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• Chapter 1. Finding Simple Solutions to Multi-Task Visual Reinforcement Learning Problems with Tangled Program Graphs.-
• Chapter 2. Grammar-based Vectorial Genetic Programming for Symbolic Regression.-
• Chapter 3. Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming.-
• Chapter 4. What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?.-
• Chapter 5. An Exploration of Exploration: Measuring the ability of lexicaseselection to find obscure pathways to optimality.-
• Chapter 6. Feature Discovery with Deep Learning Algebra Networks.-
• Chapter 7. Back To The Future - Revisiting OrdinalGP & Trustable Models After a Decade.-
• Chapter 8. Fitness First.-
• Chapter 9. Designing Multiple ANNs with Evolutionary Development: Activity Dependence.-
• Chapter 10. Evolving and Analyzing modularity with GLEAM (Genetic Learning by Extraction and Absorption of Modules).-
• Chapter 11. Evolution of the Semiconductor Industry, and the Start of X Law.
• (source: Nielsen Book Data)

This 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 year's edition, the topics covered include many of the most important issues and research questions in the field, such as opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient 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.
(source: Nielsen Book Data)

• Intro; Foreword; Preface; Contents; List of Acronyms; 1 Program synthesis; 1.1 The nature of computer programs; 1.2 Program synthesis; 1.3 Specifying program correctness; 1.4 Challenges in program synthesis; 1.5 Paradigms of program synthesis; 1.5.1 Deductive program synthesis; 1.5.2 Inductive programming; 1.5.3 Genetic programming; 1.6 Consequences of automated program synthesis; 1.6.1 Program improvement; 1.6.2 Hybrid and interactive program synthesis; 1.7 Summary; 2 Limitations of conventional program evaluation; 2.1 Evaluation bottleneck; 2.2 Consequences of evaluation bottleneck

• 2.2.1 Discreteness and loss of gradient2.2.2 Compensation; 2.2.3 Biased search; 2.3 Experimental demonstration; 2.4 Discussion; 2.5 Related concepts; 2.6 Summary and the main postulate; 3 The framework of behavioral program synthesis; 3.1 Program traces and execution records; 3.2 Realization of execution record; 3.3 Summary; 4 Behavioral assessment of test difficulty; 4.1 Test-based problems; 4.2 Implicit fitness sharing; 4.3 Promoting combinations of skills via cosolvability; 4.4 Deriving objectives from program-test interactions; 4.5 Summary; 5 Semantic Genetic Programming

• 5.1 Program semantics5.2 Semantic Genetic Programming; 5.3 Geometric Semantic Genetic Programming; 5.3.1 Approximate geometric crossover; 5.3.2 Exact geometric crossover; 5.4 Summary; 6 Synthesizing programs with consistent execution traces; 6.1 Information content of execution states; 6.2 Trace consistency measure; 6.3 Trace consistency for non-linear programs; 6.4 Summary; 7 Pattern-guided program synthesis; 7.1 Motivation; 7.2 Discovering patterns in program behavior; 7.2.1 Transforming an execution record into an ML dataset; 7.2.2 Classifier induction; 7.2.3 Evaluation functions

• 7.3 Discussion and related concepts7.4 Summary; 8 Behavioral code reuse; 8.1 Identification of useful subprograms; 8.2 Archiving subprograms; 8.3 Reuse of subprograms; 8.4 Discussion; 8.5 Summary; 9 Search drivers; 9.1 Rationale for the unified perspective; 9.2 Design rationale; 9.3 Definition; 9.4 Search drivers vs. selection operators; 9.5 Universal search drivers; 9.6 Problem-specific search drivers; 9.7 Quality of search drivers; 9.8 Employing multiple search drivers; 9.9 Multiobjective selection with search drivers; 9.10 Related concepts; 9.11 Efficiency; 9.12 Summary

• 10 Experimental assessment of search drivers10.1 Scope; 10.2 Program synthesis tasks; 10.3 Combinations of search drivers; 10.4 Configurations with subprogram archives; 10.5 Importance of subprogram selection; 10.6 Contextual search drivers; 10.7 Discussion; 11 Implications of the behavioral perspective; 11.1 Conceptual consequences; 11.2 Architectural implications; 11.3 Summary; 12 Future perspectives; 12.1 The prospects; 12.2 Closing remarks; Index; References

