Selection of Optimal Sub-ensembles of Classifiers through Evolutionary Game Theory

Author(s):  
Hadjer Ykhlef ◽  
Djamel Bouchaffra ◽  
Farid Ykhlef
2021 ◽  
Vol 2 (3) ◽  
pp. 111-119
Author(s):  
Caglar Koca ◽  
Meltem Civas ◽  
Ozgur B. Akan

Molecular Communication (MC) is an emerging technology using molecules to transfer information between nanomachines. In this paper, we approach the resource allocation problem in Molecular Nano-networks (MCN) from the perspective of evolutionary game theory. In particular, we consider an MCN as an organism having three types of nodes acting as a sensor, relay, and sink, respectively. The resources are distributed among the nodes according to an evolutionary process, which relies on the selection of the most successful organisms followed by creating their offspring iteratively. In this regard, the success of an organism is measured by the total number of dropped messages during its life cycle. To illustrate the evolution procedure, we design a toy problem, and then solve it analytically and using the evolution approach for comparison. We further simulate the performance of the evolution approach on randomly generated organisms. The results reveal the potential of evolutionary game theory tools to improve the transmission performance of MCNs.


2021 ◽  
Vol 275 ◽  
pp. 03022
Author(s):  
Siyuan Deng

Franchised store chain is the most popular business model today. The franchisor and the franchisees share the same brand, but the value of the entire brand will be degraded once one side pursues self-interests in brand management. From the perspective of franchised store chain, this paper develops an evolutionary game model between franchisor and franchisees under the assumption of bounded rationality. The strategic selection of franchisor and franchisees includes cooperation and no-cooperation. In the end, the corresponding policy recommendations are proposed in the foundation of case analysis.


2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Zhu Bai ◽  
Mingxia Huang ◽  
Shuai Bian ◽  
Huandong Wu

The emergence of online car-hailing service provides an innovative approach to vehicle booking but has negatively influenced the taxi industry in China. This paper modeled taxi service mode choice based on evolutionary game theory (EGT). The modes included the dispatching and online car-hailing modes. We constructed an EGT framework, including determining the strategies and the payoff matrix. We introduced different behaviors, including taxi company management, driver operation, and passenger choice. This allowed us to model the impact of these behaviors on the evolving process of service mode choice. The results show that adjustments in taxi company, driver, and passenger behaviors impact the evolutionary path and convergence speed of our evolutionary game model. However, it also reveals that, regardless of adjustments, the stable states in the game model remain unchanged. The conclusion provides a basis for studying taxi system operation and management.


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