4.7 Article

Explainable Artificial Intelligence by Genetic Programming: A Survey

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

Genetic Programming With Niching for Uncertain Capacitated Arc Routing Problem

Shaolin Wang et al.

Summary: This article proposes a novel genetic programming approach to solve the uncertain capacitated arc routing problem. By simplifying routing policies using a niching technique and storing the simplified policies in an external archive, the evolved routing policies are more effective and simpler.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2022)

Article Computer Science, Information Systems

SECURE-GEGELATI Always-On Intrusion Detection through GEGELATI Lightweight Tangled Program Graphs

Nicolas Sourbier et al.

Summary: This paper proposes a novel anomaly-based Network Intrusion Detection System (NIDS) called Secure-Gegelati, which is based on Tangled Program Graph (TPG) machine learning. The study evaluates the performance of Secure-Gegelati as a continuously learning, real-time, and low energy NIDS on an embedded network probe. The results show that Secure-GEGELATI achieves higher energy efficiency compared to a traditional Random Forests (RF)-based Intrusion Detection System (IDS), while detecting the majority of intrusions in real-time.

JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY (2022)

Article Computer Science, Artificial Intelligence

Simplification of genetic programs: a literature survey

Noman Javed et al.

Summary: Genetic programming suffers from the problem of excessive growth in individuals' sizes, which reduces its ability to explore complex search spaces efficiently. This paper focuses on reviewing the literature from an explainability perspective and how simplification can make GP models more explainable by reducing their sizes. Researchers have proposed various simplification techniques, and this paper organizes the literature to identify their strengths and weaknesses, as well as emerging trends and areas for future exploration.

DATA MINING AND KNOWLEDGE DISCOVERY (2022)

Article Computer Science, Artificial Intelligence

Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set

Guilherme Seidyo Imai Aldeia et al.

Summary: The interpretability of machine learning models is crucial in many situations, and this paper proposes a benchmark scheme to evaluate explanatory methods for regression models, focusing on symbolic regression models. The experiments show that symbolic regression models can be a compelling alternative to white-box and black-box models, providing accurate models with appropriate explanations. The most robust explanation models were found to be Partial Effects and SHAP, with Integrated Gradients being less stable with tree-based models.

GENETIC PROGRAMMING AND EVOLVABLE MACHINES (2022)

Article Environmental Sciences

An Object-Based Genetic Programming Approach for Cropland Field Extraction

Caiyun Wen et al.

Summary: This paper proposes a new object-based Genetic Programming (GP) approach to extract cropland fields. The approach combines genetic programming with multiresolution segmentation technique to extract spectral, shape, and texture features of the fields and automatically evolves the optimal classifier. The results show that the proposed approach achieves high accuracy in areas with different landscape complexities and outperforms commonly used classifiers.

REMOTE SENSING (2022)

Article Automation & Control Systems

Rademacher Complexity for Enhancing the Generalization of Genetic Programming for Symbolic Regression

Qi Chen et al.

Summary: Model complexity is closely related to the generalization ability and interpretability of learned models. This work proposes a novel complexity measure based on Rademacher complexity for genetic programming (GP) in symbolic regression. The proposed method, which minimizes training error and Rademacher complexity, outperforms standard GP in generalization performance and generates models closer to the target models with better interpretability.

IEEE TRANSACTIONS ON CYBERNETICS (2022)

Article Computer Science, Artificial Intelligence

Genetic programming for automatic skin cancer image classification

Qurrat Ul Ain et al.

Summary: This study analyzes GP-based approaches to skin image classification, which improve the performance of machine learning classification algorithms by constructing features, thereby enhancing diagnostic efficiency and assisting dermatologists in diagnosis.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

Article Computer Science, Artificial Intelligence

Genetic Programming for Manifold Learning: Preserving Local Topology

Andrew Lensen et al.

Summary: Manifold learning (MaL) methods are important tools for handling large-scale datasets, and genetic programming has emerged as a promising approach to MaL. This research proposes a new genetic programming-based method that preserves local topology, leading to improved performance in manifold learning tasks.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2022)

Proceedings Paper Computer Science, Artificial Intelligence

Evolving Explainable Rule Sets

Hormoz Shahrzad et al.

