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IEEE INTELLIGENT SYSTEMS & THEIR APPLICATIONS 2000年第3期15卷 74-74页

作者： Hirsh, H Rutgers State Univ Piscataway NJ 08855 USA

The paper presents essays on genetic programming which involve topics such as: the artificial evolution of computer code, human-competitive machine intelligence by means of genetic programming, GP as automatic program... 详细信息

关键词： genetic programming Biology computing Evolution (biology) Evolutionary computation Quantum computing Computer science Biological neural networks Neurons Problem-solving Data structures

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genetic programming Hyper Heuristic With Elitist Mutation for Integrated Order Batching and Picker Routing Problem

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第2期29卷 346-359页

作者： Wang, Yuquan Xie, Naiming Chen, Nanlei Ma, Hui Chen, Gang Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210016 Peoples R China Hohai Univ Business Sch Nanjing 211100 Peoples R China Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington 6012 New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6012 New Zealand

Integrated order batching and picker routing (IOBPR) is a complex combinatorial optimization problem in real-world intelligent manufacturing systems. Heuristics are often used for solving such complex scheduling problems. Manually designing scheduling heuristics suffer from two limitations: 1) few problem features can be taken into account and 2) the design process is time consuming. genetic programming hyper heuristic (GPHH) approaches have been proposed on many scheduling problems to automatically evolve effective heuristics. However, existing GPHH approaches are often problem specific and requires careful design of problem specific terminal sets and evolution operators. The aim of this work is to develop a GPHH approach to evolve heuristics for the IOBPR problem. In particular, we propose a novel terminal set (NT) with three types of terminals, and a GPHH with elitist mutation (GPHH-EM) algorithm. Extensive experiments demonstrate that the heuristics evolved by GPHH-EM can significantly outperform other state-of-the-art competing algorithms designed by human experts. Further analysis indicates that the three types of terminals effectively complement to improve evolved heuristics for decision making. Furthermore, the newly developed elitist mutation operator expedites the evolutionary process for GPHH to find high-quality heuristics.

关键词： Routing Metaheuristics Evolutionary computation genetic programming Search problems Layout Processor scheduling Job shop scheduling Heuristic algorithms genetic algorithms Elitist mutation (EM) genetic programming hyper heuristic (GPHH) integrated order batching and picker routing (IOBPR) problem

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Bagging-based ensemble classifiers using multi-objective genetic programming

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MEMETIC COMPUTING 2025年第2期17卷 1-17页

作者： Zheng, Yang Zhang, Fan Gao, Xiaoying Ma, Jianbin Hebei Agr Univ Coll Informat Sci & Technol Baoding 071001 Peoples R China Hebei Agr Univ Hebei Key Lab Agr Big Data Baoding 071001 Peoples R China Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

As an effective evolutionary computation algorithm, genetic programming (GP) can be designed as effective classifiers due to its flexible representation method. However, the classification performance of GP classifiers can be degraded due to imbalanced data and weak generalization ability. Precision-recall curve (PRC) has been proven to be an effective evaluation metric for dealing with imbalanced data. However, PRC may result in classifiers with the same PRC value being completely different classifiers. Moreover, controlling the complexity of GP individuals can improve their generalization ability. Therefore, in this paper, multi-objective GP (MOGP) is used to optimize three objectives including recall, precision and model complexity to reduce the impact of imbalanced data and improve the generalization of GP individuals. MOGP-based ensemble classifier construction methods can improve the generalization ability of classification models. However, this strategy needs to address the issues of how to improve the diversity of GP solutions and select optimal solutions from Pareto fronts. Therefore, in this paper, a bagging-based ensemble classifier construction method is proposed to improve the generalization of GP classifiers, which uses non-repeated sampling to generate multiple training subsets and runs MOGP multiple times on these training subsets to construct ensembles. Experiments on ten datasets show that our MOGP-based classifier construction method can achieve better classification performance than single-objective GP classifier construction methods, and our bagging-based ensemble classifier construction methods can further improve the classification performance compared to only using MOGP. Comparisons with six state-of-the-art GP classifier construction methods and six traditional machine learning algorithms show that our proposed approach can achieve significantly better classification performance in most cases.

