Screen LibraryGenetic programming theory and practice XIV /
These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Chapters in this volume include: S...
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| Corporate Authors: | |
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| Group Author: | ; ; ; |
| Published: |
Springer,
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| Publisher Address: | Cham, Switzerland : |
| Publication Dates: | [2018] |
| Literature type: | Book |
| Language: | English |
| Series: |
Genetic and evolutionary computation,
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| Subjects: | |
| Summary: |
These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Chapters in this volume include: Similarity-based Analysis of Population Dynamics in GP Performing Symbolic Regression Hybrid Structural and Behavioral Diversity Methods in GP Multi-Population Competitive Coevolution for Anticipation of Tax Evasion Evolving Artificial General Intelligence for Video Game Controllers A Detailed Analysis of a PushGP Run Linear Genomes for Structured Programs Neutrality, Robustness, and Evolvability in GP Local Search in GP PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification Relational Structure in Program Synthesis Problems with Analogical Reasoning An Evolutionary Algorithm for Big Data Multi-Class Classification Problems A Generic Framework for Building Dispersion Operators in the Semantic Space Assisting Asset Model Development with Evolutionary Augmentation Building Blocks of Machine Learning Pipelines for Initialization of a Data Science Automation Tool Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results. |
| Carrier Form: | xv, 227 pages : illustrations ; 25 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9783319970875 3319970879 9783319970899 3319970895 |
| Index Number: | QA76 |
| CLC: |
TP311.11-532 TP301.6-532 |
| Call Number: | TP301.6-532/W926/2016 |
| Contents: |
Intro; Preface; Acknowledgments; Contents; Contributors; 1 Similarity-Based Analysis of Population Dynamics in Genetic Programming Performing Symbolic Regression; 1.1 Introduction: Genetic Programming, Population Diversity, and Population Dynamics; 1.2 Similarity Measures; 1.2.1 Genotypic Similarity; 1.2.2 Phenotypic Similarity; 1.3 Test Setup; 1.3.1 Algorithms; 1.3.1.1 Standard Genetic Programming (SGP); 1.3.1.2 Genetic Programming with Offspring Selection (OSGP); 1.3.1.3 ALPS GP; 1.3.2 Problem Instances; 1.4 Test Results; 1.5 Conclusion; References 2 An Investigation of Hybrid Structural and Behavioral Diversity Methods in Genetic Programming2.1 Introduction; 2.2 Related Work; 2.2.1 Genetic Diversity Techniques; 2.2.2 Semantic Methods in GP; 2.3 Fitness Case Bias in Lexicase Selection; 2.4 Hybrid Structural and Behavioral Diversity Methods; 2.5 Experimental Setup; 2.5.1 Problems; 2.6 Results; 2.7 Conclusions and Future Work; References; 3 Investigating Multi-Population Competitive Coevolution for Anticipation of Tax Evasion; 3.1 Introduction; 3.2 Related Work; 3.2.1 Grammatical Evolution; 3.2.2 Coevolution 3.2.3 Coevolution and Grammatical Evolution3.3 Method; 3.3.1 Tax Regulatory Module; 3.3.1.1 Tax Network and Transactions; 3.3.1.2 Audit Score Sheets; 3.3.2 Coevolutionary Module; 3.3.2.1 Adversarial Population Representation; 3.3.2.2 Coevolutionary Tests: Objective Functions; 3.3.2.3 Adaptation-Coevolutionary Genetic Algorithm; 3.4 Experiments; 3.4.1 iBOB Description; 3.4.2 Setup; 3.4.3 Coevolution of Auditors & Evaders in iBOB; 3.5 Conclusions and Future Work; References; 4 Evolving Artificial General Intelligence for VideoGame Controllers; 4.1 Introduction; 4.2 Previous Work 4.2.1 Automated Planning and MDP4.2.2 Heuristic Search; 4.2.3 Hyper-Heuristics; 4.2.4 Real-Time Learning of Hyper-Heuristics; 4.2.5 Solvers from GVGAI (Monte Carlo); 4.3 Method; 4.3.1 Heuristic Templates; 4.3.2 Hyper-Heuristics; 4.3.3 Learning Hyper-Heuristics Through Evolution; 4.3.3.1 Individuals; 4.3.3.2 Fitness Function; 4.3.4 GVGAI; 4.3.5 Game Controller; 4.4 And the Winner is ... ; References; 5 A Detailed Analysis of a PushGP Run; 5.1 Introduction; 5.2 Languages, Configuration, Tools and Setup; 5.3 Ancestry Graphs; 5.3.1 Full Ancestry Graph; 5.3.2 Genetic Ancestry Graph 5.4 The (Successful) End and How We Got There5.4.1 Printing: The First Five Instructions; 5.4.2 Returning: The Last Four Instructions; 5.4.2.1 Branch 4:772 and the Carriers of in1; 5.4.2.2 Branches 4:425, 4:107, and Multiple Blocks; 5.4.2.3 Branch 4:897 and the Carriers of string_length; 5.4.3 From 19:554 to the End, and the Final Adjustments; 5.5 Discussion; 5.6 Conclusions and Future Work; References; 6 Linear Genomes for Structured Programs; 6.1 Introduction; 6.2 Push and PushGP; 6.3 Plush; 6.3.1 Structure; 6.3.2 Translation; 6.3.3 Special Genes; 6.3.4 Example Translation |