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• Contributing Authors.- Preface.- Foreword.- Genetic Programming: Theory and Practice.- A Population Based Study of Evolutionary Dynamics in Genetic Programming.- An Application of Information Theoretic Selection to Evolution of Models with Continuous-valued Inputs.- Pareto Cooperative-Competitive Genetic Programming: A Classification Benchmarking Study.- Genetic Programming with Historically Assessed Hardness.- Crossover and Sampling Biases on Nearly Uniform Landscapes.- Analysis of the Effects of Elitism on Bloat in Linear and Tree-based Genetic Programming.- Automated Extraction of Expert Domain Knowledge from Genetic Programming Synthesis Results.- Does Complexity Matter? Artificial Evolution, Computational Evolution and the Genetic Analysis of Epistasis in Common Human Diseases.- Exploiting Trustable Models via Pareto GP for Targeted Data Collection.- Evolving Effective Incremental SAT Solvers with GP.- Constrained Genetic Programming To Minimize Overfitting in Stock Selection.- Co-Evolving Trading Strategies to Analyze Bounded Rationality.- Profiling Symbolic Regression-Classification.- Accelerating Genetic Programming through Graphics Processing Units.- Genetic Programming for Incentive-Based Design within a Cultural Algorithms Framework.- Index.
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Genetic Programming Theory and Practice VI was developed from the sixth workshop at the University of Michigan's Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena 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. These contributions address several significant interdependent themes which emerged from this year's workshop, including: (1) Making efficient and effective use of test data. (2) Sustaining the long-term evolvability of our GP systems. (3) Exploiting discovered subsolutions for reuse. (4) Increasing the role of a Domain Expert.
(source: Nielsen Book Data)

• Contributing Authors.- Preface.- Foreword.- Genetic Programming: Theory and Practice.- Genome-Wide Genetic Analysis Using Genetic Programming: The Critical Need for Expert Knowledge.- Lifting the Curse of Dimensionality.- Genetic Programming for Classifying Cancer Data and Controlling Humanoid Robots.- Boosting Improves Stability and Accuracy of Genetic Programming in Biological Sequence Classification.- Othogonal Evoluton of Teams: A Class of Algorithms for Evolving Teams with Inversely Correlated Errors.- Multidimensional Tags, Cooperative Populations, and Genetic Programming.- Coevolving Fitness Models for Accelerating Evolution and Reducing Evaluations.- Multi-Domain Observations Concerning the Use of Genetic Programming to Automatically Synthesize Human-Competitive Designs for Analog Circuits, Optical Lens Systems, Controllers, Antennas, Mechanical Systems, and Quantum Computing Circuits.- Robust Pareto Front Genetic Programming Parameter Selection Based on Design of Experiments and Industrial Data.- Pursuing the Pareto Paradigm: Tournaments, Algorithm Variations and Ordinal Optimization.- Applying Genetic Programming to Reservoir History Matching Problem.- Comparison of Robustness of Three Filter Design Strategies Using Genetic Programming and Bond Graphs.- Design of Posynomial Models for Mosfets: Symbolic Regression Using Genetic Algorithms.- Phase Transitions in Genetic Programming Search.- Efficient Markov Chain Model of Machine Code Program Execution and Halting.- A Re-examination of a Real World Blood Flow Modeling Problem Using Context-aware Crossover.- Large-Scale, Time-Constrained Symbolic Regression.- Stock Selection: An Innovative Application of Genetic Programming Methodology.- Index.
• (source: Nielsen Book Data)

"Genetic Programming Theory and Practice IV" was developed from the fourth workshop at the University of Michigan's Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena 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. This volume represents a watershed moment in the GP field in that GP has begun to move from hand-crafted software used primarily in academic research, to an engineering methodology applied to commercial applications. It is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.
(source: Nielsen Book Data)

