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001444514 001__ 1444514
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001444514 020__ $$a9789811681134$$q(electronic bk.)
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001444514 020__ $$z9811681120
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001444514 0247_ $$a10.1007/978-981-16-8113-4$$2doi
001444514 035__ $$aSP(OCoLC)1298387863
001444514 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dYDX$$dGW5XE$$dOCLCO$$dOCLCF$$dOCLCQ$$dUKAHL$$dOCLCQ
001444514 049__ $$aISEA
001444514 050_4 $$aQA76.623
001444514 08204 $$a006.3/823$$223
001444514 24500 $$aGenetic programming theory and practice XVIII /$$cWolfgang Banzhaf, Leonardo Trujillo, Stephan Winkler, Bill Worzel, editors.
001444514 2463_ $$aGenetic programming theory and practice 18
001444514 260__ $$aSingapore :$$bSpringer,$$c2022.
001444514 300__ $$a1 online resource (220 pages)
001444514 336__ $$atext$$btxt$$2rdacontent
001444514 337__ $$acomputer$$bc$$2rdamedia
001444514 338__ $$aonline resource$$bcr$$2rdacarrier
001444514 4901_ $$aGenetic and evolutionary computation
001444514 500__ $$a5.4.1 Lexicase Selection Out-Explores Tournament Selection.
001444514 500__ $$aIncludes index.
001444514 5050_ $$aIntro -- Foreword -- Preface -- Contents -- Contributors -- 1 Finding Simple Solutions to Multi-Task Visual Reinforcement Learning Problems with Tangled Program Graphs -- 1.1 Introduction -- 1.2 Tangled Program Graphs -- 1.2.1 Learners -- 1.2.2 Teams -- 1.2.3 Graphs -- 1.2.4 Memory -- 1.3 Mechanisms for Accelerating TPG Evolution -- 1.3.1 Rampant Mutation -- 1.3.2 Multi-actions -- 1.4 ViZDoom Subtask Selection and Performance Evaluation -- 1.5 Empirical Methodology -- 1.5.1 Task Domains -- 1.5.2 Parameters -- 1.6 Results -- 1.6.1 Fitness -- 1.6.2 Generalization -- 1.6.3 Complexity
001444514 5058_ $$a1.6.4 Details of a RAPS Solution -- 1.7 Conclusions -- References -- 2 Grammar-Based Vectorial Genetic Programming for Symbolic Regression -- 2.1 Introduction -- 2.2 State of the Art -- 2.2.1 Vectorial Genetic Programming -- 2.2.2 Grammar-Based Genetic Programming -- 2.2.3 Feature Engineering and Feature Extraction -- 2.2.4 Deep Learning -- 2.3 Grammar-Based Vectorial Genetic Programming -- 2.3.1 Vectorial Tree Interpretation -- 2.3.2 Vectorial Symbolic Regression Grammar -- 2.4 Experiment Setup -- 2.5 Results -- 2.5.1 Analysis Benchmarks Group A -- 2.5.2 Analysis Benchmarks Group B
001444514 5058_ $$a2.6 Discussion and Next Steps -- References -- 3 Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming -- 3.1 Introduction -- 3.2 Software Engineering Applications of Semantically-Constrained GP -- 3.2.1 Automated Program Repair -- 3.2.2 Automated Test Generation -- 3.2.3 Program Synthesis -- 3.3 Semantic Constraints in GP -- 3.3.1 Strongly-Typed GP (STGP) -- 3.3.2 Grammar-Guided GP (GGGP) -- 3.3.3 Refined-Typed GP (RTGP) -- 3.4 Correct-by-Construction Versus Generate-and-Validate -- 3.5 Direct Versus Indirect Representations -- 3.6 A Dynamic Grammar-Guided Mapping
001444514 5058_ $$a3.6.1 GE Mapping -- 3.6.2 Semantic Filter of Valid Productions -- 3.6.3 Dynamic and Depth-Aware Dynamic Approaches -- 3.7 Evaluation -- 3.8 Conclusions -- References -- 4 What Can Phylogenetic Metrics Tell us About Useful Diversity in Evolutionary Algorithms? -- 4.1 Introduction -- 4.2 Methods -- 4.2.1 Selection Methods -- 4.2.2 Problems -- 4.2.3 Computational Substrates -- 4.2.4 Other Parameters -- 4.2.5 Phylogenetic Diversity Metrics -- 4.2.6 Analysis Techniques -- 4.2.7 Code Availability -- 4.3 Results and Discussion -- 4.3.1 Do Phylogenetic Metrics Provide Novel Information?
001444514 5058_ $$a4.3.2 Do Phylogenetic Metrics Predict Problem-Solving Success? -- 4.4 Conclusion -- 4.5 Author Contributions -- References -- 5 An Exploration of Exploration: Measuring the Ability of Lexicase Selection to Find Obscure Pathways to Optimality -- 5.1 Introduction -- 5.2 Exploration Diagnostic -- 5.3 Lexicase Selection -- 5.3.1 Epsilon Lexicase Selection -- 5.3.2 Down-Sampled Lexicase Selection -- 5.3.3 Cohort Lexicase Selection -- 5.3.4 Novelty-Lexicase Selection -- 5.4 Diagnosing the Exploratory Capacity of Lexicase Selection and Its Variants
001444514 506__ $$aAccess limited to authorized users.
001444514 520__ $$aThis book, written by the foremost international researchers and practitioners of genetic programming (GP), explores the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. In this years edition, the topics covered include many of the most important issues and research questions in the eld, such as opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efcient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms. The book includes several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.
001444514 588__ $$aDescription based on print version record.
001444514 650_0 $$aGenetic programming (Computer science)
001444514 650_6 $$aProgrammation génétique (Informatique)
001444514 655_0 $$aElectronic books.
001444514 7001_ $$aBanzhaf, Wolfgang,$$d1955-
001444514 7001_ $$aTrujillo, Leonardo.
001444514 7001_ $$aWinkler, Stephan.
001444514 7001_ $$aWorzel, Bill.
001444514 77608 $$iPrint version:$$aBanzhaf, Wolfgang.$$tGenetic Programming Theory and Practice XVIII.$$dSingapore : Springer Singapore Pte. Limited, ©2022$$z9789811681127
001444514 77608 $$iPrint version:$$tGENETIC PROGRAMMING THEORY AND PRACTICE XVIII.$$d[S.l.] : SPRINGER VERLAG, SINGAPOR, 2022$$z9811681120$$w(OCoLC)1280602133
001444514 830_0 $$aGenetic and evolutionary computation series.
001444514 852__ $$bebk
001444514 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-8113-4$$zOnline Access$$91397441.1
001444514 909CO $$ooai:library.usi.edu:1444514$$pGLOBAL_SET
001444514 980__ $$aBIB
001444514 980__ $$aEBOOK
001444514 982__ $$aEbook
001444514 983__ $$aOnline
001444514 994__ $$a92$$bISE