A genetic programming-based optimal sensor placement for greenhouse monitoring and control

Article Agriculture, Multidisciplinary

Data fusion based wireless temperature monitoring system applied to intelligent greenhouse

Sibo Xia et al.

Summary: A fusion strategy for temperature monitoring system in intelligent greenhouses is proposed, utilizing a hierarchical wireless sensor network to achieve real-time data fusion, improving the efficiency and accuracy of the system.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

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Article Computer Science, Artificial Intelligence

Semantic schema based genetic programming for symbolic regression

Zahra Zojaji et al.

Summary: This study proposes a semantic schema-based genetic programming (SBGP) method that partitions the semantic search space into semantic schemas and employs local and global search operators to guide the GP search process. Experimental results demonstrate that SBGP outperforms other methods in multiple benchmarks.

APPLIED SOFT COMPUTING (2022)

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Article Agriculture, Multidisciplinary

An online machine learning-based sensors clustering system for efficient and cost-effective environmental monitoring in controlled environment agriculture

Daniel Dooyum Uyeh et al.

Summary: This study used a machine learning algorithm to determine the optimal sensor locations and found similar sensor locations across different air properties.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

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Article Plant Sciences

Grid Search for Lowest Root Mean Squared Error in Predicting Optimal Sensor Location in Protected Cultivation Systems

Daniel Dooyum Uyeh et al.

Summary: This study uses a multi-objective approach based on supervised machine learning to determine the optimal number and placement positions of sensors in a protected cultivation system. Results show that less than 32% of the sensors considered in this study are needed to optimally monitor the system's internal environment.

FRONTIERS IN PLANT SCIENCE (2022)

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Proceedings Paper Computer Science, Cybernetics

Taylor Genetic Programming for Symbolic Regression

Baihe He et al.

Summary: Taylor genetic programming (TaylorGP) is a method for solving symbolic regression problems, which uses Taylor polynomials to approximate the symbolic equation fitting the dataset and enhances genetic programming performance by extracting features of the symbolic equation. Experimental results demonstrate that TaylorGP achieves higher accuracy and faster search speed compared to other methods.

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

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Article Computer Science, Software Engineering

An efficient framework for data aggregation in smart agriculture

Jiangjun Yuan et al.

Summary: The smart agricultural system utilizes sensor nodes to collect data and perform control operations, proposing an efficient framework for data aggregation and collection, and utilizing genetic algorithms and drones to improve system efficiency and scalability.

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE (2021)

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Article Computer Science, Artificial Intelligence

Graph representations in genetic programming

Leo Francoso Dal Piccol Sotto et al.

Summary: Different genetic programming methods were empirically studied, showing that the choice of representation, genetic operator, and evolutionary algorithm depends on the problem domain. Graph GP methods can improve search performance, especially in digital circuit synthesis tasks.

GENETIC PROGRAMMING AND EVOLVABLE MACHINES (2021)

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Article Mathematical & Computational Biology

Genetic CFL: Hyperparameter Optimization in Clustered Federated Learning

Shaashwat Agrawal et al.

Summary: Federated learning is a distributed deep learning model that has the potential to revolutionize machine learning, but faces challenges in implementation. To overcome limitations of traditional FL algorithms, a genetic clustered FL algorithm is proposed which shows significant improvements on non-IID data.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2021)

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Article Engineering, Electrical & Electronic

Identification of Optimal Sensor Location Based on Trade-Off Approach to Improve Resiliency of Electricity Market in Smart Grid

Prasanta Kumar Jena et al.

Summary: This study proposes an approach based on binary optimization to identify strategic sensor placement locations in the smart grid, aiming to minimize disruption in the electricity market while simultaneously improving the resiliency of electricity market operation and reducing sensor installation cost by achieving network observability. The effectiveness of this trade-off solution has been validated on standard IEEE 14, 30, and 57 test systems.

IEEE SENSORS JOURNAL (2021)

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Article Computer Science, Information Systems

A Reinforcement Learning Approach for Optimal Placement of Sensors in Protected Cultivation Systems

Daniel Dooyum Uyeh et al.

Summary: Optimal placement of sensors in a greenhouse using reinforcement learning can enhance monitoring and controlling of temperature and humidity, providing more accurate design optimization.

IEEE ACCESS (2021)

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Review Engineering, Multidisciplinary