application software
Theory of Computation
Artificial Intelligence
Computer and Information Systems Applications
ISBN 3-662-44303-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Table of Contents -- Oral Presentations -- Higher Order Functions for Kernel Regression -- 1 Introduction -- 2 Kernel Regression -- 3 Higher Order Functions -- 4 Method -- 4.1 Wrapper Approach to the Evolution of Distance Measures -- 4.2 Experiment Design -- 5 Results Analysis -- 6 Conclusion and Future Work -- References -- Flash: A GP-GPU Ensemble Learning System for Handling Large Datasets -- 1 Introduction -- 2 Related Work: Accelerating GP with GPUs -- 3 The Core GP Learner -- 3.1 Mean Squared Error and Pearson Correlation on GPUs -- 3.2 Individual Level Parallelism -- 4 Flash - The GP-GPU Ensemble Learning System -- 4.1 GP Instances -- 4.2 Generating a Fused Model -- 5 Experimental Setup -- 5.1 Million Song Dataset Year Prediction Challenge -- 5.2 Ensemble Configurations -- 6 Results -- 6.1 Prediction Error Analysis -- 6.2 Prediction Error vs. GP Instances -- 6.3 Runtime Analysis -- 7 Conclusions and Future Work -- References -- Learning Dynamical Systems Using Standard Symbolic Regression -- 1 Introduction -- 2 Background -- 2.1 Genetic Programming and Symbolic Regression -- 2.2 Differential Equations and First-Order Approximation -- 3 Proposed Approach -- 4 Case Study -- 5 Experimental Results -- 5.1 Noise-Free Data -- 5.2 Absolute Noise -- 5.3 Noise 5% -- 5.4 Noise 10% -- 6 Results Discussion -- 7 Conclusions and Future Works -- References -- Semantic Crossover Based on the Partial Derivative Error -- 1 Introduction -- 2 Semantic Crossover Based on Partial Derivative Error -- 2.1 Backpropagation -- 2.2 Selecting the Crossing Points -- 3 Results -- 4 Conclusions -- References -- A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems -- 1 Introduction -- 2 Related Work -- 3 Formulation of Multi-dimensional GP -- 4 Algorithm -- 5 Experimental Analysis -- 5.1 Data Sets.
5.2 Experiments with GP Classifiers -- 5.3 Comparison with Various Classifiers -- 6 Conclusions and Future Directions -- References -- Generalisation Enhancement via Input Space Transformation: A GP Approach -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 Trees Return Multiple Outputs -- 3.2 FitnessMeasure -- 4 Experiments and Analysis -- 4.1 Experimental Settings -- 4.2 Results -- 5 Conclusions -- References -- On Diversity, Teaming, and Hierarchical Policies: Observations from the Keepaway Soccer Task -- 1 Introduction -- 2 Related Work -- 3 Hierarchical Symbiotic Policy Search -- 3.1 Symbiont -- 3.2 Variation Operators -- 3.3 Selection Operator -- 3.4 Constructing Hierarchical Policies -- 3.5 Fitness and Diversity -- 4 Results -- 5 Conclusion -- References -- Genetically Improved CUDA C++ Software -- 1 Introduction -- 2 Source Code: StereoCamera -- 3 Example Stereo Pairs from Microsoft's I2I Database -- 4 Pre- and Post- Evolution Tuning and Post Evolution Minimisation of Code Changes -- 5 Alternative Implementations -- 5.1 Avoiding Reusing Threads: XHALO -- 5.2 Parallel of Discrepancy Offsets: DPER -- 6 Parameters Accessible to Evolution -- 6.1 Fixed Configuration Parameters -- 7 Evolvable Code -- 7.1 Initial Population -- 7.2 Weights -- 7.3 Mutation -- 7.4 Crossover -- 7.5 Fitness -- 7.6 Selection -- 8 Results -- 8.1 GP Better Than Random Search -- 9 Evolved Tesla K20c CUDA Code -- 10 Conclusions -- References -- Measuring Mutation Operators' Exploration-Exploitation Behaviour and Long-Term Biases -- 1 Introduction -- 1.1 Reader's Guide -- 2 Related Work -- 3 Statistics on Markov Chains -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Measuring Exploration-Exploitation Behaviour -- 4.3 Exploration-Exploitation Behaviour and Search Space Coverage -- 4.4 Exploration-Exploitation Behaviour and Performance.
