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Anchoring actions using conditional behavior trees and genetic programming

APPLIED INTELLIGENCE 2025年第6期55卷 1-15页

作者： Escudero-Rodrigo, Diego Alquezar, Rene Aranda, Joan Univ Politecn Cataluna BarcelonaTech UPC Barcelona 08034 Spain

Robotic Kitting means the creation of parts assortment to be used later. These parts are selected from one or more containers in which there are different types of them randomly distributed. The Anchoring Problem should be considered if we want to provide a general solution to robotic kitting, since users want that it works with different types of parts that are not known 'a priori'. Therefore, we are working on a human supervised approach in which Behavior Trees, robot learning and human-robot interaction are used to anchor percepts and operations to symbols during commissioning or reconfiguration phases. In this paper we explain: (1) the anchoring mechanisms in our system and how behavior trees can be used to represent an anchor, and (2) how genetic programming is used to generate Conditional Behavior Trees that anchor symbolic actions to robot operations.

关键词： Intelligent robotics Automated kitting Anchoring problem Behavior trees genetic programming Human-robot interaction

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Multi-objective genetic programming based on decomposition for feature learning in image classification

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Zhang, Tuo Bi, Ying Liang, Jing Xue, Bing Zhang, Mengjie Zhengzhou Univ Sch Elect & Informat Engn 100 KeXue Rd Zhengzhou 450001 Henan Peoples R China State Key Lab Intelligent Agr Power Equipment 39 Xiyuan Rd Luoyang 471039 Henan Peoples R China Henan Inst Technol Sch Elect Engn & Automat 699 Pingyuan Rd Xinxiang 453003 Henan Peoples R China Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Sch Engn & Comp Sci POB 600 Wellington 6140 New Zealand

Image classification presents a challenge due to its high dimensionality and extensive variations. Feature learning is a powerful method in addressing this challenge, constituting a multi-objective problem aimed at maximizing classification accuracy and minimizing the number of learned features. A few multi-objective genetic programming (MOGP) methods have been proposed to optimize these two objectives, simultaneously. However, existing MOGP methods ignore the characteristics of feature learning tasks. Therefore, this work proposes a decomposition-based MOGP approach with a global replacement strategy for feature learning in data-efficient image classification. To handle the different value ranges of the two objectives, a transformation function is designed to uniform the range of the number of learned features. In addition, a preference-based decomposition strategy is proposed to address the preference for the objective of classification accuracy. The proposed approach is compared with existing MOGP methods for feature learning on five different image classification datasets with different numbers of training images. The experimental results demonstrate the effectiveness of the proposed approach by achieving better HVs than or comparable to the existing MOGP methods in at least 13 out of 20 cases and classification accuracy significantly better than a popular neural architecture search method in all cases.

关键词： genetic programming Multi-objective optimization Feature learning Image classification Evolutionary computation

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An alternative approach to biokinetic modelling for phenol pollutant degradation and microbial growth using genetic programming

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ENVIRONMENTAL TECHNOLOGY 2025年 1-12页

作者： Krishnan, Suganya Sivapragasam, Chandrasekaran Sharma, Naresh K. Kalasalingam Acad Res & Educ Ctr Water Technol Dept Civil Engn Krishnankoil India Kalasalingam Acad Res & Educ Ctr Biotechnol Krishnankoil 626126 TN India

