• Title/Summary/Keyword: Cross-entropy algorithm

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Multi Agents-Multi Tasks Assignment Problem using Hybrid Cross-Entropy Algorithm (혼합 교차-엔트로피 알고리즘을 활용한 다수 에이전트-다수 작업 할당 문제)

  • Kim, Gwang
    • Journal of Korea Society of Industrial Information Systems
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    • v.27 no.4
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    • pp.37-45
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    • 2022
  • In this paper, a multi agent-multi task assignment problem, which is a representative problem of combinatorial optimization, is presented. The objective of the problem is to determine the coordinated agent-task assignment that maximizes the sum of the achievement rates of each task. The achievement rate is represented as a concave down increasing function according to the number of agents assigned to the task. The problem is expressed as an NP-hard problem with a non-linear objective function. In this paper, to solve the assignment problem, we propose a hybrid cross-entropy algorithm as an effective and efficient solution methodology. In fact, the general cross-entropy algorithm might have drawbacks (e.g., slow update of parameters and premature convergence) according to problem situations. Compared to the general cross-entropy algorithm, the proposed method is designed to be less likely to have the two drawbacks. We show that the performances of the proposed methods are better than those of the general cross-entropy algorithm through numerical experiments.

Applying CEE (CrossEntropyError) to improve performance of Q-Learning algorithm (Q-learning 알고리즘이 성능 향상을 위한 CEE(CrossEntropyError)적용)

  • Kang, Hyun-Gu;Seo, Dong-Sung;Lee, Byeong-seok;Kang, Min-Soo
    • Korean Journal of Artificial Intelligence
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    • v.5 no.1
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    • pp.1-9
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    • 2017
  • Recently, the Q-Learning algorithm, which is one kind of reinforcement learning, is mainly used to implement artificial intelligence system in combination with deep learning. Many research is going on to improve the performance of Q-Learning. Therefore, purpose of theory try to improve the performance of Q-Learning algorithm. This Theory apply Cross Entropy Error to the loss function of Q-Learning algorithm. Since the mean squared error used in Q-Learning is difficult to measure the exact error rate, the Cross Entropy Error, known to be highly accurate, is applied to the loss function. Experimental results show that the success rate of the Mean Squared Error used in the existing reinforcement learning was about 12% and the Cross Entropy Error used in the deep learning was about 36%. The success rate was shown.

Tri-training algorithm based on cross entropy and K-nearest neighbors for network intrusion detection

  • Zhao, Jia;Li, Song;Wu, Runxiu;Zhang, Yiying;Zhang, Bo;Han, Longzhe
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.12
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    • pp.3889-3903
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    • 2022
  • To address the problem of low detection accuracy due to training noise caused by mislabeling when Tri-training for network intrusion detection (NID), we propose a Tri-training algorithm based on cross entropy and K-nearest neighbors (TCK) for network intrusion detection. The proposed algorithm uses cross-entropy to replace the classification error rate to better identify the difference between the practical and predicted distributions of the model and reduce the prediction bias of mislabeled data to unlabeled data; K-nearest neighbors are used to remove the mislabeled data and reduce the number of mislabeled data. In order to verify the effectiveness of the algorithm proposed in this paper, experiments were conducted on 12 UCI datasets and NSL-KDD network intrusion datasets, and four indexes including accuracy, recall, F-measure and precision were used for comparison. The experimental results revealed that the TCK has superior performance than the conventional Tri-training algorithms and the Tri-training algorithms using only cross-entropy or K-nearest neighbor strategy.

A cross-entropy algorithm based on Quasi-Monte Carlo estimation and its application in hull form optimization