4.5 Stationary Distributions -- 5 Conclusions -- 5.1 Limitations -- 5.2 Future Work -- References -- Exploring the Search Space of Hardware / Software Embedded Systems by Means of GP -- 1 Introduction -- 2 Previous Work -- 2.1 Hardware -- 2.2 Software -- 3 Proposed Extensions -- 3.1 Evolvable Hardware Topology Related Changes -- 3.2 Input Modules -- 3.3 Problem Encoding and Search Method -- 4 Experimental Results -- 4.1 Newton-Raphson Division -- 4.2 Finding the Maximum -- 4.3 Parity -- 5 Conclusions -- References -- Enhancing Branch-and-Bound Algorithms for Order Acceptance and Scheduling with Genetic Programming -- 1 Introduction -- 1.1 Goals -- 1.2 Organisation -- 2 Methodology -- 2.1 Branch and Bound Algorithm for OAS -- 3 Computational Results -- 3.1 Datasets -- 3.2 Results -- 4 Conclusions -- References -- Using Genetic Improvement and Code Transplants to Specialise a C++ Program to a Problem Class -- 1 Introduction -- 2 Genetic Improvement with Multi-donor Transplantation and Specialisation -- 3 Experimental Setup -- 4 Results -- 4.1 Transplanting from MiniSAT-best09 -- 4.2 Transplanting from MiniSAT-bestCIT -- 4.3 Transplanting from MiniSAT-best09 and MiniSAT-bestCIT -- 4.4 Combining Results -- 5 Summary of Related Work -- 6 Conclusions -- References -- ESAGP - A Semantic GP Framework Based on Alignment in the Error Space -- 1 Introduction -- 2 Alignment in the Error Space -- 3 One Step Error Space Alignment GP: ESAGP-1 -- 4 Two Steps Error Space Alignment GP: ESAGP-2 -- 5 Experimental Study -- 6 Conclusions and Future Work -- References -- Building a Stage 1 Computer Aided Detector for Breast Cancer Using Genetic Programming -- 1 Introduction -- 2 Mammography -- 2.1 Computer-Aided Detection of Mammographic Abnormalities -- 2.2 Feature Extraction -- 2.3 Related Work -- 3 Workflow -- 3.1 Separation -- 3.2 Suppression of the Background.
3.3 Segmentation -- 3.4 Textural Features -- 4 Experimental Setup -- 4.1 GP Setup -- 5 Results -- 6 Conclusions and Future Work -- References -- NEAT, There's No Bloat -- 1 Introduction -- 2 Bloat -- 2.1 Causes of Bloat and Bloat Control Methods -- 2.2 The Secret Behind Operator Equalization -- 3 NeuroEvolution of Augmenting Topologies -- 3.1 NEAT Features -- 3.2 NEAT, GP and Bloat -- 4 Experiments -- 4.1 Discussion -- 5 Concluding Remarks and Future Work -- References -- Posters -- The Best Things Don't Always Come in Small Packages: Constant Creation in Grammatical Evolution -- 1 Introduction -- 2 Background -- 3 Experiments -- 3.1 Problem Suite and Evolutionary Parameters -- 3.2 Results -- 3.3 Discussion -- 4 Conclusions -- References -- Asynchronous Evolution by Reference-Based Evaluation: Tertiary Parent Selection and Its Archive -- 1 Introduction -- 2 Tierra-Based Asynchronous Genetic Programming -- 2.1 Overview -- 2.2 Algorithm -- 3 Asynchronous Reference-Based Evaluation -- 3.1 Concept -- 3.2 Algorithm -- 4 Experiment -- 4.1 Settings -- 4.2 Results -- 5 Conclusion -- References -- Behavioral Search Drivers for Genetic Programing -- 1 Introduction -- 2 Background -- 3 Motivation -- 4 Behavioral Evaluation of Programs in GP -- 5 TheExperiment -- 6 Related Work -- 7 Conclusion -- References -- Cartesian Genetic Programming: Why No Bloat? -- 1 Introduction -- 2 Cartesian Genetic Programming -- 3 Bloat and CGP -- 3.1 Neutral Genetic Drift -- 3.2 Length Bias -- 4 Experiments -- 4.1 Regular CGP -- 4.2 No Neutral Genetic Drift -- 4.3 Recurrent CGP -- 4.4 Neutral Search -- 5 Results -- 5.1 Regular CGP -- 5.2 No Neutral Genetic Drift -- 5.3 Recurrent CGP -- 5.4 Neutral Search -- 6 Discussion -- 7 Conclusion -- References -- On Evolution of Multi-category Pattern Classifiers Suitable for Embedded Systems -- 1 Introduction.
