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Journal articles on the topic 'Competitive algorithms'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Im, Sungjin, Janardhan Kulkarni, and Kamesh Munagala. "Competitive Algorithms from Competitive Equilibria." Journal of the ACM 65, no. 1 (2018): 1–33. http://dx.doi.org/10.1145/3136754.

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Bender, Michael A., Jeremy T. Fineman, Mahnush Movahedi, et al. "Resource-Competitive Algorithms." ACM SIGACT News 46, no. 3 (2015): 57–71. http://dx.doi.org/10.1145/2818936.2818949.

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3

Fiat, Amos, Richard M. Karp, Michael Luby, Lyle A. McGeoch, Daniel D. Sleator, and Neal E. Young. "Competitive paging algorithms." Journal of Algorithms 12, no. 4 (1991): 685–99. http://dx.doi.org/10.1016/0196-6774(91)90041-v.

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Budura, Georgeta, Corina Botoca, and Nicolae Miclău. "Competitive learning algorithms for data clustering." Facta universitatis - series: Electronics and Energetics 19, no. 2 (2006): 261–69. http://dx.doi.org/10.2298/fuee0602261b.

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This paper presents and discusses some competitive learning algorithms for data clustering. A new competitive learning algorithm, named the dynamically penalized rival competitive learning algorithm (DPRCL), is introduced and studied. It is a variant of the rival penalized competitive algorithm [1] and it performs appropriate clustering without knowing the clusters number, by automatically driving the extra seed points far away from the input data set. It does not have the 'dead units' problem. Simulations results, performed in different conditions, are presented showing that the performance o

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LIU, ZHI-QIANG, and YAJUN ZHANG. "COMPENSATION COMPETITIVE LEARNING." International Journal of Computational Intelligence and Applications 01, no. 03 (2001): 303–22. http://dx.doi.org/10.1142/s1469026801000263.

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In general, in competitive learning the requirement for the initial number of prototypes is a difficult task, as we do not usually know the number of clusters in the input data a priori. The behavior and performance of the competitive algorithms are very sensitive to the initial locations and number of the prototypes. In this paper after investigating several important competitive learning paradigms, we present compensation techniques for overcoming the problems in competitive learning. Our experimental results show that competition with compensation can improve the performance of the learning

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Wu, Yonghua, Guohun Zhu, Huaying Chen, and Jucun Qin. "WIN Algorithm for Discrete Online TSP." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 9 (2011): 1199–202. http://dx.doi.org/10.20965/jaciii.2011.p1199.

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Traveling Salesman Problem (TSP) which is proved as an NP-Complete problem is solved by many algorithms. In this paper, we propose online TSP which is based on general discrete metric space. A Waiting-If-Necessary (WIN) algorithm is proposed that involves with increasing cost caused by zealous algorithms and unnecessary waiting caused by cautious algorithms. We measure the performance of the WIN algorithm using competitive analysis and found that it is a 2-competitive algorithm. The competitive ratio of theWIN algorithm can be improved by setting parameterT0.

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Fiat, Amos, Yuval Rabani, and Yiftach Ravid. "Competitive k-server algorithms." Journal of Computer and System Sciences 48, no. 3 (1994): 410–28. http://dx.doi.org/10.1016/s0022-0000(05)80060-1.

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Majd, Amin, Golnaz Sahebi, Masoud Daneshtalab, Juha Plosila, Shahriar Lotfi, and Hannu Tenhunen. "Parallel imperialist competitive algorithms." Concurrency and Computation: Practice and Experience 30, no. 7 (2018): e4393. http://dx.doi.org/10.1002/cpe.4393.

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Osman, Hossam, and Moustafa M. Fahmy. "Probabilistic Winner-Take-All Learning Algorithm for Radial-Basis-Function Neural Classifiers." Neural Computation 6, no. 5 (1994): 927–43. http://dx.doi.org/10.1162/neco.1994.6.5.927.