Summary: Most AI systems are like black boxes, lacking explainability and trustworthiness. In this work, the researchers propose a method that uses transparent models and ordinary logic to generate explainable rule-sets, which can be applied in sensitive domains and have important properties such as bias detection, knowledge discovery, and modifiability.

PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022 (2022)

Proceedings Paper Computer Science, Artificial Intelligence

Imbalanced Classification with TPG Genetic Programming: Impact of Problem Imbalance and Selection Mechanisms

Nicolas Sourbier et al.

Summary: This paper investigates the impact of imbalanced data on the performance of a TPG classifier and proposes mitigation methods using adapted GP selection phases, showing significant improvement in classifier performance.

PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022 (2022)

Proceedings Paper Computer Science, Cybernetics

Local Ranking Explanation for Genetic Programming Evolved Routing Policies for Uncertain Capacitated Arc Routing Problems

Shaolin Wang et al.

Summary: This paper proposes a Local Ranking Explanation (LRE) method for explaining GP-evolved routing policies for the Uncertain Capacitated Arc Routing Problem (UCARP). Experimental results show that this method can provide more interpretable linear models to explain the routing policies in most decision situations.

PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'22) (2022)

Article Computer Science, Artificial Intelligence

Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions

M. Virgolin et al.

Summary: The study investigates the role of Linkage Learning performed by the model-based evolutionary algorithm framework GOMEA in Symbolic Regression, highlighting the impact of non-uniformity in genotype distribution and proposing methods to improve learning. The experiments on real-world datasets show that the new Linkage Learning method outperforms the standard one, with GOMEA demonstrating competitive performance with tuned decision trees in evolving small solutions for Symbolic Regression.

EVOLUTIONARY COMPUTATION (2021)

Review Computer Science, Artificial Intelligence

A historical perspective of explainable Artificial Intelligence

Roberto Confalonieri et al.

Summary: Explainability in Artificial Intelligence has become an active area of research due to the need for conveying safety and trust to users in automated decision-making. While this topic has recently gained attention, its origins can be traced back several decades to the development of expert systems. Research on explainability has been explored in various fields over different periods of AI history, leading to a historical perspective presented in this article. The criteria for explanations proposed in the conclusion are believed to be crucial for the development of human-understandable explainable systems.

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2021)

Article Computer Science, Artificial Intelligence

Cartesian genetic programming for diagnosis of Parkinson disease through handwriting analysis: Performance vs. interpretability issues

A. Parziale et al.

Summary: This study compared different machine learning techniques to automatically identify Parkinson's disease patients through handwriting and drawing samples. White-box methods showed higher accuracy and explicit classification models, helping to design non-invasive, inexpensive, and easy to administer diagnostic protocols.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2021)

Review Physics, Multidisciplinary

Explainable AI: A Review of Machine Learning Interpretability Methods

Pantelis Linardatos et al.

Summary: Recent advances in artificial intelligence have led to widespread industrial adoption, with machine learning systems demonstrating superhuman performance. However, the complexity of these systems has made them difficult to explain, hindering their application in sensitive domains. Therefore, there is a renewed interest in the field of explainable artificial intelligence.

ENTROPY (2021)

Article Automation & Control Systems

People-Centric Evolutionary System for Dynamic Production Scheduling

Su Nguyen et al.

Summary: A novel people-centric evolutionary system for dynamic production scheduling has been developed with new techniques and models to enhance the efficiency of genetic programming, outperforming existing algorithms in dynamic flexible job shop scheduling experiments.

IEEE TRANSACTIONS ON CYBERNETICS (2021)

Article Automation & Control Systems

Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization

Andrew Lensen et al.

Summary: The article introduces a genetic programming approach called GP-tSNE for evolving interpretable mappings from datasets to high-quality visualizations. A multiobjective method is designed to produce a variety of visualizations in a single run, which offers different tradeoffs between visual quality and model complexity. Testing against baseline methods on various datasets demonstrates the clear potential and benefits of GP-tSNE for deeper insight into data.