Genetic programming (GP) is a popular heuristic methodology of program synthesis with origins in evolutionary computation. In this generate-and-test approach, candidate programs are iteratively produced and evaluated. The latter involves running programs on tests, where they exhibit complex behaviors reflected in changes of variables, registers, or memory. That behavior not only ultimately determines program output, but may also reveal its `hidden qualities' and important characteristics of the considered synthesis problem. However, the conventional GP is oblivious to most of that information and usually cares only about the number of tests passed by a program. This `evaluation bottleneck' leaves search algorithm underinformed about the actual and potential qualities of candidate programs. This book proposes behavioral program synthesis, a conceptual framework that opens GP to detailed information on program behavior in order to make program synthesis more efficient. Several existing and novel mechanisms subscribing to that perspective to varying extent are presented and discussed, including implicit fitness sharing, semantic GP, co-solvability, trace convergence analysis, pattern-guided program synthesis, and behavioral archives of subprograms. The framework involves several concepts that are new to GP, including execution record, combined trace, and search driver, a generalization of objective function. Empirical evidence gathered in several presented experiments clearly demonstrates the usefulness of behavioral approach. The book contains also an extensive discussion of implications of the behavioral perspective for program synthesis and beyond.

• Long Presentations.- Evolving Adaptive Neural Network Optimizers for Image Classification.- Combining Geometric Semantic GP with Gradient-descent Optimization.- One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems.- Multi-objective GP with AWS for Symbolic Regression.- SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming.- Evolutionary Design of Reduced Precision Levodopa-Induced Dyskinesia Classifiers.- Using Denoising Autoencoder Genetic Programming to Control Exploration and Exploitation in Search.- Program Synthesis with Genetic Programming: The Influence of Batch Sizes.- Genetic Programming-Based Inverse Kinematics for Robotic Manipulators.- On the Schedule for Morphological Development of Evolved Modular Soft Robots.- An Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling.- Cooperative Co-Evolution and Adaptive Team Composition for a Multi-Rover Resources Allocation Problem.- Short Presentations.- Synthesizing Programs from Program Pieces using Genetic Programming and Refinement Type Checking.- Creating Diverse Ensembles for Classification with Genetic Programming and Neuro-MAP-Elites.- Evolving Monotone Conjunctions in Regimes Beyond Proved Convergence.- Accurate and Interpretable Representations of Environments with Anticipatory Learning Classifier Systems.- Exploiting Knowledge from Code to Guide Program Search.- Multi-Objective Genetic Programming for Explainable Reinforcement Learning.- Permutation-Invariant Representation of Neural Networks with Neuron Embeddings.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 25th European Conference on Genetic Programming, EuroGP 2022, held as part of Evo*2021, as Virtual Event, in April 2022, co-located with the Evo*2022 events, EvoCOP, EvoMUSART, and EvoApplications. The 12 revised full papers and 7 short papers presented in this book were carefully reviewed and selected from 35 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 operators for variants of GP algorithms, as well as exploring GP applications to the optimization of machine learning methods and the evolution of complex combinational logic circuits. .
(source: Nielsen Book Data)

• Quality Diversity Genetic Programming for Learning Decision Tree Ensembles.- Progressive Insular Cooperative GP.- Regenerating Soft Robots through Neural Cellular Automata.- Inclusive Genetic Programming.- Towards incorporating Human Knowledge in Fuzzy Pattern Tree Evolution.- Evolutionary Neural Architecture Search Supporting Approximate Multipliers.- Automatic design of deep neural networks applied to image segmentation problems.- On the Influence of Grammars on Crossover in Grammatical Evolution.- On the Generalizability of Programs Synthesized by Grammar-Guided Genetic Programming.- Evolution of Complex Combinational Logic Circuits Using Grammatical Evolution with SystemVerilog.- Evofficient: Reproducing a Cartesian Genetic Programming Method.- Software Anti-patterns Detection Under Uncertainty Using A Possibilistic Evolutionary Approach.- Probabilistic Grammatical Evolution.- Evolving allocation rules for beam search heuristics in assembly line balancing.- Incremental Evaluation of Genetic Programming.- Mining Feature Relationships in Data.- Getting a Head Start on Program Synthesis with Genetic Programming.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 24th European Conference on Genetic Programming, EuroGP 2021, held as part of Evo*2021, as Virtual Event, in April 2021, co-located with the Evo*2021 events, EvoCOP, EvoMUSART, and EvoApplications. The 11 revised full papers and 6 short papers presented in this book were carefully reviewed and selected from 27 submissions. The wide range of topics in this volume reflects the current state of research in the field. The collection of papers cover interesting topics including developing new operators for variants of GP algorithms, as well as exploring GP applications to the optimisation of machine learning methods and the evolution of complex combinational logic circuits.
(source: Nielsen Book Data)