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This paper proposes a new adaptive competitive learning algorithm called “the probabilistic winner-take-all.” The algorithm is based on a learning scheme developed by Agrawala within the statistical pattern recognition literature (Agrawala 1970). Its name stems from the fact that for a given input pattern once each competitor computes the probability of being the one that generated this pattern, the computed probabilities are utilized to probabilistically choose a winner. Then, only this winner is permitted to learn. The learning rule of the algorithm is derived for three different cases. Its

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Mohapatra, Prabhujit, Kedar Nath Das, Santanu Roy, Ram Kumar, and Nilanjan Dey. "A Novel Multi-Objective Competitive Swarm Optimization Algorithm." International Journal of Applied Metaheuristic Computing 11, no. 4 (2020): 114–29. http://dx.doi.org/10.4018/ijamc.2020100106.

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In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner parti

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Huang, Jian, and Yijun Gu. "Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity." Applied Sciences 13, no. 18 (2023): 10496. http://dx.doi.org/10.3390/app131810496.

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Community detection is an important task in the analysis of complex networks, which is significant for mining and analyzing the organization and function of networks. As an unsupervised learning algorithm based on the particle competition mechanism, stochastic competitive learning has been applied in the field of community detection in complex networks, but still has several limitations. In order to improve the stability and accuracy of stochastic competitive learning and solve the problem of community detection, we propose an unsupervised community detection algorithm LNSSCL (Local Node Simil

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12

Lee, Russell, Jessica Maghakian, Mohammad Hajiesmaili, Jian Li, Ramesh Sitaraman, and Zhenhua Liu. "Online peak-aware energy scheduling with untrusted advice." ACM SIGEnergy Energy Informatics Review 1, no. 1 (2021): 59–77. http://dx.doi.org/10.1145/3508467.3508473.

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This paper studies the online energy scheduling problem in a hybrid model where the cost of energy is proportional to both the volume and peak usage, and where energy can be either locally generated or drawn from the grid. Inspired by recent advances in online algorithms with Machine Learned (ML) advice, we develop parameterized deterministic and randomized algorithms for this problem such that the level of reliance on the advice can be adjusted by a trust parameter. We then analyze the performance of the proposed algorithms using two performance metrics: robustness that measures the competiti

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13

Jung, Sung-Hoon. "Competitive Generation for Genetic Algorithms." Journal of Korean Institute of Intelligent Systems 17, no. 1 (2007): 86–93. http://dx.doi.org/10.5391/jkiis.2007.17.1.086.

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Lin, Jun-Lin, Yu-Hsiang Tsai, Chun-Ying Yu, and Meng-Shiou Li. "Interaction Enhanced Imperialist Competitive Algorithms." Algorithms 5, no. 4 (2012): 433–48. http://dx.doi.org/10.3390/a5040433.

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ZHANG, YONG, YUXIN WANG, FRANCIS Y. L. CHIN, and HING-FUNG TING. "COMPETITIVE ALGORITHMS FOR ONLINE PRICING." Discrete Mathematics, Algorithms and Applications 04, no. 02 (2012): 1250015. http://dx.doi.org/10.1142/s1793830912500152.

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Given a seller with m items, a sequence of users {u1, u2, …} come one by one, the seller must set the unit price and assign some items to each user on his/her arrival. Items can be sold fractionally. Each ui has his/her value function vi(⋅) such that vi(x) is the highest unit price ui is willing to pay for x items. The objective is to maximize the revenue by setting the price and number of items for each user. In this paper, we have the following contributions: if the highest value h among all vi(x) is known in advance, we first show the lower bound of the competitive ratio is ⌊ log h⌋/2, then

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Dimou, C. K., and V. K. Koumousis. "Genetic Algorithms in Competitive Environments." Journal of Computing in Civil Engineering 17, no. 3 (2003): 142–49. http://dx.doi.org/10.1061/(asce)0887-3801(2003)17:3(142).

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Manasse, Mark S., Lyle A. McGeoch, and Daniel D. Sleator. "Competitive algorithms for server problems." Journal of Algorithms 11, no. 2 (1990): 208–30. http://dx.doi.org/10.1016/0196-6774(90)90003-w.

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Kumar, Sandeep, and Deepak Garg. "Online Financial Algorithms: Competitive Analysis." International Journal of Computer Applications 40, no. 7 (2012): 8–14. http://dx.doi.org/10.5120/4974-7228.

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Westphal, Stephan, Sven O. Krumke, and Rob van Stee. "Competitive Algorithms for Cottage Rental." Electronic Notes in Discrete Mathematics 25 (August 2006): 187–88. http://dx.doi.org/10.1016/j.endm.2006.06.069.

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Lohn, Jason D., Gregory S. Hornby, and Derek S. Linden. "Human-competitive evolved antennas." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22, no. 3 (2008): 235–47. http://dx.doi.org/10.1017/s0890060408000164.

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AbstractWe present a case study showing a human-competitive design of an evolved antenna that was deployed on a NASA spacecraft in 2006. We were fortunate to develop our antennas in parallel with another group using traditional design methodologies. This allowed us to demonstrate that our techniques were human-competitive because our automatically designed antenna could be directly compared to a human-designed antenna. The antennas described below were evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly polarized wave and w

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Banerjee, Prithu, Wei Chen, and Laks V. S. Lakshmanan. "Maximizing social welfare in a competitive diffusion model." Proceedings of the VLDB Endowment 14, no. 4 (2020): 613–25. http://dx.doi.org/10.14778/3436905.3436920.

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Influence maximization (IM) has garnered a lot of attention in the literature owing to applications such as viral marketing and infection containment. It aims to select a small number of seed users to adopt an item such that adoption propagates to a large number of users in the network. Competitive IM focuses on the propagation of competing items in the network. Existing works on competitive IM have several limitations. (1) They fail to incorporate economic incentives in users' decision making in item adoptions. (2) Majority of the works aim to maximize the adoption of one particular item, and

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22

Elhossini, Ahmed, Shawki Areibi, and Robert Dony. "Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization." Evolutionary Computation 18, no. 1 (2010): 127–56. http://dx.doi.org/10.1162/evco.2010.18.1.18105.

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This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using sever

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23

Miura, Takeshi, Kentaro Sano, Kenichi Suzuki, and Tadao Nakamura. "A Competitive Learning Algorithm with Controlling Maximum Distortion." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 2 (2005): 166–74. http://dx.doi.org/10.20965/jaciii.2005.p0166.

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Vector quantization with an optimal codebook is attractive for lossy data compression. So far, a number of codebook design algorithms have been proposed to minimize the mean square error, MSE. However, these algorithms have a problem that MSE minimization sometimes causes an unacceptable maximum-distortion, which is very important in several applications. This paper proposes a competitive learning algorithm with controlling maximum distortion that designs a codebook giving a maximum distortion within a given error bound. The proposed algorithm assigns a code vector to an input vector with a to

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24

Chen, Cong, Fanxin Wang, Jiayin Pan, Lang Xu, and Hongming Gao. "Algorithm Design for an Online Berth Allocation Problem." Journal of Marine Science and Engineering 12, no. 10 (2024): 1722. http://dx.doi.org/10.3390/jmse12101722.

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In this paper, we investigate an online berth allocation problem, where vessels arrive one by one and their information is revealed upon arrival. Our objective is to design online algorithms to minimize the maximum load of all berths (makespan). We first demonstrate that the widely used Greedy algorithm has a very poor theoretical guarantee; specifically, the competitive ratio of the Greedy algorithm for this problem is lower bounded by Ω(log⁡m/log⁡log⁡m), which increases with the number of berths m. On account of this, we borrow an idea from algorithms for the online strip packing problem and

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Ashlagi, Itai, Brendan Lucier, and Moshe Tennenholtz. "Equilibria of Online Scheduling Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 67–73. http://dx.doi.org/10.1609/aaai.v27i1.8631.

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We describe a model for competitive online scheduling algorithms. Two servers, each with a single observable queue, compete for customers. Upon arrival, each customer strategically chooses the queue with minimal expected wait time. Each scheduler wishes to maximize its number of customers, and can strategically select which scheduling algorithm, such as First-Come-First-Served (FCFS), to use for its queue. This induces a game played by the servers and the customers. We consider a non-Bayesian setting, where servers and customers play to maximize worst-case payoffs. We show that there is a uniq

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26

Jafarzadeh, H., N. Moradinasab, and M. Elyasi. "An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem." Engineering, Technology & Applied Science Research 7, no. 6 (2017): 2260–65. http://dx.doi.org/10.48084/etasr.1570.

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The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clustered set of cities. In this problem, the traveler is interested in finding the best path that goes through all clusters. As this problem is NP-hard, implementing a metaheuristic algorithm to solve the large scale problems is inevitable. The performance of these algorithms can be intensively promoted by other heuristic algorithms. In this study, a search method is developed that improves the quality of the solutions and competition time considerably in comparison with Genetic Algorithm. In the p

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Jafarzadeh, H., N. Moradinasab, and M. Elyasi. "An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem." Engineering, Technology & Applied Science Research 7, no. 6 (2017): 2260–65. https://doi.org/10.5281/zenodo.1118358.

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The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clustered set of cities. In this problem, the traveler is interested in finding the best path that goes through all clusters. As this problem is NP-hard, implementing a metaheuristic algorithm to solve the large scale problems is inevitable. The performance of these algorithms can be intensively promoted by other heuristic algorithms. In this study, a search method is developed that improves the quality of the solutions and competition time considerably in comparison with Genetic Algorithm. In the p

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Zhou, Junyi, Haowen Zheng, Shaole Li, et al. "A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection." Applied Sciences 14, no. 11 (2024): 4501. http://dx.doi.org/10.3390/app14114501.

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In real-world applications, feature selection is crucial for enhancing the performance of data science and machine learning models. Typically, feature selection is a complex combinatorial optimization problem and a multi-objective optimization problem. Its primary goals are to reduce the dimensionality of the dataset and enhance the performance of machine learning algorithms. The selection of features in high-dimensional datasets is challenging due to the intricate relationships between features, which pose significant challenges to the performance and computational efficiency of algorithms. T

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Yang, Lin, Ali Zeynali, Mohammad H. Hajiesmaili, Ramesh K. Sitaraman, and Don Towsley. "Competitive Algorithms for Online Multidimensional Knapsack Problems." Proceedings of the ACM on Measurement and Analysis of Computing Systems 5, no. 3 (2021): 1–30. http://dx.doi.org/10.1145/3491042.

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In this paper, we study the online multidimensional knapsack problem (called OMdKP) in which there is a knapsack whose capacity is represented in m dimensions, each dimension could have a different capacity. Then, n items with different scalar profit values and m-dimensional weights arrive in an online manner and the goal is to admit or decline items upon their arrival such that the total profit obtained by admitted items is maximized and the capacity of knapsack across all dimensions is respected. This is a natural generalization of the classic single-dimension knapsack problem and finds seve

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Ma, Hang. "A Competitive Analysis of Online Multi-Agent Path Finding." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 234–42. http://dx.doi.org/10.1609/icaps.v31i1.15967.

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We study online Multi-Agent Path Finding (MAPF), where new agents are constantly revealed over time and all agents must find collision-free paths to their given goal locations. We generalize existing complexity results of (offline) MAPF to online MAPF. We classify online MAPF algorithms into different categories based on (1) controllability (the set of agents that they can plan paths for at each time) and (2) rationality (the quality of paths they plan) and study the relationships between them. We perform a competitive analysis for each category of online MAPF algorithms with respect to common

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Lykouris, Thodoris, and Sergei Vassilvitskii. "Competitive Caching with Machine Learned Advice." Journal of the ACM 68, no. 4 (2021): 1–25. http://dx.doi.org/10.1145/3447579.

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Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution, as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work, we develop a framework for augmenting online algorithms with a machine learned predictor to achieve competitive ratios that provably improve upon uncondition

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Bauer, V. P., and V. V. Smirnov. "Institutional Features of the Development of Competitive Cryptocurrency." Finance: Theory and Practice 24, no. 5 (2020): 84–99. http://dx.doi.org/10.26794/2587-5671-2020-24-5-84-99.

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The aim of the article is to clarify the basics of the digitalization strategy of the competitive businesses and identify features of the institutional environment that ensure the development of cryptocurrency as a new asset (IT product) of the modern economy, analyze the methods of implementing the cryptocurrency business models. The relevance of the research paper is determined by the need to develop a competitive Russian cryptocurrency (including the cryptoruble) with the growing private, state and cross-national cryptocurrencies. The scientific novelty of the study implies clarifying the i

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Mazinani, S. M., J. Chitizadeh, M. H. Yaghmaee, M. T. Honary, and F. Tashtarian. "NEW CLUSTERING SCHEMES FOR WIRELESS SENSOR NETWORKS." IIUM Engineering Journal 11, no. 1 (2010): 51–69. http://dx.doi.org/10.31436/iiumej.v11i1.39.

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In this paper, two clustering algorithms are proposed. In the first one, we investigate a clustering protocol for single hop wireless sensor networks that employs a competitive scheme for cluster head selection. The proposed algorithm is named EECS-M that is a modified version to the well known protocol EECS where some of the nodes become volunteers to be cluster heads with an equal probability.Â In the competition phase in contrast to EECS using a fixed competition range for any volunteer node, we assign a variable competition range to it that is related to its distance to base station. The v

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Chen, Lin, Deshi Ye, and Guochuan Zhang. "Approximating the Optimal Algorithm for Online Scheduling Problems via Dynamic Programming." Asia-Pacific Journal of Operational Research 32, no. 01 (2015): 1540011. http://dx.doi.org/10.1142/s0217595915400114.

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Very recently Günther et al. [E. Günther, O. Maurer, N. Megow and A. Wiese (2013). A new approach to online scheduling: Approximating the optimal competitive ratio. In Proc. 24th Annual ACM-SIAM Symp. Discrete Algorithms (SODA).] initiate a new systematic way of studying online problems by introducing the competitive ratio approximation scheme (simplified as competitive schemes in this paper), which is a class of algorithms {Aϵ|ϵ > 0} with a competitive ratio at most ρ*(1 + ϵ), where ρ* is the best possible competitive ratio over all online algorithms. Along this line, Günther et al. [E. Gü

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Harifi, Sasan, Faraz Davachi, Narges Mohammadi, and Soheil FaridMohammadzadegan. "Two competitive hybridization approaches based on combining of Giza Pyramids Construction with Particle Swarm Optimization for solving global optimization problems." Inteligencia Artificial 28, no. 75 (2025): 114–39. https://doi.org/10.4114/intartif.vol28iss75pp114-139.

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Optimization problems are complex problems that are very difficult to solve. Although these types of problems are solved in the real world using exact methods, these methods are very time-consuming and costly. By using soft computing methods, the time and cost of problem-solving can be reduced to some extent. Engineering problems are among the complex real-world problems that can be solved through soft computing methods. One of these methods is the use of metaheuristic algorithms to optimize the solution of these types of problems. The Particle Swarm Optimization (PSO) algorithm is a common an

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Li, Yang, and Zhiqiang Wang. "A Study of Delayed Competitive Influence Propagation Based on Shortest Path Calculation." Information 15, no. 7 (2024): 370. http://dx.doi.org/10.3390/info15070370.

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In social network applications, competitive influence propagation often exhibits a certain degree of time lag. In the scenario of positive and negative competitive propagation studied in this paper, under the premise that negative nodes are activated first, how to find a set of positive seed nodes to participate in competitive propagation is studied, aiming to minimize the spread of negative influence. In the current study, the time complexity of the improved algorithms based on greedy strategies is high, which limits their scope of application in practical scenarios; some heuristic algorithms

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Shirali, Nooshin, and Marjan Abdeyazdan. "An Imperialist Competitive Algorithm for Persian Text Segmentation." Ciência e Natura 37 (December 19, 2015): 247. http://dx.doi.org/10.5902/2179460x20780.

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Segmentation has been used in different natural language processing tasks, such as information retrieval and text summarization. In this paper a novel Persian text segmentation algorithm is proposed. Our proposed algorithm applies the imperialist competitive algorithm (ICA) to find the optimal topic boundaries. It is the first time that an evolutionary algorithm applies in Persian text segmentation. The experimental results show that proposed algorithm is more accurate than other Persian text segmentation algorithms.

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Ben-Aroya, Avraham, and Sivan Toledo. "Competitive analysis of flash memory algorithms." ACM Transactions on Algorithms 7, no. 2 (2011): 1–37. http://dx.doi.org/10.1145/1921659.1921669.

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Bansal, Nikhil, Niv Buchbinder, and Joseph (Seffi) Naor. "Randomized Competitive Algorithms for Generalized Caching." SIAM Journal on Computing 41, no. 2 (2012): 391–414. http://dx.doi.org/10.1137/090779000.

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Fiat, Amos, Dean P. Foster, Howard Karloff, Yuval Rabani, Yiftach Ravid, and Sundar Vishwanathan. "Competitive Algorithms for Layered Graph Traversal." SIAM Journal on Computing 28, no. 2 (1998): 447–62. http://dx.doi.org/10.1137/s0097539795279943.