Biokinetic models can optimise pollutant degradation and enhance microbial growth processes, aiding to protect ecosystem protection. Traditional biokinetic approaches (such as Monod, Haldane, etc.) can be challenging, as they require detailed knowledge of the organism's metabolism and the ability to solve numerous kinetic differential equations based on the principles of micro, molecular biology and biochemistry (first engineering principles) which can lead to discrepancies between predicted and actual degradation rates. More recently, data-driven machine-learning techniques have emerged as a promising alternative for modelling microbial systems. A few machine learning models (such as ANN, SVM, RF, DT, XG BOOST, etc.) have been used recently for modelling phenol degradation, but they lack the robustness of generating mathematical models. This gap is addressed in this study using genetic programming (GP) as the modelling approach for modelling the phenol degradation. This study utilises the microalgae Acutodesmus Obliquus, finding that phenol degradation of 98% required 216 hours. Both the traditional kinetic approach and the genetic programming (GP) approach were used to determine the specific growth rate (mu max) and saturation constant (Ks). It is noted that without any a priori information on the form of the mathematical mode, GP can evolve a model which closely fits the Monod kinetics, thus demonstrating that data-driven models can bring out the first engineering principles on which biokinetic models are dependent or framed in a most swift and effective way. Performance was assessed using root mean square error (RMSE) and correlation coefficient (R), with the GP model showing superior predictive accuracy.

关键词： Phenol microalgae biokinetic model data-driven model genetic programming

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A hybrid genetic programming algorithm for the distributed assembly scheduling problems with transportation and sequence-dependent setup times

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ENGINEERING OPTIMIZATION 2025年第3期57卷 786-812页

作者： Deng, Jiawen Zhang, Jihui Yang, Shengxiang Qingdao Univ Inst Complex Sci Sch Automat Qingdao Peoples R China Shandong Key Lab Ind Control Technol Sch Automat Qingdao Peoples R China De Montfort Univ Ctr Computat Intelligence Sch Comp Sci & Informat Leicester England

This paper investigates a distributed assembly permutation flow-shop scheduling problem with transportation and sequence-dependent set-up times (DAPFSP-TSDST). A hybrid genetic programming (HGP) algorithm is proposed to optimize the makespan of the assembly stage, which inherits the merits of genetic programming (GP) and neighbourhood search operators. In HGP, a hybrid problem-specific initialization heuristic is developed to make populations more diverse. Multiple neighbourhood search operators are employed as the leaf nodes, which are vital for the success of GP. A product shift strategy is proposed to strengthen its exploitability. In addition, a simulated annealing criterion is adopted to make the HGP explore more thoroughly. Finally, statistical and computational experiments are carried out on the benchmark instances. The results exhaustively identify the notable competitiveness of the HGP algorithm in coping with the DAPFSP-TSDST.

关键词： Distributed assembly problem genetic programming scheduling manufacturing

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Predicting the Compression Index of Clayey Soils Using a Hybrid genetic programming and XGBoost Model

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APPLIED SCIENCES-BASEL 2025年第4期15卷 1926-1926页

作者： Baghbani, Abolfazl Kiany, Katayoon Abuel-Naga, Hossam Lu, Yi Deakin Univ Sch Engn Geelong Vic 3216 Australia La Trobe Univ Dept Civil Engn Bundoora Vic 3086 Australia Univ Melbourne Melbourne Sch Design Parkville Vic 3052 Australia Guangzhou Univ Sch Civil Engn Guangzhou 510006 Peoples R China

Featured Application This study introduces a novel hybrid method combining genetic programming (GP) and XGBoost to accurately predict the compression index (Cc) of clayey soils. The proposed model offers a powerful tool for geotechnical engineers to assess soil compressibility with higher precision and aids in the design and analysis of foundations, earth structures, and settlement calculations. The method's ability to handle complex, nonlinear relationships makes it particularly valuable for projects involving diverse soil types and challenging site conditions. Its application can significantly enhance the reliability of geotechnical assessments and streamline the design process for critical infrastructure *** The accurate prediction of the compression index (Cc) is crucial for understanding the settlement behavior of clayey soils, which is a key factor in geotechnical design. Traditional empirical models, while widely used, often fail to generalize across diverse soil conditions due to their reliance on simplified assumptions and regional dependencies. This study proposed a novel hybrid method combining genetic programming (GP) and XGBoost methods. A large database (including 385 datasets) of geotechnical properties, including the liquid limit (LL), the plasticity index (PI), the initial void ratio (e0), and the water content (w), was used. The hybrid GP-XGBoost model achieved remarkable predictive performance, with an R2 of 0.966 and 0.927 and mean squared error (MSE) values of 0.001 and 0.001 for training and testing datasets, respectively. The mean absolute error (MAE) was also exceptionally low at 0.030 for training and 0.028 for testing datasets. Comparative analysis showed that the hybrid model outperformed the standalone GP (R2 = 0.934, MSE = 0.003) and XGBoost (R2 = 0.939, MSE = 0.002) models, as well as traditional empirical methods such as Terzaghi and Peck (R2 = 0.149, MSE = 0.090). Key findings highlighted that the initial void ratio and

关键词： compression index clayey soils genetic programming XGBoost hybrid machine learning soil compressibility predictive modeling soil settlement

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Health prediction for king salmon via evolutionary machine learning with genetic programming

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JOURNAL OF THE ROYAL SOCIETY OF NEW ZEALAND 2025年第1期55卷 166-191页

作者： Zhang, Fangfang Zhang, Yuye Casanovas, Paula Schattschneider, Jessica Walker, Seumas P. Xue, Bing Zhang, Mengjie Symonds, Jane E. Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand Cawthron Inst Nelson New Zealand

King (Chinook) salmon is the only salmon species farmed in Aotearoa New Zealand and accounts for over half of the world's production of king salmon. Determining the health status of king salmon effectively is important for farming. However, it is a challenging task due to the complex biotic and abiotic factors that influence health. Evolutionary machine learning algorithms have shown their superiority in learning models for challenging tasks. However, they have not been investigated for health prediction in king salmon farming. This paper focuses on data processing and machine learning algorithm design to develop king salmon health prediction models in Aotearoa New Zealand. Particularly, this paper proposes a king salmon health prediction method based on genetic programming which is an evolutionary machine learning algorithm. The results show that genetic programming achieves the best overall performance among all examined typical machine learning algorithms for most trials. Further analyses show that genetic programming can automatically detect important features for learning classifiers for king salmon health classification tasks effectively, and can also learn potentially interpretable models. Our results are an important step forward in developing health prediction tools to automatically assess health status of farmed king salmon in Aotearoa New Zealand.

关键词： Evolutionary machine learning genetic programming king salmon health prediction classification

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Cat Swarm Algorithm Generated Based on genetic programming Framework Applied in Digital Watermarking

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Computers, Materials & Continua 2025年第5期83卷 3135-3163页

作者： Shu-Chuan Chu Libin Fu Jeng-Shyang Pan Xingsi Xue Min Liu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210000China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266000China Department of Information Management Chaoyang University of TechnologyTaichung40601TaiwanChina School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resource Nanjing University of Information Science and TechnologyNanjing210000China

Evolutionary algorithms have been extensively utilized in practical ***,manually designed population updating formulas are inherently prone to the subjective influence of the *** programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world *** paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human *** modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the *** designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update *** Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the *** validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark ***,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking *** experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.

关键词： Cat swarm algorithm genetic programming digital watermarking update mode mode generation framework

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Multiobjective Dynamic Flexible Job Shop Scheduling With Biased Objectives via Multitask genetic programming

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 169-183页

作者： Zhang, Fangfang Shi, Gaofeng Mei, Yi Zhang, Mengjie Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Emerson New Plymouth4310 New Zealand

Dynamic flexible job shop scheduling is an important combinatorial optimization problem that has rich real-world applications such as product processing in manufacturing. genetic programming has been successfully used to learn scheduling heuristics for dynamic flexible job shop scheduling. Intuitively, users prefer small and effective scheduling heuristics that can not only generate promising schedules but also are computationally efficient and easy to be understood. However, a scheduling heuristic with better effectiveness tends to have a larger size, and the effectiveness of rules and rule size are potentially conflicting objectives. With the traditional dominance relation-based multiobjective algorithms, there is a search bias toward rule size, since rule size is much easier to optimized than effectiveness, and larger rules are easily abandoned, resulting in the loss of effectiveness. To address this issue, this article develops a novel multiobjective genetic programming algorithm that takes size and effectiveness of scheduling heuristics for optimization via multitask learning mechanism. Specifically, we construct two tasks for the multiobjective optimization with biased objectives using different search mechanisms for each task. The focus of the proposed algorithm is to improve the effectiveness of learned small rules by knowledge sharing between constructed tasks which is implemented with the crossover operator. The results show that our proposed algorithm performs significantly better, i.e., with smaller and more effective scheduling heuristics, than the state-of-the-art algorithms in the examined scenarios. By analyzing the population diversity, we find that the proposed algorithm has a good balance between exploration and exploitation during the evolutionary process. © 2024 IEEE.

关键词： genetic programming

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Numerical-based multi-objective optimization and regression analysis using genetic programming for novel microfabricated thermoresistive calorimetric flow sensor in precise low-velocity biomedical applications

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2025年 1-14页

作者： Babaelahi, Mojtaba Kazemi, Mohammad Univ Qom Dept Mech Engn Qom Iran

This research scrutinizes a microfabricated thermoresistive calorimetric flow sensor by applying computational fluid dynamics (CFD) modeling, multi-objective optimization, and genetic programming-based regression. The CFD model is instrumental in conducting a parametric analysis of the sensor's geometry and performance under varying inlet conditions. Through multi-objective optimization strategies, the objective is to maximize sensitivity while minimizing power consumption, leading to the identification of optimal design parameters. The resultant optimized dataset trains a genetic programming algorithm, facilitating the derivation of analytical relations between inlet conditions and optimal geometry. Insights into the sensor's behavior are gleaned from simulations, and the amalgamation of optimization and machine learning expedites the design process. Particularly noteworthy is the heightened precision demonstrated by the sensor at low velocities ranging from 1 to 6 m s-1, rendering it well-suited for biomedical applications. This interdisciplinary methodology marks a significant stride in advancing sensor technology, leveraging the combination of numerical simulation, optimization, and data-driven modeling.

关键词： Computational fluid dynamics Multi-objective optimization genetic programming Thermo-resistive sensor Biomedical systems

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Automatic Design of Dynamic Collaboration Strategies for Machines and Automated Guided Vehicles via Multiobjective genetic programming

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UNMANNED SYSTEMS 2025年第1期13卷 233-246页

作者： Xin, Bin Lu, Sai He, Yingmei Wang, Qing Deng, Fang Beijing Inst Technol Sch Automat 5 South St Beijing 100081 Peoples R China

In the flexible manufacturing system (FMS), the automated guided vehicles (AGVs) have been widely applied to the material logistics. The transporting phases of AGVs and the processing phases of machines are alternately executed and form the production flow. The two kinds of phases will both influence the completing time and cause energy consumption and are difficult to decouple. Therefore, in this paper, we focus on the dynamic collaboration problem between processing machines and AGVs (DCPMA) and establish a multiobjective optimization model to minimize the makespan and the energy consumption of FMS. In order to solve DCPMA, we propose a novel genetic programming (GP) to evolve collaboration strategies. In GP, 10 status statistics related to the handling time and energy consumption are selected into GP terminal set to express the GP tree. During dynamic simulation, each collaboration strategy evaluated by GP will dynamically select the job-machine-AGV scheme combination with the highest priority calculated from the GP tree. In addition, a series of generation operators and selection operators are customized for DCPMA. Finally, the training and testing results show that the proposed GP is superior to 28 combinations of basic collaboration strategies, and has better adaptability and scalability for various scenarios.

关键词： Dynamic collaboration flexible manufacturing system automated guided vehicles genetic programming multiobjective optimization

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