• Background on Genetic Programming
• Automatic Synthesis of Controllers
• Automatic Synthesis of Circuits
• Automatic Synthesis of Circuit Topology, Sizing, Placement, and Routing
• Automatic Synthesis of Antennas
• Automatic Synthesis of Genetic Networks
• Automatic Synthesis of Metabolic Pathways
• Automatic Synthesis of Parameterized Topologies for Controllers
• Automatic Synthesis of Parameterized Topologies for Circuits
• Automatic Synthesis of Parameterized Topologies with Conditional Developmental Operators for Circuits
• Automatic Synthesis of Improved Tuning Rules for PID Controllers
• Automatic Synthesis of Parameterized Topologies for Improved Controllers
• Reinvention of Negative Feedback
• Automated Re-Invention of Six Post-2000 Patented Circuits
• Problems for Which Genetic Programming May Be Well Suited
• Parallel Implementation and Computer Time
• Historical Perspective on Moore's Law and the Progression of Qualitatively More Substantial Results Produced by Genetic Programming
• Conclusion. Appendices: Functions and Terminals
• Control Parameters
• Patented or Patentable Inventions Generated by Genetic Programming.
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Genetic programming (GP) is method for automatically creating computer programs. It starts from a high-level statement of what needs to be done and uses the Darwinian principle of natural selection to breed a population of improving programs over many generations. This text presents the application of GP to a wide variety of problems involving automated synthesis of controllers, circuits, antennas, genetic networks and metabolic pathways. The book describes 15 instances where GP has created an entity that either infringes or duplicates the functionality of a previously patented 20th-century invention, six instances where it has done the same with respect to post-2000 patented inventions, two instances where GP has created a patentable new invention, and 13 other human-competitive results.
(source: Nielsen Book Data)

Genetic Programming Theory and Practice explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The material contained in this contributed volume was developed from a workshop at the University of Michigan's Center for the Study of Complex Systems where an international group of genetic programming theorists and practitioners met to examine how GP theory informs practice and how GP practice impacts GP theory. The contributions cover the full spectrum of this relationship and are written by leading GP theorists from major universities, as well as active practitioners from leading industries and businesses. Chapters include such topics as John Koza's development of human-competitive electronic circuit designs; David Goldberg's application of "competent GA" methodology to GP; Jason Daida's discovery of a new set of factors underlying the dynamics of GP starting from applied research; and Stephen Freeland's essay on the lessons of biology for GP and the potential impact of GP on evolutionary theory. The book also includes chapters on the dynamics of GP, the selection of operators and population sizing, specific applications such as stock selection in emerging markets, predicting oil field production, modeling chemical production processes, and developing new diagnostics from genomic data. Genetic Programming Theory and Practice is an excellent reference for researchers working in evolutionary algorithms and for practitioners seeking innovative methods to solve difficult computing problems.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Fitness Landscapes
• 3. Program Component Schema Theories
• 4. Pessimistic GP Schema Theories
• 5. Exact GP Schema Theorems
• 6. Lessons from the GP Schema Theory
• 7. The Genetic Programming Search Space
• 8. The GP Search Space: Theoretical Analysis
• 9. Example I: The Artificial Ant
• 10. Exemple II: The Max Problem
• 11. Genetic Programming Convergence and Bloat
• 12. Conclusions.
• (source: Nielsen Book Data)

Genetic programming (GP), one of the most advanced forms of evolutionary computation, has been highly successful as a technique for getting computers to automatically solve problems without having to tell them explicitly how. Since its inceptions more than ten years ago, GP has been used to solve practical problems in a variety of application fields. Along with this ad-hoc engineering approaches interest increased in how and why GP works. This book provides a coherent consolidation of recent work on the theoretical foundations of GP. A concise introduction to GP and genetic algorithms (GA) is followed by a discussion of fitness landscapes and other theoretical approaches to natural and artificial evolution. Having surveyed early approaches to GP theory it presents new exact schema analysis, showing that it applies to GP as well as to the simpler GAs. New results on the potentially infinite number of possible programs are followed by two chapters applying these new techniques.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 5th European Conference on Genetic Programming, EuroGP 2002, held in Kinsale, Ireland, in April 2002. The 18 revised full papers and 14 posters presented were carefully reviewed and selected from 42 submissions. All current aspects of genetic programming and genetic algorithms are addressed, ranging from theoretical and foundational issues to applications in a variety of fields.
(source: Nielsen Book Data)