关键词： genetic programming Multi-objective Ensemble Classification Bagging

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Evolve ensemble rules automatically for the block spatial scheduling under dynamic environments via surrogate-assisted cooperative evolution genetic programming

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年第6期63卷 2010-2037页

作者： Li, Lubo Zhang, Haohua Bai, Sijun North China Univ Water Resources & Elect Power Inst Management & Econ Zhengzhou Peoples R China Northwestern Polytech Univ Sch Management Xian 710129 Shaanxi Peoples R China

The spatial scheduling problem is a crucial investigated problem in operations research and is widely used in shipbuilding, assembly line production and engineering projects. In this paper, we introduce a new block spatial scheduling problem (BSSP) by considering regular resources (manpower and equipment) and dynamic environments. Then, a surrogate-assisted cooperative evolution genetic programming (SCE-GP) is designed to address the BSSP. For the developed algorithm, we firstly propose a new surrogate model by considering the problem surrogate and fitness function surrogate simultaneously, and compare it with the existing models that consider only the fitness function surrogate or problem surrogate under different uncertain environments. Secondly, the cooperative evolution mechanism and random forest technique are embedded in the algorithm to improve its performance. More importantly, we compare different methods for selecting promising individuals. In addition, the design-of-experiment (DOE) approach is utilised to explore the effect of parameter settings. Finally, the performance of SCE-GP with different surrogate models is investigated on our configured data sets based on the benchmark instances of the PSPLIB library. At the same time, we verify the effectiveness of the SCE-GP under different surrogate models and uncertain environments, the performance of the cooperative evolution mechanism, random forest technique and selected method for promising individuals through extensive numerical experiments is also investigated. The results show that the SCE-GP is more excellent than traditional heuristic priority rules (PRs), but different surrogate models yield different results in different uncertain environments.

关键词： Block spatial scheduling priority rule genetic programming cooperative evolution surrogate model uncertain environment

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MOGGP: A novel multi objective geometric genetic programming model for drought forecasting

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PHYSICS AND CHEMISTRY OF THE EARTH 2025年 138卷

作者： Mehr, Ali Danandeh Jabarnejad, Masood Safari, Mir Jafar Sadegh Antalya Bilim Univ Civil Engn Dept Antalya Turkiye Dogus Univ Ind Engn Dept Istanbul Turkiye Toronto Metropolitan Univ Dept Geog & Environm Studies Toronto ON Canada

Drought is an environmental challenge, with devastating impacts across a wide range of sectors, including agriculture, economy, and ecosystems. Accurate drought forecasting models are necessary for sustainable water resources planning. Therefore, exploring the efficacy and parsimony of emerging machine learning (ML) techniques to enhance predictive drought forecasting models' accuracy while reducing their complexity is essential. This article introduces a novel hybrid evolutionary ML model, called MOGGP, and compares its efficiency with two evolutionary models, namely gene expression programming and multigene genetic programming as well as conventional Multilayer Perceptron. The new model integrates multi-objective geometric mean optimizer with a traditional symbolic genetic programming that allows parsimonious model selection through developing Pareto optimal solutions. Grided Standardized Precipitation Evapotranspiration Index (SPEI) datasets were employed for demonstrating MOGGP and verifying its efficiency. The results showed that annual cycle is not an effective input for the evolved evolutionary SPEI model. In addition, performance appraisal analysis revealed that the MOGGP consistently exhibits parsimonious models, superior to its counterparts, and excels in addressing multiobjective hydrological modeling problems.