  • Liu, Xin;Zhang, Heng;Liu, Qiang;Dong, Suzhen;Xiao, Changshi
    • International Journal of Naval Architecture and Ocean Engineering
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    • v.13 no.1
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    • pp.115-125
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    • 2021
  • Simulation-based hull form optimization is a typical HEB (high-dimensional, expensive computationally, black-box) problem. Conventional optimization algorithms easily fall into the "curse of dimensionality" when dealing with HEB problems. A recently proposed Cross-Entropy (CE) optimization algorithm is an advanced stochastic optimization algorithm based on a probability model, which has the potential to deal with high-dimensional optimization problems. Currently, the CE algorithm is still in the theoretical research stage and rarely applied to actual engineering optimization. One reason is that the Monte Carlo (MC) method is used to estimate the high-dimensional integrals in parameter update, leading to a large sample size. This paper proposes an improved CE algorithm based on quasi-Monte Carlo (QMC) estimation using high-dimensional truncated Sobol subsequence, referred to as the QMC-CE algorithm. The optimization performance of the proposed algorithm is better than that of the original CE algorithm. With a set of identical control parameters, the tests on six standard test functions and a hull form optimization problem show that the proposed algorithm not only has faster convergence but can also apply to complex simulation optimization problems.

Application of Subarray Averaging and Entropy Minimization Algorithm to Stepped-Frequency ISAR Autofocus (부배열 평균과 엔트로피 최소화 기법을 이용한 stepped-frequency ISAR 자동초점 기법 성능 향상 연구)

  • Jeong, Ho-Ryung;Kim, Kyung-Tae;Lee, Dong-Han;Seo, Du-Chun;Song, Jeong-Heon;Choi, Myung-Jin;Lim, Hyo-Suk
    • Proceedings of the KSRS Conference
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    • 2008.03a
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    • pp.158-163
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    • 2008
  • In inverse synthetic aperture radar (ISAR) imaging, An ISAR autofocusing algorithm is essential to obtain well-focused ISAR images. Traditional methods have relied on the approximation that the phase error due to target motion is a function of the cross-range dimension only. However, in the stepped-frequency radar system, it tends to become a two-dimensional function of both down-range and cross-range, especially when target's movement is very fast and the pulse repetition frequency (PRF) is low. In order to remove the phase error along down-range, this paper proposes a method called SAEM (subarray averaging and entropy minimization) [1] that uses a subarray averaging concept in conjunction with the entropy cost function in order to find target motion parameters, and a novel 2-D optimization technique with the inherent properties of the proposed entropy-based cost function. A well-focused ISAR image can be obtained from the combination of the proposed method and a traditional autofocus algorithm that removes the phase error along the cross-range dimension. The effectiveness of this method is illustrated and analyzed with simulated targets comprised of point scatters.

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A Modified Error Function to Improve the Error Back-Propagation Algorithm for Multi-Layer Perceptrons

  • Oh, Sang-Hoon;Lee, Young-Jik
    • ETRI Journal
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    • v.17 no.1
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    • pp.11-22
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    • 1995
  • This paper proposes a modified error function to improve the error back-propagation (EBP) algorithm for multi-Layer perceptrons (MLPs) which suffers from slow learning speed. It can also suppress over-specialization for training patterns that occurs in an algorithm based on a cross-entropy cost function which markedly reduces learning time. In the similar way as the cross-entropy function, our new function accelerates the learning speed of the EBP algorithm by allowing the output node of the MLP to generate a strong error signal when the output node is far from the desired value. Moreover, it prevents the overspecialization of learning for training patterns by letting the output node, whose value is close to the desired value, generate a weak error signal. In a simulation study to classify handwritten digits in the CEDAR [1] database, the proposed method attained 100% correct classification for the training patterns after only 50 sweeps of learning, while the original EBP attained only 98.8% after 500 sweeps. Also, our method shows mean-squared error of 0.627 for the test patterns, which is superior to the error 0.667 in the cross-entropy method. These results demonstrate that our new method excels others in learning speed as well as in generalization.

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An Efficient Video Coding Algorithm Applying Brightness Variation Compensation (밝기변화 보상을 적용한 효율적인 비디오 코딩 알고리즘)

  • Kim Sang-Hyun
    • Journal of the Institute of Convergence Signal Processing
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    • v.5 no.4
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    • pp.287-293
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    • 2004
  • This paper proposes an efficient motion compensation algorithm for video sequences with brightness variations. In the proposed algorithm, the brightness variation parameters are estimated and local motions are compensated. To detect the frame with large brightness variations, we employ the frame classification based on the cross entropy between histograms of two successive frames, which can reduce the computational redundancy. Simulation results show that the proposed method yields a higher peak signal to noise ratio (PSNR) than that of the conventional methods, with a low computational load, when the video scene contains large brightness changes.

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Vertiport Location Problem to Maximize Utilization Rate for Air Taxi (에어 택시 이용률 최대화를 위한 수직이착륙장 위치 결정 문제)

  • Gwang Kim
    • Journal of Korea Society of Industrial Information Systems
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    • v.28 no.5
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    • pp.67-75
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    • 2023
  • This paper deals with the operation of air taxis, which is one of the latest innovative technologies aimed at solving the issue of traffic congestion in cities. A key challenge for the successful introduction of the technology and efficient operation is a vertiport location problem. This paper employs a discrete choice model to calculate choice probabilities of transportation modes for each route, taking into account factors such as cost and travel time associated with different modes. Based on this probability, a mathematical formulation to maximize the utilization rate for air taxi is proposed. However, the proposed model is NP-hard, effective and efficient solution methodology is required. Compared to previous studies that simply proposed the optimization models, this study presents a solution methodology using the cross-entropy algorithm and confirms the effectiveness and efficiency of the algorith through numerical experiments. In addition to the academic excellence of the algorithm, it suggests that decision-making that considers actual data and air taxi utilization plans can increase the practial usability.

Adaptive Multi-class Segmentation Model of Aggregate Image Based on Improved Sparrow Search Algorithm

  • Mengfei Wang;Weixing Wang;Sheng Feng;Limin Li
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.2
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    • pp.391-411
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    • 2023
  • Aggregates play the skeleton and supporting role in the construction field, high-precision measurement and high-efficiency analysis of aggregates are frequently employed to evaluate the project quality. Aiming at the unbalanced operation time and segmentation accuracy for multi-class segmentation algorithms of aggregate images, a Chaotic Sparrow Search Algorithm (CSSA) is put forward to optimize it. In this algorithm, the chaotic map is combined with the sinusoidal dynamic weight and the elite mutation strategies; and it is firstly proposed to promote the SSA's optimization accuracy and stability without reducing the SSA's speed. The CSSA is utilized to optimize the popular multi-class segmentation algorithm-Multiple Entropy Thresholding (MET). By taking three METs as objective functions, i.e., Kapur Entropy, Minimum-cross Entropy and Renyi Entropy, the CSSA is implemented to quickly and automatically calculate the extreme value of the function and get the corresponding correct thresholds. The image adaptive multi-class segmentation model is called CSSA-MET. In order to comprehensively evaluate it, a new parameter I based on the segmentation accuracy and processing speed is constructed. The results reveal that the CSSA outperforms the other seven methods of optimization performance, as well as the quality evaluation of aggregate images segmented by the CSSA-MET, and the speed and accuracy are balanced. In particular, the highest I value can be obtained when the CSSA is applied to optimize the Renyi Entropy, which indicates that this combination is more suitable for segmenting the aggregate images.

Blockchain Based Financial Portfolio Management Using A3C (A3C를 활용한 블록체인 기반 금융 자산 포트폴리오 관리)

  • Kim, Ju-Bong;Heo, Joo-Seong;Lim, Hyun-Kyo;Kwon, Do-Hyung;Han, Youn-Hee
    • KIPS Transactions on Computer and Communication Systems
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    • v.8 no.1
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    • pp.17-28
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    • 2019
  • In the financial investment management strategy, the distributed investment selecting and combining various financial assets is called portfolio management theory. In recent years, the blockchain based financial assets, such as cryptocurrencies, have been traded on several well-known exchanges, and an efficient portfolio management approach is required in order for investors to steadily raise their return on investment in cryptocurrencies. On the other hand, deep learning has shown remarkable results in various fields, and research on application of deep reinforcement learning algorithm to portfolio management has begun. In this paper, we propose an efficient financial portfolio investment management method based on Asynchronous Advantage Actor-Critic (A3C), which is a representative asynchronous reinforcement learning algorithm. In addition, since the conventional cross-entropy function can not be applied to portfolio management, we propose a proper method where the existing cross-entropy is modified to fit the portfolio investment method. Finally, we compare the proposed A3C model with the existing reinforcement learning based cryptography portfolio investment algorithm, and prove that the performance of the proposed A3C model is better than the existing one.