Using quantum amplitude amplification in genetic algorithms

Schiattarella, Roberto; Vitiello, Autilia

doi:10.1016/j.eswa.2022.118203

Cited by 18 publications

(9 citation statements)

References 25 publications

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“…Thus, the paper presents the modeling of the evolution of introducing dual education complex system in a particular specialty in the form of a multi-criteria optimization problem. To solve the problems of optimization of complex systems of different origin, evolutionary algorithms [19], [20], [21] are widely used, which are part of a broader technology of so-called Soft Computing, including neural networks, fuzzy logic, probabilistic reasoning and trust networks [22], [23]. These technologies complement each other and are used in various combinations or independently to create Intelligent Systems (IS).…”

Section: Model Development and Modelling Methods Usingmentioning

confidence: 99%

Application of fuzzy evolutionary methods for the development of dual-education projects

Kozyr¹,

Molokanova²

2022

HAIT

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The article aims to present a method of specialties cases management based on models of system evolution within the framework of a pilot project in institutions of professional pre-higher and higher education of Ukraine for the training of specialists in the dual form of education. The obtained data allowed determining that the methodology of project management can be successfully implemented in Ukrainian dual education. However, the Ukrainian system of dual education still lacks many elements, and some of them cannot be reproduced in the national environment, because the student himself can choose a form of education. The article presents a simulation of the dual education system evolution in a specific specialty in the form of a multicriterial optimization problem. Well-known McKinsey model is adapted to determine the variant of the strategy for managing the dual education system. With the help of such model, the specialties of the educational institution were ranked as candidates for investment according to the criterion of future profit. The trajectory of the specialty evolution has been developed by means of the method for assessing of qualitative factors, such as the attractiveness of the labor market and the competitive status of the educational institution. The inclusion of the specialty evolution trajectory in the integral criteria of the McKinsey model, made it possible to not only establish a competitive status for the current period, but also extrapolate it without significant changes to the entire strategic period to predict the competitive status of a particular specialty. It is proposed to apply a decision support system based on the use of an apparatus of fuzzy sets and fuzzy logic to formalize decision-making in an intelligent system. The using of the fuzzy logic apparatus made it possible to adjust and clarify the strategic positions of each business unit of the dual education project portfolio. Thus, a significant practical effect was obtained, since the using of this fuzzy system allows to quickly assessing the forecast positions of dual education projects, to evaluate the future competitive status of the educational product and the management strategy of this project.

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Section: Model Development and Modelling Methods Usingmentioning

confidence: 99%

Application of fuzzy evolutionary methods for the development of dual-education projects

Kozyr¹,

Molokanova²

2022

HAIT

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“…Through operations like crossover and mutation, offspring chromosomes with even higher fitness values were generated. After several generations of reproduction, the chromosome with the highest fitness value [27], representing the optimal solution to the problem, was obtained.…”

Section: Three-way K-means Algorithm Based On Genetic Algorithm 261 G...mentioning

confidence: 99%

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

Li,

et al. 2024

Animals

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Sensors were of paramount importance in the context of poultry and livestock farming, serving as essential tools for monitoring a variety of production management parameters. The effective surveillance and optimal control of the swine facility environment critically depend on the implementation of a robust strategy for situating the optimal number of sensors in precisely the right locations. This study presents a dynamic sensor placement approach for pigsties using the three-way k-means algorithm. The method involves determining candidate sensor combinations through the application of the k-means algorithm and a re-clustering strategy. The optimal sensor locations were then identified using the Joint Entropy-Based Method (JEBM). This approach adjusts sensor positions based on different seasons (summer and winter) to effectively monitor the overall environment of the pigsty. We employ two clustering models, one based on particle swarm optimization and the other on genetic algorithms, along with a re-clustering strategy to identify candidate sensor combinations. The joint entropy-based method (JEBM) helps select the optimal sensor placement. Fused data from the optimal sensor layout undergo a fuzzy fusion process, reducing errors compared to direct averaging. The results show varying sensor needs across seasons, and dynamic placement enhances pigsty environment monitoring. Our approach reduced the number of sensors from 30 to 5 (in summer) and 6 (in winter). The optimal sensor positions for both seasons were integrated. Comparing the selected sensor layout to the average of all sensor readings representing the overall pigsty environment, the RMSE were 0.227–0.294 and the MAPE were 0.172–0.228, respectively, demonstrating the effectiveness of the sensor layout.

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“…The QGA is distinguished by its simultaneous capacity for quick convergence and global search. Quantum computing concepts and principles like qubits and a linear superposition of states form the basis of the QGA [ 22 , 23 ]. One way to express the status of a qubit is as follows:

The probabilities of the qubit being in the ‘0’ and ‘1’ states are specified by the expressions

and

, respectively, where

and

are complex numbers describing the probability amplitudes of the two states.…”

Section: Preliminariesmentioning

confidence: 99%

“…Even if every individual was examined, which led to the generation of every fitness, there was still only one instance of the fitness function that needed to be applied to each register. The parallelism of quantum mechanics may be shown here [ 22 , 23 , 24 ]. The optimal solution would be to evaluate the highest fitness in register

, which would then cause register

to collapse into a superposition of perfect individuals.…”

Section: The Proposed Qga-based Plagiarism Detection Modelmentioning

confidence: 99%

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications

Darwish,

Mhaimeed,

Elzoghabi

2023

Entropy

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The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document’s idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document’s main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model’s ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10–15% for each increase in the number of samples in the dataset.

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Using quantum amplitude amplification in genetic algorithms

Cited by 18 publications

References 25 publications

Application of fuzzy evolutionary methods for the development of dual-education projects

Application of fuzzy evolutionary methods for the development of dual-education projects

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications

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