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• 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.
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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. .
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• 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.
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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.
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• 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.
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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.
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• 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.
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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.
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• 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.
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• 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.
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This book gathers papers presented at the 13th International Conference on Genetic and Evolutionary Computing (ICGEC 2019), which was held in Qingdao, China, from 1st to 3rd, November 2019. Since it was established, in 2006, the ICGEC conference series has been devoted to new approaches with a focus on evolutionary computing. Today, it is a forum for the researchers and professionals in all areas of computational intelligence including evolutionary computing, machine learning, soft computing, data mining, multimedia and signal processing, swarm intelligence and security. The book appeals to policymakers, academics, educators, researchers in pedagogy and learning theory, school teachers, and other professionals in the learning industry, and further and continuing education. .
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• 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.
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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.
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• 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.
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• Intro; Preface; Organization; Contents; Full Presentations; One-Class Classification for Anomaly Detection with Kernel Density Estimation and Genetic Programming; 1 Introduction; 2 Related Work; 3 Preliminaries; 3.1 Genetic Programming; 3.2 Kernel Density Estimation; 4 Proposed Approach; 4.1 Description of Method; 4.2 Generating Artificial Data; 5 Experiments; 5.1 Datasets; 5.2 Experimental Settings; 6 Results and Discussion; 7 Conclusion and Further Work; References; Evolutionary Approximation of Edge Detection Circuits; 1 Introduction; 2 Relevant Work; 2.1 Edge Detectors

• 2.2 Evolutionary Computing in Edge Detector Design2.3 Approximate Computing in Image Processing; 2.4 Evolutionary Circuit Design; 3 Adopting CGP for Circuit Approximation; 3.1 Cartesian Genetic Programming; 3.2 Resources-Oriented Approximation; 4 Experimental Results; 4.1 Evolutionary Approximation of Adders; 4.2 Approximation of Sobel Edge Detector; 4.3 Approximation of Evolved Edge Operator; 5 Conclusions; References; On the Impact of Class Imbalance in GP Streaming Classification with Label Budgets; 1 Introduction; 2 Related Work; 3 Methodology; 3.1 Anytime Operation; 3.2 Archiving Policy

• 3.3 Sampling Policy4 Experimental Methodology; 4.1 Datasets; 4.2 Class-Wise Detection Rate; 4.3 Parameters; 5 Results; 5.1 Single Generation Performance; 5.2 Multi-generation Performance; 5.3 Overall Detection Rates; 6 Conclusion; References; Genetic Programming for Region Detection, Feature Extraction, Feature Construction and Classification in Image Data; 1 Introduction; 2 Background; 2.1 Related Work; 3 The Proposed Method; 3.1 GP Program Representation; 3.2 Outline of the HoG Function; 3.3 The Fitness Function; 4 Experiment Design; 4.1 Datasets; 4.2 Training and Test Sets

• 4.3 Baseline Methods4.4 Generating SURF Keypoints; 4.5 Evolutionary Parameters; 5 Results and Discussion; 5.1 Compared to the 2TGP Approach; 5.2 Compared to the Baselines; 6 Further Analysis; 6.1 Example Program 1; 6.2 Example Program 2; 6.3 Example Program 3; 7 Conclusions; References; Surrogate Fitness via Factorization of Interaction Matrix; 1 Introduction; 2 Background; 3 Factorization of Interaction Matrix; 4 The SFIMX Algorithm; 5 Related Work; 6 Experimental Verification; 7 Conclusions and Future Work; References

• Scheduling in Heterogeneous Networks Using Grammar-Based Genetic Programming1 Introduction; 2 Problem Definition; 3 Previous Work; 4 Simulation Environment; 4.1 Generating Inputs; 4.2 Calculating Fitness; 5 Experiments; 6 Results and Discussion; 6.1 Terminal Utilisation; 6.2 Subframe Utilisation; 6.3 Benchmarking; 7 Future Work and Conclusions; References; On the Analysis of Simple Genetic Programming for Evolving Boolean Functions; 1 Introduction; 2 Preliminaries; 3 Analysis for Complete Training Sets; 3.1 Analysis for ANDn with Complete Training Sets

This book constitutes the refereed proceedings of the 19th European Conference on Genetic Programming, EuroGP 2016, held in Porto, Portugal, in March/April 2016 co-located with the Evo*2016 events: EvoCOP, EvoMUSART, and EvoApplications. The 11 revised full papers presented together with 8 poster papers were carefully reviewed and selected from 36 submissions. The wide range of topics in this volume reflects the current state of research in the field. Thus, we see topics as diverse as semantic methods, recursive programs, grammatical methods, coevolution, Cartesian GP, feature selection, metaheuristics, evolvability, and fitness predictors; and applications including image processing, one-class classification, SQL injection attacks, numerical modelling, streaming data classification, creation and optimisation of circuits, multi-class classification, scheduling in manufacturing and wireless networks.

• 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.
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• Nature Inspired Constrained Optimization.- Recent Advances on Evolutionary Optimization Technologies.- Wearable Computing and Intelligent Data Hiding.- Image Processing and Intelligent Applications.- Intelligent Multimedia Tools and Applications.- Technologies for Next-Generation Network Environments.
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This volume of Advances in Intelligent Systems and Computing contains accepted papers presented at ICGEC 2014, the 8th International Conference on Genetic and Evolutionary Computing. The conference this year was technically co-sponsored by Nanchang Institute of Technology in China, Kaohsiung University of Applied Science in Taiwan, and VSB-Technical University of Ostrava. ICGEC 2014 is held from 18-20 October 2014 in Nanchang, China. Nanchang is one of is the capital of Jiangxi Province in southeastern China, located in the north-central portion of the province. As it is bounded on the west by the Jiuling Mountains, and on the east by Poyang Lake, it is famous for its scenery, rich history and cultural sites. Because of its central location relative to the Yangtze and Pearl River Delta regions, it is a major railroad hub in Southern China. The conference is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing.
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