• I. Introduction II. Background III. Architecture-Altering Operations IV. Genetic Programming Problem Solver (GPPS) V. Automated Synthesis of Analog Electrical Circuits VI. Evolvable Hardware VII. Discovery of Cellular Automata Rules VIII. Discovery of Motifs and Programmatic Motifs for Molecular Biology IX. Parallelization and Implementation Issues X. Conclusion.
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Genetic programming is a method for getting a computer to solve a problem by telling it what needs to be done instead of how to do it. Koza, Bennett, Andre, and Keane present genetically evolved solutions to dozens of problems of design, optimal control, classification, system identification, function learning, and computational molecular biology. Among the solutions are 14 results competitive with human-produced results, including 10 rediscoveries of previously patented inventions. Researchers in artificial intelligence, machine learning, evolutionary computation, and genetic algorithms will find this an essential reference to the most recent and most important results in the rapidly growing field of genetic programming. This work: explains how the success of genetic programming arises from seven fundamental differences distinguishing it from conventional approaches to artificial intelligence and machine learning; describes how genetic programming uses architecture-altering operations to make on-the-fly decisions on whether to use subroutines, loops, recursions, and memory; demonstrates that genetic programming possesses 16 attributes that can reasonably be expected of a system for automatically creating computer programs; and presents the general-purpose Genetic Programming Problem Solver. It focuses on the previously unsolved problem of analog circuit synthesis, presenting genetically evolved filters, amplifiers, computational circuits, a robot controller circuit, source identification circuits, a temperature-measuring circuit, a voltage reference circuit, and more; introduces evolvable hardware in the form of field-programmable gate arrays; and, includes an introduction to genetic programming for the uninitiated.
(source: Nielsen Book Data)

Explanation of genetic programming, which seeks to make computer do what needs to be done without being told exactly how to do it by automatically creating a working computer program from a high level statement of the problem. Includes a bibliography at the end of the video.

• 1 Genetic Programming as Machine Learning 2 Genetic Programming and Biology 3 Computer Science and Mathematical Basics 4 Genetic Programming as Evolutionary Computation 5 Basic ConceptsThe Foundation 6 CrossoverThe Center of the Storm 7 Genetic Programming and Emergent Order 8 AnalysisImproving Genetic Programming with Statistics 9 Different Varieties of Genetic Programming 10 Advanced Genetic Programming 11 ImplementationMaking Genetic Programming Work 12 Applications of Genetic Programming 13 Summary and Perspectives A Printed and Recorded Resources B Information Available on the Internet C GP Software D Events.
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Since the early 1990s, genetic programming (GP) - a discipline whose goal is to enable the automatic generation of computer programs - has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks. This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.
(source: Nielsen Book Data)