关键词： Machine learning Multi-objective optimization genetic programming Standardized precipitation evapotranspiration index (SPEI) Model complexity Iraq

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Medical Data Classification Using genetic programming: A Systematic Literature Review

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EXPERT SYSTEMS 2025年第3期42卷

作者： Maurya, Pratibha Kushwaha, Arati Prakash, Om Amity Univ Uttar Pradesh Amity Inst Informat Technol Noida India Univ Allahabad Allahabad Degree Coll Dept Comp Sci Prayagraj India HNB Garhwal Univ Dept Comp Sci & Engn Srinagar India

BackgroundMedical data classification has always been a growing area of research. While machine learning techniques have been successfully applied in this field, the vast amount of data generated and the complexity of applications necessitate more robust and powerful methods, especially in the absence of domain expertise. genetic programming (GP) being a flexible evolutionary approach can autonomously craft efficient classification programs merely from example data and has thus gained significant attention across various classification *** article presents a literature survey on the application of genetic programming to medical data classification. Reported studies are evaluated based on the examination of datasets, classifier architecture, and achieved classification accuracy. Additionally, we also discuss the strengths and weaknesses of genetic programming with other algorithms, covering aspects like classification accuracy, computational efficiency, interpretability, and resource consumption. The limitations of existing GP techniques and future directions are also presented in this *** study presented in this article indicates that GP-based classifiers perform better than other classifiers in the medical domain. To the best of our knowledge, this article is the first of its kind which discusses the application of GP explicitly in medical data classification. Through this article, we aim to enlighten the readers on key concepts of GP and encourage them to build new classifiers by exploring the potential and limitations of genetic programming.

关键词： classification fitness function genetic programming medical datasets

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Learn to optimise for job shop scheduling: a survey with comparison between genetic programming and reinforcement learning

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第6期58卷 1-53页

作者： Xu, Meng Mei, Yi Zhang, Fangfang Zhang, Mengjie Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington 6012 New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6012 New Zealand

Job shop scheduling holds significant importance due to its relevance and impact on various industrial and manufacturing processes. It involves dynamically assigning and sequencing jobs to machines in a flexible production environment, where job characteristics, machine availability, and other factors might change over time. genetic programming and reinforcement learning have emerged as powerful approaches to automatically learn high-quality scheduling heuristics or directly optimise sequences of specific job-machine pairs to generate efficient schedules in manufacturing. Existing surveys on job shop scheduling typically provide overviews from a singular perspective, focusing solely on genetic programming or reinforcement learning, but overlook the hybridisation and comparison of both approaches. This survey aims to bridge this gap by reviewing recent developments in genetic programming and reinforcement learning approaches for job shop scheduling problems, providing a comparison in terms of the learning principles and characteristics for solving different kinds of job shop scheduling problems. In addition, this survey identifies and discusses current issues and challenges in the field of learning to optimise for job shop scheduling. This comprehensive exploration of genetic programming and reinforcement learning in job shop scheduling provides valuable insights into the learning principles for optimising different job shop scheduling problems. It deepens our understanding of recent developments, suggesting potential research directions for future advancements.

关键词： Hyper-heuristic Job shop scheduling genetic programming Reinforcement learning

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Surrogate-assisted cooperative learning genetic programming for the resource-constrained project scheduling problem with stochastic activity durations and transfer times

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COMPUTERS & OPERATIONS RESEARCH 2025年 173卷

作者： Zhang, Haohua Demeulemeester, Erik Li, Lubo Bai, Sijun Henan Univ Sch Business Kaifeng 475004 Henan Peoples R China Northwestern Polytech Univ Sch Management Xian 710129 Shaanxi Peoples R China Katholieke Univ Leuven Res Ctr Operat Management Fac Econ & Business B-3000 Leuven Belgium