IEEE TRANSACTIONS ON CYBERNETICS (2021)

Article Computer Science, Artificial Intelligence

Multi-Objective Memetic Algorithms with Tree-Based Genetic Programming and Local Search for Symbolic Regression

Jiayu Liang et al.

Summary: Symbolic regression involves searching for mathematical expressions that best fit given datasets. Genetic programming is commonly used for regression, but can lead to bloating and poor generalization. This study aims to address these issues by using a multi-objective technique and incorporating mutation-based local search operators. The proposed methods outperform GP-based methods, with smaller evolved solutions and improved search ability. Among the proposed methods, MOMA_MR performs best in testing accuracy.

NEURAL PROCESSING LETTERS (2021)

Article Automation & Control Systems

Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling

Fangfang Zhang et al.

Summary: A novel two-stage GPHH framework with feature selection is designed in this article to automatically evolve scheduling heuristics in DFJSS, and individual adaptation strategies are proposed to utilize information. Results show that the proposed algorithm can successfully achieve more interpretable scheduling heuristics with fewer unique features and smaller sizes, and reach comparable scheduling heuristic quality with much shorter training time than traditional algorithms.

IEEE TRANSACTIONS ON CYBERNETICS (2021)

Article Computer Science, Artificial Intelligence

Soft target and functional complexity reduction: A hybrid regularization method for genetic programming

Leonardo Vanneschi et al.

Summary: This study applies soft target regularization and introduces a new measure of functional complexity to genetic programming, showing that they can reduce overfitting but not eliminate it completely. The integration of these two strategies into a novel hybrid genetic programming system is able to completely eliminate overfitting. The research also provides experimental evidence that the size of genetic programming models has no correlation with their generalization ability.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Article Computer Science, Artificial Intelligence

Multi-View Feature Construction Using Genetic Programming for Rolling Bearing Fault Diagnosis

Bo Peng et al.

Summary: A new diagnosis approach named MFCGPE is proposed to automatically construct high-level features and build an effective ensemble for identifying different fault types. The approach achieves higher diagnostic accuracy than all compared methods on three bearing datasets with a small number of training samples.

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Measuring Feature Importance of Symbolic Regression Models Using Partial Effects

Guilherme Seidyo Imai Aldeia et al.

Summary: In explainable AI, one aspect of a prediction's explanation is to measure the importance of each predictor, which can be calculated using Partial Effect. Symbolic Regression is a method for regression that returns an analytical model approximating the input data, often associated with interpretability but with limited research on this property.

PROCEEDINGS OF THE 2021 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'21) (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Using Genetic Programming to Find Functional Mappings for UMAP Embeddings

Finn Schofield et al.

Summary: This study proposes utilizing UMAP in combination with genetic programming for manifold learning to produce results with functional mappings, showing promising performance compared to UMAP. Experimental results reinforce the value of this method and provide further analysis of the behavior of both algorithms.

2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021) (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Evolving Simple Solutions to the CIFAR-10 Benchmark using Tangled Program Graphs

Robert J. Smith et al.

Summary: The goal of the CIFAR-10 benchmark is to discover lightweight and accurate solutions, addressing practical issues like cardinality, multi-class classification, and diversity maintenance. Solutions are demonstrated using a data subset for exemplar pools, tangled program graph approach, and modified mutation operator to ensure class labels do not 'die out' during evolution. The resulting solutions are significantly more accurate than AutoML and comparable to unsupervised feature discovery, with TPG solutions being simpler.

2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021) (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Genetic Programming for Symbolic Regression: A Study on Fish Weight Prediction

Yunhan Yang et al.

Summary: The study builds a new model for predicting fish weight using Genetic Programming (GP) for symbolic regression, which outperforms or performs equally well as several baseline methods. Furthermore, GP has the ability to select different features to improve prediction performance.

2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021) (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Genetic Programming with Algebraic Simplification for Dynamic Job Shop Scheduling

Sai Panda et al.

Summary: The study develops algebraic simplification operators to evolve simpler but effective dispatching rules in Genetic Programming. Results show that using algebraic simplification can slightly reduce program size without sacrificing test performance. However, limitations of pure algebraic simplification for evolving DJSS dispatching rules in GP have been discovered through deep analysis.