• [I]. Long presentations: 1. Ariadne : evolving test data using grammatical evolution / Muhammad Sheraz Anjum, Conor Ryan
• 2. Quantum program synthesis : swarm algorithms and benchmarks / Timothy Atkinson, Athena Karsa, John Drake, Jerry Swan
• 3. A genetic programming approach to predict mosquitoes abundance / Riccardo Gervasi, Irene Azzali, Donal Bisanzio, Andrea Mosca, Luigi Bertolotti, Mario Giacobini
• 4. Complex network analysis of a genetic programming phenotype network / Ting Hu, Marco Tomassini, Wolfgang Banzhof
• 5. Improving genetic programming with novel exploration : exploitation control / Jonathan Kelly, Erik Hemberg, Una-May O'Reilly
• 6. Towards a scalable EA-based optimization of digital circuits / Jitka Kocnova, Zdenek Vasicek
• 7. Cartesian genetic programming as an optimizer of programs evolved with geometric semantic genetic programming / Ondrej Koncal, Lukas Sekanina
• 8. Can genetic programming do manifold learning too? / Andrew Lensen, Bing Xue, Mengjie Zhang
• 9. Why is auto-encoding difficult for genetic programming? / James McDermott
• 10. Solution and fitness evolution (SAFE) : coevolving solutions and their objective functions / Moshe Sipper, James H. Moore, Ryan J. Urbanowicz
• 11. A model of external memory for navigation in partially observable visual reinforcement learning tasks / Robert J. Smith, Malcolm I. Heywood
• 12. Fault detection and classification for induction motors using genetic programming / Yu Zhang, Ting Hu, Xiaodong Liang, Mohammad Zawad Ali, Md. Nasmus Sakib Khan Shabbir.

• [II]. Short presentations: 13. Fast DENSER : efficient deep NeuroEvolution / Filipe Assunção, Nuno Lourenço, Penousal Machado, Bernardete Ribeiro
• 14. A vectorial approach to genetic programming / Irene Azzali, Leonardo Vanneschi, Sara Silva, Illya Bakurov, Mario Giacobini
• 15. Comparison of genetic programming methods on design of cryptographic Boolean functions / Jakub Husa
• 16. Evolving AVX512 parallel C code using GP / William B. Langdon, Ronny Lorenz
• 17. Hyper-bent Boolean functions and evolutionary algorithms / Luca Mariot, Domagoj Jakobovic, Alberto Leporati, Stjepan Picek
• 18. Learning class disjointness axioms using grammatical evolution / Thu Huong Nguyen, Andrea G.B. Tettamanzi.

This book constitutes the refereed proceedings of the 22nd European Conference on Genetic Programming, EuroGP 2019, held as part of Evo* 2019, in Leipzig, Germany, in April 2019, co-located with the Evo* events EvoCOP, EvoMUSART, and EvoApplications. The 12 revised full papers and 6 short papers presented in this volume were carefully reviewed and selected from 36 submissions. They cover a wide range of topics and reflect the current state of research in the field. With a special focus on real-world applications in 2019, the papers are devoted to topics such as the test data design in software engineering, fault detection and classification of induction motors, digital circuit design, mosquito abundance prediction, machine learning and cryptographic function design. .
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Long Presentations
• A Self-Adaptive Approach to Exploit Topological Properties of Different GAs' Crossover Operators
• 1 Introduction
• 2 Fundamental Concepts
• 2.1 Crossover
• 2.2 Convex Combination, Convex Hull, and Convex Search
• 3 Related Works
• 4 Methodology
• 4.1 Dynamic Diversity Maintenance
• 4.2 Self-adaptive Crossover
• 5 Experimental Settings
• 6 Experimental Results
• 7 Conclusions
• References
• A Genetic Programming Encoder for Increasing Autoencoder Interpretability
• 1 Introduction
• 1.1 Structure

• 2 Background and Related Work
• 2.1 Non-linear Dimensionality Reduction
• 2.2 Evolutionary Computation for Dimensionality Reduction
• 2.3 Genetic Programming for Autoencoding
• 3 Proposed Method: GPE-AE
• 3.1 GP Representation of Encoder
• 3.2 Fitness Evaluation
• 3.3 Decoder Architecture
• 4 Experiment Design
• 4.1 Comparison Methods
• 4.2 Evaluation Measures
• 4.3 Datasets
• 5 Results
• 6 Further Analysis
• 7 Conclusions
• References
• Graph Networks as Inductive Bias for Genetic Programming: Symbolic Models for Particle-Laden Flows
• 1 Introduction

