Genetic Programming Theory and Practice VIII (Genetic and Evolutionary Computation (8))

The contributions in this volume are written by the foremost international researchers and practitioners in the GP arena. They examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application. Topics include: FINCH: A System for Evolving Java, Practical Autoconstructive Evolution, The Rubik Cube and GP Temporal Sequence Learning, Ensemble classifiers: AdaBoost and Orthogonal Evolution of Teams, Self-modifying Cartesian GP, Abstract Expression Grammar Symbolic Regression, Age-Fitness Pareto Optimization, Scalable Symbolic Regression by Continuous Evolution, Symbolic Density Models, GP Transforms in Linear Regression Situations, Protein Interactions in a Computational Evolution System, Composition of Music and Financial Strategies via GP, and Evolutionary Art Using Summed Multi-Objective Ranks. 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 in GP . Front Matter....Pages 1-1 Finch: A System for Evolving Java (Bytecode)....Pages 1-16 Towards Practical Autoconstructive Evolution: Self-Evolution of Problem-Solving Genetic Programming Systems....Pages 17-33 The Rubik Cube and GP Temporal Sequence Learning: An Initial Study....Pages 35-54 Ensemble Classifiers: AdaBoost and Orthogonal Evolution of Teams....Pages 55-69 Covariant Tarpeian Method for Bloat Control in Genetic Programming....Pages 71-89 A Survey of Self Modifying Cartesian Genetic Programming....Pages 91-107 Abstract Expression Grammar Symbolic Regression....Pages 109-128 Age-Fitness Pareto Optimization....Pages 129-146 Scalable Symbolic Regression by Continuous Evolution with Very Small Populations....Pages 147-160 Symbolic Density Models of One-in-a-Billion Statistical Tails via Importance Sampling and Genetic Programming....Pages 161-173 Genetic Programming Transforms in Linear Regression Situations....Pages 175-194 Exploiting Expert Knowledge of Protein-Protein Interactions in a Computational Evolution System for Detecting Epistasis....Pages 195-210 Composition of Music and Financial Strategies via Genetic Programming....Pages 211-226 Evolutionary Art Using Summed Multi-Objective Ranks....Pages 227-244 Back Matter....Pages 16-16 The contributions in this volume are written by the foremost international researchers and practitioners in the GP arena. They examine the similarities and differences between theoretical and empirical results on real-world problems. This text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application. It includes topics such as: Finch: A System for Evolving Java, Practical Autoconstructive Evolution, The Rubik Cube and GP Temporal Sequence Learning, Ensemble classifiers: AdaBoost and Orthogonal Evolution of Teams, Self-modifying In this book, international experts examine similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, offering a comprehensive view of the state of GP application.