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Achlioptas, Dimitris, Marek Chrobak, and John Noga. "Competitive analysis of randomized paging algorithms." Theoretical Computer Science 234, no. 1-2 (2000): 203–18. http://dx.doi.org/10.1016/s0304-3975(98)00116-9.

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Ahalt, Stanley C., Ashok K. Krishnamurthy, Prakoon Chen, and Douglas E. Melton. "Competitive learning algorithms for vector quantization." Neural Networks 3, no. 3 (1990): 277–90. http://dx.doi.org/10.1016/0893-6080(90)90071-r.

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Hopf, Michael, Clemens Thielen, and Oliver Wendt. "Competitive algorithms for multistage online scheduling." European Journal of Operational Research 260, no. 2 (2017): 468–81. http://dx.doi.org/10.1016/j.ejor.2016.12.047.

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Bansal, Nikhil, Ho-Leung Chan, and Kirk Pruhs. "Competitive Algorithms for Due Date Scheduling." Algorithmica 59, no. 4 (2009): 569–82. http://dx.doi.org/10.1007/s00453-009-9321-4.

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Card, H. C., G. K. Rosendahl, D. K. McNeill, and R. D. McLeod. "Competitive learning algorithms and neurocomputer architecture." IEEE Transactions on Computers 47, no. 8 (1998): 847–58. http://dx.doi.org/10.1109/12.707586.

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Bartal, Y., A. Fiat, and Y. Rabani. "Competitive Algorithms for Distributed Data Management." Journal of Computer and System Sciences 51, no. 3 (1995): 341–58. http://dx.doi.org/10.1006/jcss.1995.1073.

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Karlin, A. R., M. S. Manasse, L. A. McGeoch, and S. Owicki. "Competitive randomized algorithms for nonuniform problems." Algorithmica 11, no. 6 (1994): 542–71. http://dx.doi.org/10.1007/bf01189993.

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48

Brown, Zach Y., and Alexander MacKay. "Competition in Pricing Algorithms." American Economic Journal: Microeconomics 15, no. 2 (2023): 109–56. http://dx.doi.org/10.1257/mic.20210158.

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We document new facts about pricing technology using high-frequency data, and we examine the implications for competition. Some online retailers employ technology that allows for more frequent price changes and automated responses to price changes by rivals. Motivated by these facts, we consider a model in which firms can differ in pricing frequency and choose pricing algorithms that are a function of rivals’ prices. In competitive (Markov perfect) equilibrium, the introduction of simple pricing algorithms can increase price levels, generate price dispersion, and exacerbate the price effects o

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Siuly, Yan Li, and Peng Wen. "COMPARISONS BETWEEN MOTOR AREA EEG AND ALL-CHANNELS EEG FOR TWO ALGORITHMS IN MOTOR IMAGERY TASK CLASSIFICATION." Biomedical Engineering: Applications, Basis and Communications 26, no. 03 (2014): 1450040. http://dx.doi.org/10.4015/s1016237214500409.

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This article reports on a comparative study to identify electroencephalography (EEG) signals during motor imagery (MI) for motor area EEG and all-channels EEG in the brain–computer interface (BCI) application. In this paper, we present two algorithms: CC-LS-SVM and CC-LR for MI tasks classification. The CC-LS-SVM algorithm combines the cross-correlation (CC) technique and the least square support vector machine (LS-SVM). The CC-LR algorithm assembles the CC technique and binary logistic regression (LR) model. These two algorithms are implemented on the motor area EEG and the all-channels EEG t

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Demidova, Liliya, and Nikita Moroshkin. "Optimization of regular expressions using competitive coevolutionary algorithm based on symbolic regression." ITM Web of Conferences 72 (2025): 05005. https://doi.org/10.1051/itmconf/20257205005.

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The paper presents an algorithm for optimizing the structure of regular expressions of the Python programming language dialect of the re module. The optimization algorithm is implemented as a competitive coevolution algorithm based on the symbolic regression algorithm (the Gene Expression Programming algorithm will be used as an implementation of the symbolic regression algorithm). The paper proposes a pseudocode for the regular expression optimization algorithm as an abstract “black box” model, provides hyperparameters of competitive coevolution, as well as a function for assessing the suitab

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