• 2 Background and Related Work
• 2.1 Genetic Programming in Physics Applications
• 2.2 Machine Learning for Particle-Laden Flows
• 3 Proposed Methods
• 3.1 Graph Networks
• 3.2 Genetic Programming
• 4 Experiment Design
• 4.1 Data Generation: Simulation of Particle-Laden Flows
• 4.2 Data Preprocessing
• 4.3 Algorithm Settings
• 5 Results and Analysis
• 5.1 Overall Algorithm Performance
• 5.2 Explainability of Equations
• 5.3 Validation of Symbolic Models
• 6 Conclusion and Future Work
• References
• Phenotype Search Trajectory Networks for Linear Genetic Programming

• 1 Introduction
• 2 The LGP System
• 2.1 Boolean LGP Algorithm
• 2.2 Genotype, Phenotype, and Fitness
• 3 Kolmogorov Complexity
• 4 Sampling and Metrics Estimation
• 5 Search Trajectory Networks
• 5.1 General Definitions
• 5.2 The Proposed STN Models
• 5.3 Network Visualisation
• 5.4 Comparing Three Targets with Increasing Difficulty
• 6 Discussion
• References
• GPAM: Genetic Programming with Associative Memory
• 1 Introduction
• 2 Related Work
• 2.1 Symbolic Regression and Genetic Programming
• 2.2 Efficient Processing of DNNs
• 2.3 Weight Compression
• 3 Proposed Method

• 3.1 The GPAM Approach
• 3.2 GPAM for Weight Generation
• 4 Results for Symbolic Regression Benchmarks
• 4.1 Benchmarks
• 4.2 Setup
• 4.3 Memory Sizing
• 4.4 Role of Constants in GPAM
• 5 Results for Weight Generation
• 6 Discussion and Conclusions
• References
• MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning
• 1 Introduction
• 2 Related Work
• 2.1 Semantic GP
• 2.2 GP-Based Ensemble Learning
• 2.3 Quality Diversity Optimization
• 3 The Proposed Ensemble Learning Algorithm
• 3.1 The Overall Framework
• 3.2 Angle-Based Dimensionality Reduction

This book constitutes the refereed proceedings of the 26th European Conference on Genetic Programming, EuroGP 2023, held as part of EvoStar 2023, in Brno, Czech Republic, during April 12-14, 2023, and co-located with the EvoStar events, EvoCOP, EvoMUSART, and EvoApplications. The 14 revised full papers and 8 short papers presented in this book were carefully reviewed and selected from 38 submissions. The wide range of topics in this volume reflects the current state of research in the field. The collection of papers cover topics including developing new variants of GP algorithms for both optimization and machine learning problems as well as exploring GP to address complex real-world problems. .

• Contributing Authors.- Preface.- Foreword.- Genetic Programming: Theory and Practice.- Better Solutions Faster: Soft Evolution of Robust Regression Models in Pareto Genetic Programming.- Manipulation of Convergence in Evolutionary Systems.- Large-Scale, Time-Constrained Symbolic Regression-Classification.- Solving Complex Problems in Human Genetics Using Genetic Programming.- Towards an Information Theoretic Framework for Genetic Programming.- Investigating Problem Hardness in Real Life Applications.- Improving the Scalability of Generative Representations for Open-Ended Design.- Program Structure-Fitness Disconnect and Its Impact on Evolution in GP.- Genetic Programming with Reuse of Known Designs.- Robust Engineering Design of Electronic Circuits with Active Components Using Genetic Programming and Bond Graphs.- Trustable Symbolic Regression Models.- Improving Performance and Cooperation in Multi-Agent Systems.- An Empirical Study of Multi-Objective Algorithms for Stock Ranking.- Using GP and Cultural Algorithms to Simulate the Evolution of an Ancient Urban Center.- Index.
• (source: Nielsen Book Data)