• Hessian Complexity Measure for Genetic Programming-based Imputation Predictor Selection in Symbolic Regression with Incomplete Data.- Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing.- Incremental Evolution and Development of Deep Artificial Neural Networks.- Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming.- Comparing Genetic Programming Approaches for Non-Functional Genetic Improvement.- Automatically Evolving Lookup Tables for Function Approximation.- Optimising Optimisers with Push GP.- An Evolutionary View on Reversible Shift-invariant Transformations.- Benchmarking Manifold Learning Methods on a Large Collection of Datasets.- Ensemble Genetic Programming.- SGP-DT: Semantic Genetic Programming Based on Dynamic Targets.- Effect of Parent Selection Methods on Modularity.- Time Control or Size Control? Reducing Complexity and Improving Accuracy of Genetic Programming Models.- Challenges of Program Synthesis with Grammatical Evolution.- Detection of Frailty Using Genetic Programming : The Case of Older People in Piedmont, Italy.- Is k Nearest Neighbours Regression Better than GP.- Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling.- Classification of Autism Genes using Network Science and Linear Genetic Programming.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EvoCOP, EvoMUSART and EvoApplications. The 12 full papers and 6 short papers presented in this book were carefully reviewed and selected from 36 submissions. The papers cover a wide spectrum of topics, including designing GP algorithms for ensemble learning, comparing GP with popular machine learning algorithms, customising GP algorithms for more explainable AI applications to real-world problems.
(source: Nielsen Book Data)

• Oral Presentations.- One-Class Genetic Programming.- Genetic Programming Based Approach for Synchronization with Parameter Mismatches in EEG.- Memory with Memory in Tree-Based Genetic Programming.- On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems.- Why Coevolution Doesn't "Work": Superiority and Progress in Coevolution.- On Improving Generalisation in Genetic Programming.- Mining Evolving Learning Algorithms.- The Role of Population Size in Rate of Evolution in Genetic Programming.- Genetic Programming Crossover: Does It Cross over?.- Evolution of Search Algorithms Using Graph Structured Program Evolution.- Genetic Programming for Feature Subset Ranking in Binary Classification Problems.- Self Modifying Cartesian Genetic Programming: Fibonacci, Squares, Regression and Summing.- Automatic Creation of Taxonomies of Genetic Programming Systems.- Extending Operator Equalisation: Fitness Based Self Adaptive Length Distribution for Bloat Free GP.- Modeling Social Heterogeneity with Genetic Programming in an Artificial Double Auction Market.- Exploring Grammatical Evolution for Horse Gait Optimisation.- There Is a Free Lunch for Hyper-Heuristics, Genetic Programming and Computer Scientists.- Tree Based Differential Evolution.- A Rigorous Evaluation of Crossover and Mutation in Genetic Programming.- On Crossover Success Rate in Genetic Programming with Offspring Selection.- An Experimental Study on Fitness Distributions of Tree Shapes in GP with One-Point Crossover.- Posters.- Behavioural Diversity and Filtering in GP Navigation Problems.- A Real-Time Evolutionary Object Recognition System.- On the Effectiveness of Evolution Compared to Time-Consuming Full Search of Optimal 6-State Automata.- Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued Function Regression.- Beneficial Preadaptation in the Evolution of a 2D Agent Control System with Genetic Programming.- Adaptation, Performance and Vapnik-Chervonenkis Dimension of Straight Line Programs.- A Statistical Learning Perspective of Genetic Programming.- Quantum Circuit Synthesis with Adaptive Parameters Control.- Comparison of CGP and Age-Layered CGP Performance in Image Operator Evolution.
• (source: Nielsen Book Data)

