Parallel Problem Solving from Nature -- PPSN XVI : 16th International Conference, PPSN 2020, Leiden, the Netherlands, September 5-9, 2020, Proceedings.: 16th International Conference, PPSN 2020, Leiden, the Netherlands, September 5-9, 2020, Proceedings. Part II (2020)

• Record Type: Book
• Other Titles: PPSN 2020
• Further Information: Note: Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, Heike Trautmann (eds.).
• Other Names: Bäck, Thomas, 1963-; Preuss, Mike; Deutz, André; Wang, Hao; Doerr, Carola; (Associate professor), Emmerich, Michael; Trautmann, Heike; International Conference on Parallel Problem Solving from Nature, 16th, author
• Contents: Intro -- Preface -- Organization -- Contents -- Part II -- Contents -- Part I -- Genetic Programming -- Generation of New Scalarizing Functions Using Genetic Programming -- 1 Introduction -- 2 Our Proposed Approach -- 3 Experimental Results -- 4 Conclusions and Future Work -- References -- The Usability Argument for Refinement Typed Genetic Programming -- 1 Introduction -- 2 The Æon Programming Language -- 3 Refinements in GP -- 3.1 Liquid Refinements for Constraining the Search Space -- 3.2 Non-liquid Refinements to Express Fitness Functions -- 4 The RTGP Algorithm -- 4.1 Representation 4.2 Initialization Procedure -- 4.3 Evaluation -- 4.4 Selection and Genetic Operators -- 4.5 Stopping Criteria -- 5 Examples of RTGP -- 5.1 Santa Fe Ant Trail -- 5.2 Super Mario Bros Level Design -- 5.3 Logical Gates -- 6 Discussion -- 6.1 A Direct Comparison with GGGP -- 6.2 Usability -- 7 Conclusions and Future Work -- References -- Program Synthesis in a Continuous Space Using Grammars and Variational Autoencoders -- 1 Introduction -- 2 Methods -- 2.1 Grammar Design Pattern -- 2.2 Variational Autoencoder -- 2.3 Evolutionary Algorithms -- 3 Experimental Setup -- 4 Results and Discussion 4.1 Success Rates -- 4.2 Landscape Analysis -- 5 Conclusions and Future Work -- References -- Cooperative Co-Evolutionary Genetic Programming for High Dimensional Problems -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 Cooperative Co-Evolutionary GP -- 4.1 Genotype Level -- 4.2 Feature Level -- 4.3 Ensemble Level -- 4.4 Fitness Assignment -- 5 Experimental Results -- 5.1 Datasets -- 5.2 Parameters Settings -- 5.3 Analysis of Results -- 5.4 Other GP Approaches Comparison -- 6 Conclusions -- References -- Image Feature Learning with Genetic Programming -- 1 Introduction 2 Background and Related Work -- 3 Genetic Programming Feature Learning (GPFL) -- 4 Experiments -- 5 Conclusion -- References -- Learning a Formula of Interpretability to Learn Interpretable Formulas -- 1 Introduction -- 2 Related Work -- 3 The Survey -- 3.1 Simulatability and Decomposability -- 3.2 Overview on the Survey and Results -- 4 Learning a Formula of Interpretability -- 4.1 Learning the Model -- 5 Exploiting the Model of Interpretability in MOGP -- 6 Results -- 7 Discussion -- 8 Conclusion -- References -- Landscape Analysis On Stochastic Fitness Landscapes: Local Optimality and Fitness Landscape Analysis for Stochastic Search Operators -- 1 Introduction -- 2 Preliminaries -- 3 Stochastic Fitness Landscapes and Local Optimality -- 4 Experimental Analysis -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 4.3 Stochastic Fitness Landscape Analysis -- 5 Discussion and Further Considerations -- References -- Fitness Landscape Analysis of Dimensionally-Aware Genetic Programming Featuring Feynman Equations -- 1 Introduction -- 2 Background -- 2.1 Feynman's Equations -- 2.2 Fitness Landscape Analysis
• Publisher Details: Cham : Springer
• Publication Date: 2020
• Extent: 1 online resource
• Subjects: 004.0151; Parallel processing (Electronic computers) -- Congresses; Evolutionary computation -- Congresses; Artificial intelligence; Computer science -- Mathematics; Software engineering; Mathematical statistics; Electronic books
• Languages: English
• ISBNs: 9783030581152; 3030581152
• Related ISBNs: 9783030581145
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