For the project scheduling problem with transfer times under an uncertain environment, not only the activity durations are stochastic, but transfer times are often also stochastic. Therefore, we propose a resource- constrained project scheduling problem with stochastic activity durations and transfer times (RCPSP-SDT), which requires complex activity sequencing and resource transfer decisions with an activity priority rule (APR) and a resource transfer priority rule (RTPR) under unpredicted dynamic factors. However, manually designed combination rules of APRs and RTPRs are time-consuming and only for specific scenarios. Therefore, we develop a hyper-heuristic approach based on genetic programming (GP), which has been successfully applied to evolve activity priority rules for project scheduling problems. A new representation of GP individuals was designed to evolve the APR and the RTPR simultaneously. In order to improve the efficiency and solution quality of the approach, we propose surrogate-assisted cooperative learning genetic programming (SCLGP) based on GP. Based on the benchmark data set, computer experiments were conducted under nine variance levels of stochastic distributions. The results show that the proposed algorithm SCLGP performs significantly better than the classical priority rule (PR)-based heuristics. Furthermore, the effectiveness and efficiency of SCLGP were verified by comparing it to four other GP-based algorithms. Finally, the impact of the parameters on the algorithm was investigated, proving that these parameters affect the algorithm's performance.

关键词： Stochastic project scheduling Transfer times genetic programming Surrogate model Cooperative learning

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Toward Evolving Dispatching Rules With Flow Control Operations by Grammar-Guided Linear genetic programming

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第1期29卷 217-231页

作者： Huang, Zhixing Mei, Yi Zhang, Fangfang Zhang, Mengjie Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Sch Engn & Comp Sci Wellington 6140 New Zealand

LGP has been successfully applied to dynamic job shop scheduling (DJSS) to automatically evolve dispatching rules. Flow control operations are crucial in concisely describing complex knowledge of dispatching rules, such as different dispatching rules in different conditions. However, existing linear genetic programming (LGP) methods for DJSS have not fully considered the use of flow control operations. They simply included flow control operations in their primitive set, which inevitably leads to a huge number of redundant and obscure solutions in LGP search spaces. To move one step toward evolving effective and interpretable dispatching rules, this article explicitly considers the characteristics of flow control operations via grammar-guided LGP and focuses on IF operations as a starting point. Specifically, this article designs a new set of normalized terminals to improve the interpretability of IF operations and proposes three restrictions by grammar rules on the usage of IF operations: 1) specifying the available inputs;2) the maximum number;and 3) the possible locations of IF operations. The experiment results verify that the proposed method can achieve significantly better-test performance than state-of-the-art LGP methods and improves interpretability by IF-included dispatching rules. Further investigation confirms that the explicit introduction of IF operations helps effectively evolve different dispatching rules according to their decision situations.

关键词： Dispatching genetic programming Job shop scheduling Grammar Dynamic scheduling Aerospace electronics Process control Dynamic job shop scheduling (DJSS) flow control operations grammar-guided linear genetic programming (LGP) hyper heuristics

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A survey on batch training in genetic programming

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genetic programming AND EVOLVABLE MACHINES 2025年第1期26卷 1-28页

作者： Rosenfeld, Liah Vanneschi, Leonardo Univ Nova Lisboa NOVA Informat Management Sch NOVA IMS Campus Campolide P-1070312 Lisbon Portugal

In Machine Learning (ML), the use of subsets of training data, referred to as batches, rather than the entire dataset, has been extensively researched to reduce computational costs, improve model efficiency, and enhance algorithm generalization. Despite extensive research, a clear definition and consensus on what constitutes batch training have yet to be reached, leading to a fragmented body of literature that could otherwise be seen as different facets of a unified methodology. To address this gap, we propose a theoretical redefinition of batch training, creating a clearer and broader overview that integrates diverse perspectives. We then apply this refined concept specifically to genetic programming (GP). Although batch training techniques have been explored in GP, the term itself is seldom used, resulting in ambiguity regarding its application in this area. This review seeks to clarify the existing literature on batch training by presenting a new and practical classification system, which we further explore within the specific context of GP. We also investigate the use of dynamic batch sizes in ML, emphasizing the relatively limited research on dynamic or adaptive batch sizes in GP compared to other ML algorithms. By bringing greater coherence to previously disjointed research efforts, we aim to foster further scientific exploration and development. Our work highlights key considerations for researchers designing batch training applications in GP and offers an in-depth discussion of future research directions, challenges, and opportunities for advancement.

关键词： genetic programming Batch training Sampling methods Generalization Overfitting

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