2 Cartesian Genetic Programming -- 2.1 Representation -- 2.2 Search Algorithm -- 3 Evolutionary Design of Classifiers -- 4 Experimental Setup -- 5 Experimental Results -- 5.1 Evaluation of the Evolved Classifiers -- 6 Conclusion -- References -- Author Index.
Record Nr. UNISA-996202528503316
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Genetic Programming [[electronic resource] ] : 17th European Conference, EuroGP 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers / / edited by Miguel Nicolau, Krzysztof Krawiec, Malcolm I. Heywood, Mauro Castelli, Pablo García-Sánchez, Juan J. Merelo, Victor Manuel Rivas Santos, Kevin Sim
Genetic Programming [[electronic resource] ] : 17th European Conference, EuroGP 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers / / edited by Miguel Nicolau, Krzysztof Krawiec, Malcolm I. Heywood, Mauro Castelli, Pablo García-Sánchez, Juan J. Merelo, Victor Manuel Rivas Santos, Kevin Sim
Edizione [1st ed. 2014.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Descrizione fisica 1 online resource (XII, 247 p. 78 illus.)
Disciplina 006.31
Collana Theoretical Computer Science and General Issues
Soggetto topico Algorithms
Computer science
Artificial intelligence
Application software
Theory of Computation
Artificial Intelligence
Computer and Information Systems Applications
ISBN 3-662-44303-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Table of Contents -- Oral Presentations -- Higher Order Functions for Kernel Regression -- 1 Introduction -- 2 Kernel Regression -- 3 Higher Order Functions -- 4 Method -- 4.1 Wrapper Approach to the Evolution of Distance Measures -- 4.2 Experiment Design -- 5 Results Analysis -- 6 Conclusion and Future Work -- References -- Flash: A GP-GPU Ensemble Learning System for Handling Large Datasets -- 1 Introduction -- 2 Related Work: Accelerating GP with GPUs -- 3 The Core GP Learner -- 3.1 Mean Squared Error and Pearson Correlation on GPUs -- 3.2 Individual Level Parallelism -- 4 Flash - The GP-GPU Ensemble Learning System -- 4.1 GP Instances -- 4.2 Generating a Fused Model -- 5 Experimental Setup -- 5.1 Million Song Dataset Year Prediction Challenge -- 5.2 Ensemble Configurations -- 6 Results -- 6.1 Prediction Error Analysis -- 6.2 Prediction Error vs. GP Instances -- 6.3 Runtime Analysis -- 7 Conclusions and Future Work -- References -- Learning Dynamical Systems Using Standard Symbolic Regression -- 1 Introduction -- 2 Background -- 2.1 Genetic Programming and Symbolic Regression -- 2.2 Differential Equations and First-Order Approximation -- 3 Proposed Approach -- 4 Case Study -- 5 Experimental Results -- 5.1 Noise-Free Data -- 5.2 Absolute Noise -- 5.3 Noise 5% -- 5.4 Noise 10% -- 6 Results Discussion -- 7 Conclusions and Future Works -- References -- Semantic Crossover Based on the Partial Derivative Error -- 1 Introduction -- 2 Semantic Crossover Based on Partial Derivative Error -- 2.1 Backpropagation -- 2.2 Selecting the Crossing Points -- 3 Results -- 4 Conclusions -- References -- A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems -- 1 Introduction -- 2 Related Work -- 3 Formulation of Multi-dimensional GP -- 4 Algorithm -- 5 Experimental Analysis -- 5.1 Data Sets.