The 12th European Conference on Genetic Programming, EuroGP 2009, took place in Tu ..bingen, Germany during April 15-17 at one of the oldest univer- ties in Germany, the Eberhard Karls Universitat .. Tubing .. en. This volume c- tains manuscripts of the 21 oral presentations held during the day, and the nine posters that were presented during a dedicated evening session and reception. The topics covered in this volume re?ectthecurrentstateoftheartofgenetic programming, including representations, theory, operators and analysis, feature selection, generalization, coevolution, and numerous applications. A rigorous, double-blind peer-review process was used, with each submission reviewed by at least three members of the international ProgramCommittee. In total, 57 papers were submitted with an acceptance rate of 36% for full papers and an overall acceptance rate of 52% including posters. The MyReview m- agement software originally developed by Philippe Rigaux, Bertrand Chardon, and other colleagues from the Universit' e Paris-Sud Orsay, France was used for the reviewing process. We are sincerely grateful to Marc Schoenauer from IN- RIA, France for his continued assistance in hosting and managing the software. Paper review assigments were largely done by an optimization process matching paper keywords to keywords of expertise submitted by reviewers.
(source: Nielsen Book Data)

• 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.
• (source: Nielsen Book Data)

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)

• Genetic Programming Theory and Practice.- Discovering Financial Technical Trading Rules Using.- Abstraction GP.- Using Genetic Programming in Industrial Statistical Model Building Population Sizing for Genetic Programming.- Considering the Roles of Structure in Problem Solving by Computer.- Lessons Learned using Genetic Programming in a Stock Picking Context Favourable Biasing of Function Sets.- Toward Automated Design of Industrial-Strength Analog Circuits by Means of Genetic Programming.- Topological Synthesis of Robust Systems.- Does Genetic Programming Inherently Adopt Structured DesignTechniques?- Genetic Programming of an Algorithmic Chemistry.- ACGP: Adaptable Constrained Genetic Programming.- Searching for Supply Chain Reordering Policies.- Cartesian Genetic Programming and the Post Docking Filtering Problem.- Listening to Data: Tuning a Genetic Programming System.- Incident Detection on Highways.- Pareto-Front Exploitation in Symbolic Regression.- An Evolved Antenna for a NASA Mission.
• (source: Nielsen Book Data)

The work described in this book was first presented at the Second Workshop on Genetic Programming, Theory and Practice, organized by the Center for the Study of Complex Systems at the University of Michigan, Ann Arbor, 13-15 May 2004. The goal of this workshop series is to promote the exchange of research results and ideas between those who focus on Genetic Programming (GP) theory and those who focus on the application of GP to various re- world problems. In order to facilitate these interactions, the number of talks and participants was small and the time for discussion was large. Further, participants were asked to review each other's chapters before the workshop. Those reviewer comments, as well as discussion at the workshop, are reflected in the chapters presented in this book. Additional information about the workshop, addendums to chapters, and a site for continuing discussions by participants and by others can be found at http://cscs.umich.edu:8000/GPTP-20041. We thank all the workshop participants for making the workshop an exciting and productive three days. In particular we thank all the authors, without whose hard work and creative talents, neither the workshop nor the book would be possible. We also thank our keynote speakers Lawrence ("Dave") Davis of NuTech Solutions, Inc., Jordan Pollack of Brandeis University, and Richard Lenski of Michigan State University, who delivered three thought-provoking speeches that inspired a great deal of discussion among the participants.
(source: Nielsen Book Data)

