Evolving cellular automata by parallel quantum genetic algorithm

Laboudi, Zakaria; Chikhi, Salim

doi:10.1109/ndt.2009.5272212

Cited by 9 publications

(19 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

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

Darwish,

Mhaimeed,

Elzoghabi

2023

Entropy

View full text Add to dashboard Cite

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.

show abstract

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%

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

Darwish,

Mhaimeed,

Elzoghabi

2023

Entropy

View full text Add to dashboard Cite

show abstract

“…The essence of the algorithm lies in quantum evolution and the group search method, so it has powerful global search ability and high operational efficiency. Compared with the traditional GA, QGA is superior, but QGA still has shortcomings such as more iterations when optimizing complex functions and falling into local extremes [34,35].…”

Section: Principle Of Quantum Genetic Algorithmmentioning

confidence: 99%

A Knowledge Acquisition Method of Ship Coating Defects Based on IHQGA-RS

Bu¹,

Ji²,

Zhang³

et al. 2022

Coatings

View full text Add to dashboard Cite

Coating defects are caused by a series of factors such as the improper operation of workers and the quality of the coating itself. At present, the coating process of all shipyards is inspected and recorded at a specific time after construction, which cannot prevent and control defects scientifically. As a result, coating quality decreases, and production costs increase. Therefore, this paper proposes a knowledge acquisition method based on a rough set (RS) optimized by an improved hybrid quantum genetic algorithm (IHQGA) to guide the ship-coating construction process. Firstly, the probability amplitude is determined according to the individual position of the population, and the adaptive value k is proposed to determine the rotation angle of the quantum gate. On this basis, the simulated annealing algorithm is combined to enhance the local search ability of the algorithm. Finally, the algorithm is applied to rough set attribute reduction to improve the efficiency and accuracy of rough set attribute reduction. The data of 600 painted examples of 210-KBC bulk carriers from a shipyard between 2015 and 2020 are randomly selected to test the knowledge acquisition method proposed in the paper and other knowledge acquisition methods. The results show that the IHQGA attribute approximate reduction algorithm proposed in this paper is the first to reach the optimal adaptation degree of 0.847, the average adaptation degree is better than other algorithms, and the average consumption time is about 10% less than different algorithms, so the IHQGA has more vital and more efficient seeking ability. The knowledge acquisition result based on the IHQGA optimization rough set has 20–50% fewer rules and 5–10% higher accuracy than other methods, and the industry experts have high recognition. The knowledge acquisition method of this paper is validated on a hull segment. The obtained results are consistent with the expert diagnosis results, indicating that the method proposed in this paper has certain practicability.

show abstract

“…For example, QGA was used to solve the knapsack problem in search optimization [13]. It is also used to solve the binary decision diagram ordering problem [14] and density classification problem [15].…”

Section: Quantum Genetic Algorithmmentioning

confidence: 99%

A fusion model of SWT, QGA and BP neural network for wireless network traffic prediction

Liu

Sun

et al. 2013

2013 15th IEEE International Conference on Communication Technology

View full text Add to dashboard Cite

In this paper a fusion model by combining the Stationary Wavelet Transform (SWT), Quantum Genetic Algorithm (QGA) and Back-propagation (BP) Neural Network is proposed to forecast wireless network traffic. In order to achieve guaranteed Quality of Service (QoS) in wireless networks, various managing measures can be taken only by knowing the network traffic in advance. This developed fusion model which is called the SWT-QGA-BP model can be efficiently used to assess the future network and provide adequate evidence for wireless network management. By using SWT, the original non-stationary wireless traffic data are transformed into multiple stationary components. With the QGA evolution, the BP neural network is optimized in both architecture and initial parameters. The simulation further indicates the effectiveness of the proposed SWT-QGA-BP fusion model, and the results show that our model can enhance the prediction performance significantly in accuracy.

show abstract

Evolving cellular automata by parallel quantum genetic algorithm

Cited by 9 publications

References 10 publications

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

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

A Knowledge Acquisition Method of Ship Coating Defects Based on IHQGA-RS

A fusion model of SWT, QGA and BP neural network for wireless network traffic prediction

Contact Info

Product

Resources

About