5.2 Experiments with GP Classifiers -- 5.3 Comparison with Various Classifiers -- 6 Conclusions and Future Directions -- References -- Generalisation Enhancement via Input Space Transformation: A GP Approach -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 Trees Return Multiple Outputs -- 3.2 FitnessMeasure -- 4 Experiments and Analysis -- 4.1 Experimental Settings -- 4.2 Results -- 5 Conclusions -- References -- On Diversity, Teaming, and Hierarchical Policies: Observations from the Keepaway Soccer Task -- 1 Introduction -- 2 Related Work -- 3 Hierarchical Symbiotic Policy Search -- 3.1 Symbiont -- 3.2 Variation Operators -- 3.3 Selection Operator -- 3.4 Constructing Hierarchical Policies -- 3.5 Fitness and Diversity -- 4 Results -- 5 Conclusion -- References -- Genetically Improved CUDA C++ Software -- 1 Introduction -- 2 Source Code: StereoCamera -- 3 Example Stereo Pairs from Microsoft's I2I Database -- 4 Pre- and Post- Evolution Tuning and Post Evolution Minimisation of Code Changes -- 5 Alternative Implementations -- 5.1 Avoiding Reusing Threads: XHALO -- 5.2 Parallel of Discrepancy Offsets: DPER -- 6 Parameters Accessible to Evolution -- 6.1 Fixed Configuration Parameters -- 7 Evolvable Code -- 7.1 Initial Population -- 7.2 Weights -- 7.3 Mutation -- 7.4 Crossover -- 7.5 Fitness -- 7.6 Selection -- 8 Results -- 8.1 GP Better Than Random Search -- 9 Evolved Tesla K20c CUDA Code -- 10 Conclusions -- References -- Measuring Mutation Operators' Exploration-Exploitation Behaviour and Long-Term Biases -- 1 Introduction -- 1.1 Reader's Guide -- 2 Related Work -- 3 Statistics on Markov Chains -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Measuring Exploration-Exploitation Behaviour -- 4.3 Exploration-Exploitation Behaviour and Search Space Coverage -- 4.4 Exploration-Exploitation Behaviour and Performance.
4.5 Stationary Distributions -- 5 Conclusions -- 5.1 Limitations -- 5.2 Future Work -- References -- Exploring the Search Space of Hardware / Software Embedded Systems by Means of GP -- 1 Introduction -- 2 Previous Work -- 2.1 Hardware -- 2.2 Software -- 3 Proposed Extensions -- 3.1 Evolvable Hardware Topology Related Changes -- 3.2 Input Modules -- 3.3 Problem Encoding and Search Method -- 4 Experimental Results -- 4.1 Newton-Raphson Division -- 4.2 Finding the Maximum -- 4.3 Parity -- 5 Conclusions -- References -- Enhancing Branch-and-Bound Algorithms for Order Acceptance and Scheduling with Genetic Programming -- 1 Introduction -- 1.1 Goals -- 1.2 Organisation -- 2 Methodology -- 2.1 Branch and Bound Algorithm for OAS -- 3 Computational Results -- 3.1 Datasets -- 3.2 Results -- 4 Conclusions -- References -- Using Genetic Improvement and Code Transplants to Specialise a C++ Program to a Problem Class -- 1 Introduction -- 2 Genetic Improvement with Multi-donor Transplantation and Specialisation -- 3 Experimental Setup -- 4 Results -- 4.1 Transplanting from MiniSAT-best09 -- 4.2 Transplanting from MiniSAT-bestCIT -- 4.3 Transplanting from MiniSAT-best09 and MiniSAT-bestCIT -- 4.4 Combining Results -- 5 Summary of Related Work -- 6 Conclusions -- References -- ESAGP - A Semantic GP Framework Based on Alignment in the Error Space -- 1 Introduction -- 2 Alignment in the Error Space -- 3 One Step Error Space Alignment GP: ESAGP-1 -- 4 Two Steps Error Space Alignment GP: ESAGP-2 -- 5 Experimental Study -- 6 Conclusions and Future Work -- References -- Building a Stage 1 Computer Aided Detector for Breast Cancer Using Genetic Programming -- 1 Introduction -- 2 Mammography -- 2.1 Computer-Aided Detection of Mammographic Abnormalities -- 2.2 Feature Extraction -- 2.3 Related Work -- 3 Workflow -- 3.1 Separation -- 3.2 Suppression of the Background.