2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021) (2021)

Article Computer Science, Artificial Intelligence

Generating Knowledge-Guided Discriminative Features Using Genetic Programming for Melanoma Detection

Qurrat Ul Ain et al.

Summary: A novel skin image classification method using local binary pattern and color variation features with multi-tree genetic programming was developed. The proposed method significantly outperformed other commonly used classification algorithms in terms of performance. This method can assist dermatologists in real-time identification of specific types of skin cancer.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2021)

Article Computer Science, Artificial Intelligence

Generating trading rules on US Stock Market using strongly typed genetic programming

Kevin Michell et al.

SOFT COMPUTING (2020)

Article Computer Science, Artificial Intelligence

Cartesian genetic programming: its status and future

Julian Francis Miller

GENETIC PROGRAMMING AND EVOLVABLE MACHINES (2020)

Article Computer Science, Artificial Intelligence

Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis

Andrew Lensen et al.

EVOLUTIONARY COMPUTATION (2020)

Review Computer Science, Hardware & Architecture

Techniques for Interpretable Machine Learning

Mengnan Du et al.

COMMUNICATIONS OF THE ACM (2020)

Article Computer Science, Artificial Intelligence

Multi-objective genetic programming for manifold learning: balancing quality and dimensionality

Andrew Lensen et al.

GENETIC PROGRAMMING AND EVOLVABLE MACHINES (2020)

Article Computer Science, Theory & Methods

Destructiveness of lexicographic parsimony pressure and alleviation by a concatenation crossover in genetic programming

Timo Koetzing et al.

THEORETICAL COMPUTER SCIENCE (2020)

Article Computer Science, Artificial Intelligence

An Effective Feature Learning Approach Using Genetic Programming With Image Descriptors for Image Classification [Research Frontier]

Ying Bi et al.

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2020)

Article Computer Science, Artificial Intelligence

On explaining machine learning models by evolving crucial and compact features

Marco Virgolin et al.

SWARM AND EVOLUTIONARY COMPUTATION (2020)

Article Computer Science, Interdisciplinary Applications

Dimensionally Aware Multi-Objective Genetic Programming for Automatic Crowd Behavior Modeling

D. Li et al.

ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION (2020)

Article Computer Science, Artificial Intelligence

Preference-driven Pareto front exploitation for bloat control in genetic programming

Jiayu Liang et al.

APPLIED SOFT COMPUTING (2020)

Article Computer Science, Artificial Intelligence

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Alejandro Barredo Arrieta et al.

INFORMATION FUSION (2020)

Proceedings Paper Automation & Control Systems

Interpretable Control by Reinforcement Learning

Daniel Hein et al.

IFAC PAPERSONLINE (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Adaptive Weighted Splines - A New Representation to Genetic Programming for Symbolic Regression

Christian Raymond et al.

GECCO'20: PROCEEDINGS OF THE 2020 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (2020)

Article Computer Science, Artificial Intelligence

Semantic approximation for reducing code bloat in Genetic Programming

Quang Uy Nguyen et al.

SWARM AND EVOLUTIONARY COMPUTATION (2020)

Article Computer Science, Artificial Intelligence

Multi-view Genetic Programming Learning to Obtain Interpretable Rule-Based Classifiers for Semi-supervised Contexts. Lessons Learnt

Carlos Garcia-Martinez et al.

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS (2020)

Article Mathematics, Applied

Machine learning control - explainable and analyzable methods

Markus Quade et al.

PHYSICA D-NONLINEAR PHENOMENA (2020)

Article Computer Science, Artificial Intelligence

Pruning of genetic programming trees using permutation tests

Peter Rockett

EVOLUTIONARY INTELLIGENCE (2020)

Article Computer Science, Artificial Intelligence

Explanation in artificial intelligence: Insights from the social sciences

Tim Miller

ARTIFICIAL INTELLIGENCE (2019)

Article Computer Science, Artificial Intelligence

Local search in speciation-based bloat control for genetic programming

Perla Juarez-Smith et al.