Genetic Programming Theory and Practice V was developed from the fifth workshop at the University of Michigan's Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena 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. The work covers applications of GP to a wide variety of domains, including bioinformatics, symbolic regression for system modeling, financial modeling, circuit design and robot controllers. This volume is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.
(source: Nielsen Book Data)

• Plenary Talks.- A Grammatical Genetic Programming Approach to Modularity in Genetic Algorithms.- An Empirical Boosting Scheme for ROC-Based Genetic Programming Classifiers.- Confidence Intervals for Computational Effort Comparisons.- Crossover Bias in Genetic Programming.- Density Estimation with Genetic Programming for Inverse Problem Solving.- Empirical Analysis of GP Tree-Fragments.- Empirical Comparison of Evolutionary Representations of the Inverse Problem for Iterated Function Systems.- Evolution of an Efficient Search Algorithm for the Mate-In-N Problem in Chess.- Fast Genetic Programming on GPUs.- FIFTHTM: A Stack Based GP Language for Vector Processing.- Genetic Programming with Fitness Based on Model Checking.- Geometric Particle Swarm Optimisation.- GP Classifier Problem Decomposition Using First-Price and Second-Price Auctions.- Layered Learning in Boolean GP Problems.- Mining Distributed Evolving Data Streams Using Fractal GP Ensembles.- Multi-objective Genetic Programming for Improving the Performance of TCP.- On Population Size and Neutrality: Facilitating the Evolution of Evolvability.- On the Limiting Distribution of Program Sizes in Tree-Based Genetic Programming.- Predicting Prime Numbers Using Cartesian Genetic Programming.- Real-Time, Non-intrusive Evaluation of VoIP.- Training Binary GP Classifiers Efficiently: A Pareto-coevolutionary Approach.- Posters.- A Comprehensive View of Fitness Landscapes with Neutrality and Fitness Clouds.- Analysing the Regularity of Genomes Using Compression and Expression Simplification.- Changing the Genospace: Solving GA Problems with Cartesian Genetic Programming.- Code Regulation in Open Ended Evolution.- Data Mining of Genetic Programming Run Logs.- Evolving a Statistics Class Using Object Oriented Evolutionary Programming.- Evolving Modular Recursive Sorting Algorithms.- Fitness Landscape Analysis and Image Filter Evolution Using Functional-Level CGP.- Genetic Programming Heuristics for Multiple Machine Scheduling.- Group-Foraging with Particle Swarms and Genetic Programming.- Multiple Interactive Outputs in a Single Tree: An Empirical Investigation.- Parsimony Doesn't Mean Simplicity: Genetic Programming for Inductive Inference on Noisy Data.- The Holland Broadcast Language and the Modeling of Biochemical Networks.- The Induction of Finite Transducers Using Genetic Programming.
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This book constitutes the refereed proceedings of the 10th European Conference on Genetic Programming, EuroGP 2007, held in Valencia, Spain in April 2007 colocated with EvoCOP 2007. The 21 revised plenary papers and 14 revised poster papers were carefully reviewed and selected from 71 submissions. The papers address fundamental and theoretical issues, along with a wide variety of papers dealing with different application areas.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 9th European Conference on Genetic Programming, EuroGP 2006, held in Budapest, Hungary, in April 2006, colocated with EvoCOP 2006. The 21 revised plenary papers and 11 revised poster papers were carefully reviewed and selected from 59 submissions. The papers address fundamental and theoretical issues, along with a wide variety of papers dealing with different application areas, such as computer science, engineering, machine learning, Kolmogorov complexity, biology and computational design, showing that GP is a powerful and practical problem-solving paradigm.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 7th European Conference on Genetic Programming, EuroGP 2004, held in Coimbra, Portugal, in April 2004. The 38 revised papers presented were carefully reviewed and selected from 61 submissions. The papers deal with a variety of foundational and methodological issues as well as with advanced applications in areas like engineering, computer science, language understanding, bioinformatics, and design.
(source: Nielsen Book Data)