Retracted: High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation

Logeswaran, K.; Suresh, P.

doi:10.1111/coin.12490

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Substantial experiments are conduction and the obtained results are assessed against the above said performance metrics by comparing the proposed APSO_RL OFF approach with existing EC based HUIM approach. AGA_RL OFF [14], HUIM-BPSO [9], and HUPE UMU -GRAM [5] are the three existing state-of-the-art HUIM methods based on meta heuristic EC.…”

Section: Performance Metricsmentioning

confidence: 99%

“…Here comes the role of Reinforcement Learning. RL approaches namely Q-Learning is used to assess the quality of solution in past iterations and generates the feedback from the assessment [14]. This feedback is used as metric to calibrate the control parameters of evolutionary approaches which will impact the quality of solution generated during the next iteration.…”

Section: Introductionmentioning

confidence: 99%

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Particle Swarm Optimization Method Combined with off Policy Reinforcement Learning Algorithm for the Discovery of High Utility Itemset

Logeswaran¹,

Suresh

Anandamurugan³

2023

ITC

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Mining of High Utility Itemset (HUI) is an area of high importance in data mining that involves numerous methodologies for addressing it effectively. When the diversity of items and size of an item is quite vast in the given dataset, then the problem search space that needs to be solved by conventional exact approaches to High Utility Itemset Mining (HUIM) also increases in terms of exponential. This factual issue has made the researchers to choose alternate yet efficient approaches based on Evolutionary Computation (EC) to solve the HUIM problem. Particle Swarm Optimization (PSO) is an EC-based approach that has drawn the attention of many researchers to unravel different NP-Hard problems in real-time. Variants of PSO techniques have been established in recent years to increase the efficiency of the HUIs mining process. In PSO, the Minimization of execution time and generation of reasonable decent solutions were greatly influenced by the PSO control parameters namely Acceleration Coefficient and and Inertia Weight. The proposed approach is called Adaptive Particle Swarm Optimization using Reinforcement Learning with Off Policy (APSO-RLOFF), which employs the Reinforcement Learning (RL) concept to achieve the adaptive online calibration of PSO control and, in turn, to increase the performance of PSO. The state-of-the-art RL approach called the Q-Learning algorithm is employed in the APSO-RLOFF approach. In RL, state-action utility values are estimated during each episode using Q-Learning. Extensive tests are carried out on four benchmark datasets to evaluate the performance of the suggested technique. An exact approach called HUP-Miner and three EC-based approaches, namely HUPEUMU-GRAM, HUIM-BPSO, and AGA_RLOFF, are used to relate the performance of the anticipated approach. From the outcome, it is inferred that the performance metrics of APSO-RLOFF, namely no of discovered HUIs and execution time, outstrip the previously considered EC computations.

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Section: Performance Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Method Combined with off Policy Reinforcement Learning Algorithm for the Discovery of High Utility Itemset

Logeswaran¹,

Suresh

Anandamurugan³

2023

ITC

View full text Add to dashboard Cite

show abstract

“…The pattern extraction process was a meticulous endeavor, involving not only the capture but also the transformation of these patterns. Techniques, such as simplification, addition, and exaggeration, were meticulously applied to deform and model these patterns effectively [28]. This intricate process aimed to ensure that the patterns were not only visually appealing but also adaptable to the interactive genetic algorithm.…”

Section: Research On Shijingshan Park Square Landscape Color Design M...mentioning

confidence: 99%

Chromatics in Urban Landscapes: Integrating Interactive Genetic Algorithms for Sustainable Color Design in Marine Cities

Zhang,

Kim

2023

Applied Sciences

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Color design plays a pivotal role in determining whether marine urban landscapes meet the diverse needs of individuals, encompassing their visual, psychological, behavioral, and spiritual dimensions. In the contemporary urban environment, a host of practical challenges, including severe color pollution, the absence of regional cultural characteristics, and a lack of humanistic sensitivity in public spaces’ color schemes, necessitates a thorough exploration of the intricate relationship between color and human psychological needs within environmental contexts. This study leverages the potential of interactive genetic algorithms as a powerful tool for innovative color design solutions. Anchored in the nexus of the environment, human psychology, and color theory, this research delves into the intricate nuances of marine urban landscape color. Employing a comprehensive approach that combines questionnaires and extensive field surveys, we seek to elucidate the critical interplay between color and human well-being. Drawing upon the case study of Zhuhai Shijingshan Park, we harness the principles of interactive genetic algorithms to iteratively design marine urban color patterns. Through this experimentation, our study unveils novel design strategies that harmonize with the psychological idiosyncrasies of the human–environment interface. Specifically, the findings of our research highlight the profound impact of color selection on the emotional and behavioral responses of individuals within marine urban landscapes. Our study contributes vital insights into the art and science of marine urban color design, shedding light on the nuanced ways in which color choices can enhance the quality of urban spaces and promote human well-being. This research endeavors to bridge the gap between marine urban design and human psychology through a comprehensive exploration of color design in marine urban landscapes. By addressing the practical challenges and leveraging innovative design techniques, we strive to provide a holistic understanding of the symbiotic relationship between color and human experience, ultimately enriching the design practices that shape our urban environments.

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Designing an Adaptive Learning Platform Based on OBE Teaching in Cloud Computing

Líu

2022

2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)

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Retracted: High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation

Cited by 4 publications

References 26 publications

Particle Swarm Optimization Method Combined with off Policy Reinforcement Learning Algorithm for the Discovery of High Utility Itemset

Particle Swarm Optimization Method Combined with off Policy Reinforcement Learning Algorithm for the Discovery of High Utility Itemset

Chromatics in Urban Landscapes: Integrating Interactive Genetic Algorithms for Sustainable Color Design in Marine Cities

Designing an Adaptive Learning Platform Based on OBE Teaching in Cloud Computing

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