GENETIC PROGRAMMING AND EVOLVABLE MACHINES (2019)

Article Multidisciplinary Sciences

Unmasking Clever Hans predictors and assessing what machines really learn

Sebastian Lapuschkin et al.

NATURE COMMUNICATIONS (2019)

Review Multidisciplinary Sciences

A survey on evolutionary machine learning

Harith Al-Sahaf et al.

JOURNAL OF THE ROYAL SOCIETY OF NEW ZEALAND (2019)

Article Computer Science, Artificial Intelligence

Structural Risk Minimization-Driven Genetic Programming for Enhancing Generalization in Symbolic Regression

Qi Chen et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2019)

Article Computer Science, Artificial Intelligence

Genetic programming for multiple-feature construction on high-dimensional classification

Binh Tran et al.

PATTERN RECOGNITION (2019)

Article Multidisciplinary Sciences

Definitions, methods, and applications in interpretable machine learning

W. James Murdoch et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)

Article Computer Science, Artificial Intelligence

A Grammar-Guided Genetic Programing Algorithm for Associative Classification in Big Data

F. Padillo et al.

COGNITIVE COMPUTATION (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Novel Ensemble Genetic Programming Hyper-Heuristics for Uncertain Capacitated Arc Routing Problem

Shaolin Wang et al.

PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'19) (2019)

Proceedings Paper Computer Science, Artificial Intelligence

A Two-stage Genetic Programming Hyper-heuristic Approach with Feature Selection for Dynamic Flexible Job Shop Scheduling

Fangfang Zhang et al.

PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'19) (2019)

Proceedings Paper Computer Science, Artificial Intelligence

What's inside the black-box? A genetic programming method for interpreting complex machine learning models

Benjamin P. Evans et al.

PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'19) (2019)

Article Computer Science, Artificial Intelligence

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Cynthia Rudin

NATURE MACHINE INTELLIGENCE (2019)

Proceedings Paper Engineering, Electrical & Electronic

Genetic Programming with Rademacher Complexity for Symbolic Regression

Christian Raymond et al.

2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) (2019)

Proceedings Paper Engineering, Electrical & Electronic

Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics

Noelie Cherrier et al.

2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) (2019)

Article Engineering, Mechanical

Synchronization control of oscillator networks using symbolic regression

Julien Gout et al.

NONLINEAR DYNAMICS (2018)

Article Computer Science, Hardware & Architecture

The Mythos of Model Interpretability

Zachary C. Lipton

COMMUNICATIONS OF THE ACM (2018)

Article Automation & Control Systems

Interpretable policies for reinforcement learning by genetic programming

Daniel Hein et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2018)

Editorial Material Computer Science, Artificial Intelligence

Guest editorial: Special issue on genetic programming, evolutionary computation and visualization

Nadia Boukhelifa et al.

GENETIC PROGRAMMING AND EVOLVABLE MACHINES (2018)

Article Computer Science, Artificial Intelligence

Unveiling evolutionary algorithm representation with DU maps

Eric Medvet et al.

GENETIC PROGRAMMING AND EVOLVABLE MACHINES (2018)

Article Computer Science, Information Systems

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

Amina Adadi et al.

IEEE ACCESS (2018)

Article Computer Science, Artificial Intelligence

Visualizing the Evolution of Computer Programs or Genetic Programming

Su Nguyen et al.

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2018)

Proceedings Paper Computer Science, Artificial Intelligence

An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming

Ying Bi et al.

APPLICATIONS OF EVOLUTIONARY COMPUTATION, EVOAPPLICATIONS 2018 (2018)

Proceedings Paper Computer Science, Artificial Intelligence

A Study on Multimodal Genetic Programming Introducing Program Simplification

Kei Murano et al.

2018 JOINT 10TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS (SCIS) AND 19TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (ISIS) (2018)

Article Computer Science, Software Engineering

Application-Specific Tone Mapping Via Genetic Programming

K. Debattista

COMPUTER GRAPHICS FORUM (2018)

Proceedings Paper Computer Science, Theory & Methods

Semantics Based Substituting Technique for Reducing Code Bloat in Genetic Programming

Thi Huong Chu et al.