3.3 Segmentation -- 3.4 Textural Features -- 4 Experimental Setup -- 4.1 GP Setup -- 5 Results -- 6 Conclusions and Future Work -- References -- NEAT, There's No Bloat -- 1 Introduction -- 2 Bloat -- 2.1 Causes of Bloat and Bloat Control Methods -- 2.2 The Secret Behind Operator Equalization -- 3 NeuroEvolution of Augmenting Topologies -- 3.1 NEAT Features -- 3.2 NEAT, GP and Bloat -- 4 Experiments -- 4.1 Discussion -- 5 Concluding Remarks and Future Work -- References -- Posters -- The Best Things Don't Always Come in Small Packages: Constant Creation in Grammatical Evolution -- 1 Introduction -- 2 Background -- 3 Experiments -- 3.1 Problem Suite and Evolutionary Parameters -- 3.2 Results -- 3.3 Discussion -- 4 Conclusions -- References -- Asynchronous Evolution by Reference-Based Evaluation: Tertiary Parent Selection and Its Archive -- 1 Introduction -- 2 Tierra-Based Asynchronous Genetic Programming -- 2.1 Overview -- 2.2 Algorithm -- 3 Asynchronous Reference-Based Evaluation -- 3.1 Concept -- 3.2 Algorithm -- 4 Experiment -- 4.1 Settings -- 4.2 Results -- 5 Conclusion -- References -- Behavioral Search Drivers for Genetic Programing -- 1 Introduction -- 2 Background -- 3 Motivation -- 4 Behavioral Evaluation of Programs in GP -- 5 TheExperiment -- 6 Related Work -- 7 Conclusion -- References -- Cartesian Genetic Programming: Why No Bloat? -- 1 Introduction -- 2 Cartesian Genetic Programming -- 3 Bloat and CGP -- 3.1 Neutral Genetic Drift -- 3.2 Length Bias -- 4 Experiments -- 4.1 Regular CGP -- 4.2 No Neutral Genetic Drift -- 4.3 Recurrent CGP -- 4.4 Neutral Search -- 5 Results -- 5.1 Regular CGP -- 5.2 No Neutral Genetic Drift -- 5.3 Recurrent CGP -- 5.4 Neutral Search -- 6 Discussion -- 7 Conclusion -- References -- On Evolution of Multi-category Pattern Classifiers Suitable for Embedded Systems -- 1 Introduction.
2 Cartesian Genetic Programming -- 2.1 Representation -- 2.2 Search Algorithm -- 3 Evolutionary Design of Classifiers -- 4 Experimental Setup -- 5 Experimental Results -- 5.1 Evaluation of the Evolved Classifiers -- 6 Conclusion -- References -- Author Index.
Record Nr. UNINA-9910484703203321
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Genetic Programming [[electronic resource] ] : 14th European Conference, EuroGP 2011, Torino, Italy, April 27-29, 2011, Proceedings / / edited by Sara Silva, James A. Foster, Miguel Nicolau, Penousal Machado, Mario Giacobini
Genetic Programming [[electronic resource] ] : 14th European Conference, EuroGP 2011, Torino, Italy, April 27-29, 2011, Proceedings / / edited by Sara Silva, James A. Foster, Miguel Nicolau, Penousal Machado, Mario Giacobini
Edizione [1st ed. 2011.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2011
Descrizione fisica 1 online resource (XIII, 348 p.)
Disciplina 005.11
Collana Theoretical Computer Science and General Issues
Soggetto topico Computer programming
Computer science
Algorithms
Pattern recognition systems
Artificial intelligence
Bioinformatics
Programming Techniques
Theory of Computation
Automated Pattern Recognition
Artificial Intelligence
Computational and Systems Biology
ISBN 3-642-20407-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996465554203316
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2011
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui