Article
Computer Science, Artificial Intelligence
Yifeng Zeng, Qiang Ran, Biyang Ma, Yinghui Pan
Summary: Modeling other agents is a challenging task in artificial intelligence research. Traditional research often leads to monotonous behaviors for other agents, making it difficult for a subject agent to handle unexpected decisions. Evolutionary computation methods are used to generate diverse behaviors for other agents, effectively addressing complex agent behavior search and evaluation issues.
Article
Computer Science, Artificial Intelligence
Wei Fang, Mindan Gu
Summary: CGP is a variant of GP where individuals are represented by a two-dimensional acyclic directed graph and only use a point mutation operator, potentially leading to lack of population diversity and premature convergence. To address this issue, FMCGP is proposed, introducing frame shift mutation operator and variable-length genotype.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Materials Science, Ceramics
Jiahua Luo, Daoyuan Yang, Linwen Wei, Xing Li, Jingjing Zhao, Mingxuan Hao, Junyan Cui, Huiyu Yuan
Summary: Rolling ceramic thermal insulation balls, with advantages of low cost and easy control of particle size, are likely to become the main raw material for 3D printing, but research on their thermal insulation properties is lacking. This study analyzed the effects of temperature, particle size, and thermal fatigue on the thermal conductivity of aluminum oxide balls, and found that the contact area between particles played a dominant role in heat transfer process. Increasing contact area significantly increased thermal conductivity, while reducing surface contact area and increasing porosity could decrease thermal conductivity of the materials.
CERAMICS INTERNATIONAL
(2021)
Article
Computer Science, Artificial Intelligence
Min Shi, Yufei Tang, Xingquan Zhu, Yu Huang, David Wilson, Yuan Zhuang, Jianxun Liu
Summary: Neural architecture search (NAS) has gained significant attention in computational intelligence research, but there is limited research on Graph Neural Network (GNN) models for unstructured network data. This paper proposes a novel framework that evolves individual models in a large GNN architecture search space to dynamically approach the optimal fit. Experimental results show that evolutionary NAS matches state-of-the-art reinforcement learning methods for graph representation learning and node classification.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Andrea Murari, Riccardo Rossi, Michela Gelfusa
Summary: A methodology has been developed to determine causal relations between time series and derive equations describing interacting systems using data-driven techniques based on ensembles of Time Delay Neural Networks (TDNNs) and Symbolic Regression (SR) via Genetic Programming (GP). The developed tools outperform existing ones in detecting causal influences and identifying graphical causal networks, and they exhibit excellent performance in handling evolving systems, non-Markovianity, feedback loops, and multicausality. Numerical tests and real life examples demonstrate the power and versatility of the developed tools in handling multiple time series and even images, emphasizing the importance of recording the time evolution of signals in various fields ranging from physics to social and medical sciences.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Dominik Sobania, Dirk Schweim, Franz Rothlauf
Summary: The automatic generation of computer programs is a practical application in evolutionary computation. This survey identifies and discusses various evolutionary program synthesis approaches and provides a detailed analysis of their performance. The results suggest that these approaches perform well on problems with simple mappings from input to output, but tend to struggle with more complex problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Chemistry, Multidisciplinary
Mauro Castelli, Luca Manzoni, Luca Mariot, Giuliamaria Menara, Gloria Pietropolli
Summary: In the field of genetic programming, using a stored evolutionary history in geometric semantic genetic programming (GSGP) can lead to a multi-generational selection scheme that utilizes individuals from older populations, showing improved performance with no additional cost.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Yuan Yuan, Wolfgang Banzhaf
Summary: Program synthesis aims to automatically find programs that satisfy a given specification, but searching efficiently over the vast space of programs is still an unsolved challenge. Stochastic search, particularly through the iterative genetic improvement framework, appears to be a promising solution for scalability issues in program synthesis. This approach incrementally builds up program complexity and shows considerable advantages over existing stochastic program synthesizers in terms of scalability and solution quality.
ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Industrial
Manfred Vogel, Marion Merklein
Summary: The feasibility of using orbital forming process to produce tailored blanks is fundamentally investigated by developing a numerical model of the process and validating it with experimental results, in order to analyze material flow and determine an effective design.
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
(2021)
Article
Automation & Control Systems
Ye Tian, Haowen Chen, Haiping Ma, Xingyi Zhang, Kay Chen Tan, Yaochu Jin
Summary: In this paper, a hybrid algorithm is proposed to solve large-scale multi-objective optimization problems (LSMOPs) by combining differential evolution and conjugate gradient method. The proposed algorithm exhibits better convergence and diversity performance compared to existing evolutionary algorithms, mathematical programming methods, and hybrid algorithms on various benchmark and real-world LSMOPs.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Energy & Fuels
Christos Kyriklidis, Marios-Errikos Kyriklidis, Efstratios Loizou, Adam Stimoniaris, Constantinos G. Tsanaktsidis
Summary: This paper proposes an evolutionary computation approach to optimize raw materials mixtures for Bio Marine Fuel (BMF) production, focusing on producing high-quality Biodiesel first and then creating low-sulfur BMF to reduce pollutant emissions. The genetic algorithm utilized improves fuel prices and demonstrates the capability of coping with mixture optimization problems.
Article
Computer Science, Information Systems
Ying Bi, Bing Xue, Mengjie Zhang
Summary: This paper proposes a new approach for face image classification based on multi-objective genetic programming. It automatically evolves image descriptors that extract effective features by optimizing both accuracy and distance measure, aiming to enhance generalization. Experimental results on multiple datasets demonstrate that this method significantly outperforms other competitive methods.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Ying Bi, Bing Xue, Mengjie Zhang
Summary: Face recognition is a challenging task, and the new multi-objective genetic programming algorithms can effectively learn facial features, improve classification accuracy while reducing the number of learned features.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Software Engineering
Moshe Sipper, Tomer Halperin, Itai Tzruia, Achiya Elyasaf
Summary: EC-KitY is a comprehensive Python library for conducting evolutionary computation, which is licensed under the BSD 3-Clause License and compatible with scikit-learn. It supports various popular EC paradigms, such as genetic algorithms, genetic programming, coevolution, and evolutionary multi-objective optimization. This paper provides an overview of EC-KitY, including its ease of use, architecture, main features, and a comparison with other libraries.
Article
Chemistry, Physical
Tomasz Trzepiecinski, Andrzej Kubit, Romuald Fejkiel, Lukasz Chodola, Daniel Ficek, Ireneusz Szczesny
Summary: This study analyzed the impact of different parameters on the coefficient of friction of steel sheets through friction tests, constructed a friction model using artificial neural networks, and found that increasing the drawbead height will increase the coefficient of friction. In addition, the chlorine-based Heavy Draw 1150 compound provides more effective friction reduction compared to LAN-46 machine oil.
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)