PROCEEDINGS OF THE NINTH INTERNATIONAL SYMPOSIUM ON INFORMATION AND COMMUNICATION TECHNOLOGY (SOICT 2018) (2018)

Article Computer Science, Artificial Intelligence

Multi-objective genetic programming for feature extraction and data visualization

Alberto Cano et al.

SOFT COMPUTING (2017)

Article Geochemistry & Geophysics

Remote Sensing Image Classification Using Genetic-Programming-Based Time Series Similarity Functions

Alexandre E. Almeida et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2017)

Article Computer Science, Artificial Intelligence

Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression

Qi Chen et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2017)

Article Computer Science, Artificial Intelligence

An Efficient Feature Selection Algorithm for Evolving Job Shop Scheduling Rules With Genetic Programming

Yi Mei et al.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2017)

Review Computer Science, Artificial Intelligence

Genetic programming for production scheduling: a survey with a unified framework

Su Nguyen et al.

COMPLEX & INTELLIGENT SYSTEMS (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Multi-Task Learning in Atari Video Games with Emergent Tangled Program Graphs

Stephen Kelly et al.

PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'17) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Improving Generalization of Evolved Programs through Automatic Simplification

Thomas Helmuth et al.

PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'17) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Sensitivity-Like Analysis for Feature Selection in Genetic Programming

Grant Dick

PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'17) (2017)

Article Computer Science, Artificial Intelligence

Genetic programming for feature construction and selection in classification on high-dimensional data

Binh Tran et al.

MEMETIC COMPUTING (2016)

Article Automation & Control Systems

Inference of compact nonlinear dynamic models by epigenetic local search

William La Cava et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2016)

Review Computer Science, Artificial Intelligence

Automated Design of Production Scheduling Heuristics: A Review

Juergen Branke et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2016)

Article Computer Science, Information Systems

neat Genetic Programming: Controlling bloat naturally

Leonardo Trujillo et al.

INFORMATION SCIENCES (2016)

Proceedings Paper Computer Science, Theory & Methods

Feature Selection in Evolving Job Shop Dispatching Rules with Genetic Programming

Yi Mei et al.

GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (2016)

Proceedings Paper Computer Science, Theory & Methods

Improving Generalisation of Genetic Programming for Symbolic Regression with Structural Risk Minimisation

Qi Chen et al.

GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (2016)

Article Physics, Fluids & Plasmas

Prediction of dynamical systems by symbolic regression

Markus Quade et al.

PHYSICAL REVIEW E (2016)

Article Computer Science, Artificial Intelligence

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

Somayeh Mousavi et al.

APPLIED SOFT COMPUTING (2015)

Article Computer Science, Artificial Intelligence

GPFIS-CLASS: A Genetic Fuzzy System based on Genetic Programming for classification problems

Adriano S. Koshiyama et al.

APPLIED SOFT COMPUTING (2015)

Article Computer Science, Artificial Intelligence

Tikhonov Regularization as a Complexity Measure in Multiobjective Genetic Programming

Ji Ni et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2015)

Proceedings Paper Computer Science, Artificial Intelligence

AGGE: A Novel Method to Automatically Generate Rule Induction Classifiers Using Grammatical Evolution

Romaissaa Mazouni et al.

INTELLIGENT DISTRIBUTED COMPUTING VIII (2015)

Article Computer Science, Artificial Intelligence

Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programming

Soha Ahmed et al.

CONNECTION SCIENCE (2014)

Article Computer Science, Artificial Intelligence

Feature Learning for Image Classification via Multiobjective Genetic Programming

Ling Shao et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2014)

Proceedings Paper Computer Science, Artificial Intelligence

Multiple Feature Construction for Effective Biomarker Identification and Classification using Genetic Programming

Soha Ahmed et al.

GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (2014)

Article Computer Science, Artificial Intelligence

A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem

Su Nguyen et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2013)

Article Computer Science, Artificial Intelligence

Evolving Diverse Ensembles Using Genetic Programming for Classification With Unbalanced Data

Urvesh Bhowan et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2013)

Article Mathematics, Applied

Modeling global temperature changes with genetic programming

Karolina Stanislawska et al.