• Contributors
• Acknowledgments
• 1. An Introduction to the Third Volume / Lee Spector, William B. Langdon, Una-May O'Reilly, Peter J. Angelino
• I. Applications
• 2. An Automatic Software Re-Engineering Tool Based on Genetic Programming / Conor Ryan and Laur Ivan
• 3. CAD Surface Reconstruction from Digitized 3D Point Data with a Genetic Programming/Evolution Strategy Hybrid / Robert E. Keller, Wolfgang Banzhaf, Jorn Mehnen and Klaus Weinert
• 4. A Genetic Programming Approach for Robust Language Interpretation / Carolyn Penstein Rose
• 5. Time Series Modeling Using Genetic Programming: An Application to Rainfall-Runoff Models / Peter A. Whigham and Peter F. Crapper
• 6. Automatic Synthesis, Placement, and Routing of Electrical Circuits by Means of Genetic Programming / John R. Koza and Forest H. Bennett III
• 7. Quantum Computing Applications of Genetic Programming / Lee Spector, Howard Barnum, Herbert J. Bernstein and Nikhil Swamy
• II. Theory
• II. Theory
• 8. The Evolution of Size and Shape / William B. Langdon, Terry Soule, Riccardo Poli and James A. Foster
• 9. Fitness Distributions: Tools for Designing Efficient Evolutionary Computations / Christian Igel and Kumar Chellapilla
• 10. Analysis of Single-Node (Building) Blocks in Genetic Programming / Jason M. Daida, Robert R. Bertram, John A. Polito 2 and Stephen A. Stanhope
• 11. Rooted-Tree Schemata in Genetic Programming / Justinian P. Rosca and Dana H. Ballard
• III. Extensions
• III. Extensions
• 12. Efficient Evolution of Machine Code for CISC Architectures Using Instruction Blocks and Homologous Crossover / Peter Nordin, Wolfgang Banzhaf and Frank D. Francone
• 13. Sub-machine-code Genetic Programming / Riccardo Poli and William B. Langdon
• 14. The Internal Reinforcement of Evolving Algorithms / Astro Teller
• 15. Inductive Genetic Programming with Immune Network Dynamics / Nikolay I. Nikolaev, Hitoshi Iba and Vanio Slavov
• 16. A Self-Tuning Mechanism for Depth-Dependent Crossover / Takuyo Ito, Hitoshi Iba and Satoshi Sato
• 17. Genetic Recursive Regression for Modeling and Forecasting Real-World Chaotic Time Series / Geum Yong Lee
• 18. Co-evolutionary Fitness Switching: Learning Complex Collective Behaviors Using Genetic Programming / Byoung-Tak Zhang and Dong-Yeon Cho
• 19. Evolving Multiple Agents by Genetic Programming / Hitoshi Iba
• Index.

Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time-and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programming highlights many of the recent technical advances in this increasingly popular field. Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time-and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programming highlights many of the recent technical advances in this increasingly popular field.
(source: Nielsen Book Data)
Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time -- and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of "Advances in Genetic Programming" highlights many of the recent technical advances in this increasingly popular field.
(source: Nielsen Book Data)

• Evolutionary Computation: from Genetic Algorithms to Genetic Programming.- Automatically Defined Functions in Gene Expression Programming.- Evolving Intrusion Detection Systems.- Evolutionary Pattern Matching Using Genetic Programming.- Genetic Programming in Data Modelling.- Stock Market Modeling Using Genetic Programming Ensembles.- Evolutionary Digital Circuit Design Using Genetic Programming.- Evolving Complex Robotic Behaviors Using Genetic Programming.- Automatic Synthesis of Microcontroller Assembly Code Through Linear Genetic Programming.
• (source: Nielsen Book Data)

Designing complex programs such as operating systems, compilers, filing systems, data base systems, etc. is an old ever lasting research area. Genetic programming is a relatively new promising and growing research area. Among other uses, it provides efficient tools to deal with hard problems by evolving creative and competitive solutions. Systems Programming is generally strewn with such hard problems. This book is devoted to reporting innovative and significant progress about the contribution of genetic programming in systems programming. The contributions of this book clearly demonstrate that genetic programming is very effective in solving hard and yet-open problems in systems programming. Followed by an introductory chapter, in the remaining contributed chapters, the reader can easily learn about systems where genetic programming can be applied successfully. These include but are not limited to, information security systems, compilers, data mining systems, stock market prediction systems, robots and automatic programming.
(source: Nielsen Book Data)

• Part I Introduction.- 1 Introduction.- 2 Preliminaries.- Part II Genetic Programming for Static Production Scheduling Problems.- 3 Learning Schedule Construction Heuristics.- 4 Learning Schedule Improvement Heuristics.- 5 Learning to Augment Operations Research Algorithms.- Part III Genetic Programming for Dynamic Production Scheduling Problems.- 6 Representations with Multi-tree and Cooperative Coevolution.- 7 Efficiency Improvement with Multi-fidelity Surrogates.- 8 Search Space Reduction with Feature Selection.- 9 Search Mechanism with Specialised Genetic Operators.- Part IV Genetic Programming for Multi-objective Production Scheduling Problems.- 10 Learning Heuristics for Multi-objective Dynamic Production Scheduling Problems.- 11 Cooperative Coevolutionary for Multi-objective Production Scheduling Problems.- 12 Learning Scheduling Heuristics for Multi-objective Dynamic Flexible Job Shop Scheduling.- Part V Multitask Genetic Programming for Production Scheduling Problems.- 13 Multitask Learning in Hyper-heuristic Domain with Dynamic Production Scheduling.- 14 Adaptive Multitask Genetic Programming for Dynamic Job Shop Scheduling.- 15 Surrogate-Assisted Multitask Genetic Programming for Learning Scheduling Heuristics.- Part VI Conclusions and Prospects.- 16 Conclusions and Prospects.
• (source: Nielsen Book Data)

This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP's performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future. Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.
(source: Nielsen Book Data)

• On the Foundations and the Applications of Evolutionary Computing / Pierre Del Moral, Alexandru-Adrian Tantar and Emilia Tantar
• Incorporating Regular Vines in Estimation of Distribution Algorithms / Rogelio Salinas-Gutiérrez, Arturo Hernández-Aguirre and Enrique R. Villa-Diharce
• The Gaussian Polytree EDA with Copula Functions and Mutations / Ignacio Segovia Domínguez, Arturo Hernández Aguirre and Enrique Villa Diharce
• On Quality Indicators for Black-Box Level Set Approximation / Michael T.M. Emmerich, André H. Deutz and Johannes W. Kruisselbrink
• Set Oriented Methods for the Numerical Treatment of Multiobjective Optimization Problems / Oliver Schütze, Katrin Witting, Sina Ober-Blöbaum and Michael Dellnitz
• A Complex-Networks View of Hard Combinatorial Search Spaces / Marco Tomassini and Fabio Daolio
• Cooperative Coevolution for Agrifood Process Modeling / Olivier Barrière, Evelyne Lutton, Pierre-Henri Wuillemin, Cédric Baudrit and Mariette Sicard, et al.
• Hybridizing cGAs with PSO-like Mutation / E. Alba and A. Villagra
• On Gradient-Based Local Search to Hybridize Multi-objective Evolutionary Algorithms / Adriana Lara, Oliver Schütze and Carlos A. Coello Coello
• On the Integration of Theoretical Single-Objective Scheduling Results for Multi-objective Problems / Christian Grimme, Markus Kemmerling and Joachim Lepping
• Analysing the Robustness of Multiobjectivisation Approaches Applied to Large Scale Optimisation Problems / Carlos Segura, Eduardo Segredo and Coromoto León
• A Comparative Study of Heuristic Conversion Algorithms, Genetic Programming and Return Predictability on the German Market / Esther Mohr, Günter Schmidt and Sebastian Jansen.

The aim of this book is to provide a strong theoretical support for understanding and analyzing the behavior of evolutionary algorithms, as well as for creating a bridge between probability, set-oriented numerics and evolutionary computation. The volume encloses a collection of contributions that were presented at the EVOLVE 2011 international workshop, held in Luxembourg, May 25-27, 2011, coming from invited speakers and also from selected regular submissions. The aim of EVOLVE is to unify the perspectives offered by probability, set oriented numerics and evolutionary computation. EVOLVE focuses on challenging aspects that arise at the passage from theory to new paradigms and practice, elaborating on the foundations of evolutionary algorithms and theory-inspired methods merged with cutting-edge techniques that ensure performance guarantee factors. EVOLVE is also intended to foster a growing interest for robust and efficient methods with a sound theoretical background. The chapters enclose challenging theoretical findings, concrete optimization problems as well as new perspectives. By gathering contributions from researchers with different backgrounds, the book is expected to set the basis for a unified view and vocabulary where theoretical advancements may echo in different domains.
(source: Nielsen Book Data)

• 1. Characterizing the Effects of Random Subsampling on Lexicase Selection.-
• 2. It is Time for New Perspectives on How to Fight Bloatin GP.-
• 3. Explorations of the Semantic Learning Machine Neuroevolution Algorithm.-
• 4. Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics?.-
• 5. Symbolic Regression by Exhaustive Search - Reducing the Search Space using Syntactical Constraints and Efficient Semantic Structure Deduplication.-
• 6. Temporal Memory Sharing in Visual Reinforcement Learning.-
• 7. The Evolution of Representations in Genetic Programming Trees.-
• 8. How Competitive is Genetic Programming in Business Data Science Applications?.-
• 9. Using Modularity Metrics as Design Features to Guide Evolution in Genetic Programming.-
• 10. Evolutionary Computation and AI Safety.-
• 11. Genetic Programming Symbolic Regression.-
• 12. Hands-on Artificial Evolution through Brain Programming.-
• 13. Comparison of Linear Genome Representations For Software Synthesis.-
• 14. Enhanced Optimization with Composite Objectives and Novelty Pulsation.-
• 15. New Pathways in Coevolutionary Computation.-
• 16. 2019 Evolutionary Algorithms Review.-
• 17. Evolving a Dota 2 Hero Bot with a Probabilistic Shared Memory Model.-
• 18. Modelling Genetic Programming as a Simple Sampling Algorithm.-
• 19. An Evolutionary System for Better Automatic Software Repair.- Index.
• (source: Nielsen Book Data)

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. In this year's edition, the topics covered include many of the most important issues and research questions in the field, such as: opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms.The volume 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.
(source: Nielsen Book Data)

• SWEEP: A System for the Software Engineering of Evolving Programs - The Genetic Programming Element Agents - The Metrics Apprentice: Using Cultural Algorithms to Formulate Quality Metrics for Software Systems - An Example Problem for Automatic Programming: Solving the Noisy Sine Problem with Discipulus - Data Collection and Analysis - Analysis: The Relationship of Software Metrics to Bloat - Defining a New Software Metric to Estimate Generalization Using the Metrics Apprentice.
• (source: Nielsen Book Data)

This is the first book that attempts to provide a framework in which to embed an automatic programming system based on evolutionary learning (genetic programming) into a traditional software engineering environment. As such, it looks at how traditional software engineering knowledge can be integrated with an evolutionary programming process in a symbiotic way.
(source: Nielsen Book Data)

• A perspective on the work in this book / Kenneth E. Kinnear, Jr.
• Introduction to genetic programming / John R. Koza
• The evolution of evolvability in genetic programming / Lee Altenberg
• Genetic programming and emergent intelligence / Peter J. Angeline
• Scalable learning in genetic programming using automatic function definition / John R. Koza
• Alternatives in automatic function definition : a comparison of performance / Kenneth E. Kinnear, Jr.
• The donut problem : scalability, generalization and breeding policies in genetic programming / Walter Alden Tackett, Aviram Carmi
• Effects of locality in individual and population evolution / Patrik D'haeseleer, Jason Bluming
• The evolution of mental models / Astro Teller
• Evolution of obstacle avoidance behavior : using noise to promote robust solutions / Craig W. Reynolds
• Pygmies and civil servants / Conor Ryan
• Genetic programming using a minimum decsription length principle / Hitoshi Iba, Hugo de Garis, Taisuke Sato
• Genetic programming in C++: implementation issues / Mike J. Keith, Martin C. Martin. A compiling genetic programming system that directly manipulates the machine code / Peter Nordin
• Automatic generation of programs for crawling and walking / Graham Spencer
• Genetic programming for the acquisition of double auction market strategies / Martin Andrews, Richard Prager
• Two scientific applications of genetic programming : stack filters and non-linear equation fitting to chaotic data / Howard Oakley
• The automatic generation of plans for a mobile robot via genetic programming with automatically defined functions / Simon G. Handley
• Competitively evolving decision trees against fixed training cases for natural language processing / Eric V. Siegel
• Cracking and co-evolving randomizers / Jan Jannink
• Optimizing confidence of text classification by evolution of symbolic expressions / Brij Masand
• Evolvable 3D modeling for model-based object recognition systems / Thang Nguyen, Thomas Huang
• Automatically defined features : the simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them / David Andre
• Genetic micro programming of neural networks / Frédéric Gruau.

Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in manu of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public-domain code is available, and on how to become part of the active genetic programming community via electronic mail.
(source: Nielsen Book Data)
There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm. Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in many of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public domain code is available, and on how to become part of the active genetic programming community via electronic mail. A major focus of the book is on improving the power of genetic programming. Experimental results are presented in a variety of areas, including adding memory to genetic programming, using locality and "demes" to maintain evolutionary diversity, avoiding the traps of local optima by using coevolution, using noise to increase generality, and limiting the size of evolved solutions to improve generality. Significant theoretical results in the understanding of the processes underlying genetic programming are presented, as are several results in the area of automatic function definition. Performance increases are demonstrated by directly evolving machine code, and implementation and design issues for genetic programming in C++ are discussed.
(source: Nielsen Book Data)

• Escape! Code your way out of a paper bag
• Decide! Find the paper bag
• Boom! Create a genetic algorithm
• Swarm! Build a nature-inspired swarm
• Colonize! Discover pathways
• Diffuse! Employ a stochastic model
• Buzz! Converge on one solution
• Alive! Create artificial life
• Dream! Explore CA with GA
• Optimize! Find the best.

• Computer Vision and Machine Learning.- Evolutionary Computation and Genetic Programming.- Multi-Layer Representation for Binary Image Classification.- Evolutionary Deep Learning Using GP with Convolution Operators.- GP with Image Descriptors for Learning Global and Local Features.- GP with Image-Related Operators for Feature Learning.- GP for Simultaneous Feature Learning and Ensemble Learning.- Random Forest-Assisted GP for Feature Learning.- Conclusions and Future Directions.
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This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation. .
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• Foreword / Scott Meyers
• Foreword / John Vlissides
• pt. I. Techniques
• Ch. 1. Policy-Based Class Design
• Ch. 2. Techniques
• Ch. 3. Typelists
• Ch. 4. Small-Object Allocation
• pt. II. Components
• Ch. 5. Generalized Functors
• Ch. 6. Implementing Singletons
• Ch. 7. Smart Pointers
• Ch. 8. Object Factories
• Ch. 9. Abstract Factory
• Ch. 10. Visitor
• Ch. 11. Multimethods. App. Minimalist Multithreading Library.

In Modern C++ Design, Andrei Alexandrescu opens new vistas for C++ programmers. Displaying extraordinary creativity and programming virtuosity, Alexandrescu offers a cutting-edge approach to design that unites design patterns, generic programming, and C++, enabling programmers to achieve expressive, flexible, and highly reusable code. This book introduces the concept of generic components-reusable design templates that produce boilerplate code for compiler consumption-all within C++. Generic components enable an easier and more seamless transition from design to application code, generate code that better expresses the original design intention, and support the reuse of design structures with minimal recoding. The author describes the specific C++ techniques and features that are used in building generic components and goes on to implement industrial strength generic components for real-world applications. Recurring issues that C++ developers face in their day-to-day activity are discussed in depth and implemented in a generic way. These include: Policy-based design for flexibility Partial template specialization Typelists-powerful type manipulation structures Patterns such as Visitor, Singleton, Command, and Factories Multi-method engines For each generic component, the book presents the fundamental problems and design options, and finally implements a generic solution. In addition, an accompanying Web site, http://www.awl.com/cseng/titles/0-201-70431-5, makes the code implementations available for the generic components in the book and provides a free, downloadable C++ library, called Loki, created by the author. Loki provides out-of-the-box functionality for virtually any C++ project. Get a value-added service! Try out all the examples from this book at www.codesaw.com. CodeSaw is a free online learning tool that allows you to experiment with live code from your book right in your browser.
(source: Nielsen Book Data)

• Swarm Intelligence and Its Applications.- Operational Technologies and Networked Multimedia Applications.- Wearable Computing and Intelligent Data Hiding.- Image Processing and Intelligent Applications.- Intelligent Multimedia Tools and Applications.- Technologies for Next-Generation Network Environments.- Recent Progress in Computational Electromagnetic Dynamics.- Future Cyber Security, Privacy and Forensics for Advanced systems.- Data Mining Techniques and its Applications.- Optimization Models in Deep Learning/Machine Learning.
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This book contains selected papers presented at ICGEC 2021, the 14th International Conference on Genetic and Evolutionary Computing, held from October 21-23, 2021 in Jilin City, China. The conference was technically co-sponsored by Springer, Northeast Electric Power University Fujian University of Technology, Shandong University of Science and Technology, and Western Norway University of Applied Sciences. It is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing. And the readers may learn the up-to-date techniques of the mentioned topics, including swarm intelligence, artificial intelligence, information hiding and data mining techniques, which can help them to bring new ideas or apply the designed approaches from the collected papers to their professional jobs.
(source: Nielsen Book Data)