COMPUTERS & MATHEMATICS WITH APPLICATIONS (2012)

Article Computer Science, Artificial Intelligence

Operator equalisation for bloat free genetic programming and a survey of bloat control methods

Sara Silva et al.

GENETIC PROGRAMMING AND EVOLVABLE MACHINES (2012)

Proceedings Paper Computer Science, Theory & Methods

Computational Complexity Analysis of Multi-Objective Genetic Programming

Frank Neumann

PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (2012)

Article Automation & Control Systems

Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation

P. Barmpalexis et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2011)

Article Computer Science, Artificial Intelligence

Semantically-based crossover in genetic programming: application to real-valued symbolic regression

Nguyen Quang Uy et al.

GENETIC PROGRAMMING AND EVOLVABLE MACHINES (2011)

Article Computer Science, Artificial Intelligence

Bloat Control Operators and Diversity in Genetic Programming: A Comparative Study

E. Alfaro-Cid et al.

EVOLUTIONARY COMPUTATION (2010)

Article Computer Science, Artificial Intelligence

The identification and exploitation of dormancy in genetic programming

David Jackson

GENETIC PROGRAMMING AND EVOLVABLE MACHINES (2010)

Article Computer Science, Artificial Intelligence

Grammar-based Genetic Programming: a survey

Robert I. McKay et al.

GENETIC PROGRAMMING AND EVOLVABLE MACHINES (2010)

Article Computer Science, Artificial Intelligence

Implicitly Controlling Bloat in Genetic Programming

Peter A. Whigham et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2010)

Review Computer Science, Artificial Intelligence

A Survey on the Application of Genetic Programming to Classification

Pedro G. Espejo et al.

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS (2010)

Article Computer Science, Theory & Methods

Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories

Sara Silva et al.

Genetic Programming and Evolvable Machines (2009)

Article Computer Science, Hardware & Architecture

Template-Free Symbolic Performance Modeling of Analog Circuits via Canonical-Form Functions and Genetic Programming

Trent McConaghy et al.

IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS (2009)

Article Computer Science, Artificial Intelligence

Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming

Ekaterina J. Vladislavleva et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2009)

Article Computer Science, Artificial Intelligence

Evolving rule induction algorithms with multi-objective grammar-based genetic programming

Gisele L. Pappa et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2009)

Article Multidisciplinary Sciences

Distilling Free-Form Natural Laws from Experimental Data

Michael Schmidt et al.

SCIENCE (2009)

Article Computer Science, Artificial Intelligence

Numerical simplification for bloat control and analysis of building blocks in genetic programming

David Kinzett et al.

EVOLUTIONARY INTELLIGENCE (2009)

Article Computer Science, Artificial Intelligence

Classifier design with feature selection and feature extraction using layered genetic programming

Jung-Yi Lin et al.

EXPERT SYSTEMS WITH APPLICATIONS (2008)

Article Computer Science, Interdisciplinary Applications

Development of pipe deterioration models for water distribution systems using EPR

L. Berardi et al.

JOURNAL OF HYDROINFORMATICS (2008)

Article Computer Science, Artificial Intelligence

EXPLICITLY SIMPLIFYING EVOLVED GENETIC PROGRAMS DURING EVOLUTION

Mengjie Zhang et al.

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS (2008)

Article Computer Science, Artificial Intelligence

Designing a classifier by a layered multi-population genetic programming approach

Jung-Yi Lin et al.

PATTERN RECOGNITION (2007)

Article Computer Science, Artificial Intelligence

A multi-objective genetic programming approach to developing Pareto optimal decision trees

Huimin Zhao

DECISION SUPPORT SYSTEMS (2007)

Article Computer Science, Artificial Intelligence

A comparison of bloat control methods for genetic programming

Sean Luke et al.

EVOLUTIONARY COMPUTATION (2006)

Article Computer Science, Artificial Intelligence

Diversity in genetic programming: An analysis of measures and correlation with fitness

EK Burke et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2004)

Article Automation & Control Systems

Dynamic page based crossover in linear genetic programming

MI Heywood et al.

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS (2002)

Article Computer Science, Artificial Intelligence

Grammatical evolution

M O'Neill et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2001)