The Experts below are selected from a list of 12513 Experts worldwide ranked by ideXlab platform
Marco Archetti - One of the best experts on this subject based on the ideXlab platform.
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Evolutionary Game Theory of growth factor production implications for tumour heterogeneity and resistance to therapies
British Journal of Cancer, 2013Co-Authors: Marco ArchettiAbstract:Evolutionary Game Theory of growth factor production: implications for tumour heterogeneity and resistance to therapies
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Evolutionary Game Theory of growth factor production implications for tumour heterogeneity and resistance to therapies
British Journal of Cancer, 2013Co-Authors: Marco ArchettiAbstract:Tumour heterogeneity is documented for many characters, including the production of growth factors, one of the hallmarks of cancer. What maintains heterogeneity remains an open question that has implications for diagnosis and treatment, as drugs that target growth factors are susceptible to the evolution of resistance. I use Evolutionary Game Theory to model collective interactions between cancer cells, to analyse the dynamics of the production of growth factors and the effect of therapies that reduce their amount. Five types of dynamics are possible, including the coexistence of producer and non-producer cells, depending on the production cost of the growth factor, on its diffusion range and on the degree of synergy of the benefit it confers to the cells. Perturbations of the equilibrium mimicking therapies that target growth factors are effective in reducing the amount of growth factor in the long term only if the reduction is extremely efficient and immediate. Collective interactions within the tumour can maintain heterogeneity for the production of growth factors and explain why therapies like anti-angiogenic drugs and RNA interference that reduce the amount of available growth factors are effective in the short term but often lead to relapse. Alternative strategies for evolutionarily stable treatments are discussed.
Axel Krings - One of the best experts on this subject based on the ideXlab platform.
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dynamic hybrid fault modeling and extended Evolutionary Game Theory for reliability survivability and fault tolerance analyses
IEEE Transactions on Reliability, 2011Co-Authors: Axel KringsAbstract:We introduce a new layered modeling architecture consisting of dynamic hybrid fault modeling and extended Evolutionary Game Theory for reliability, survivability, and fault tolerance analyses. The architecture extends traditional hybrid fault models and their relevant constraints in the Agreement algorithms with survival analysis, and Evolutionary Game Theory. The dynamic hybrid fault modeling (i) transforms hybrid fault models into time- and covariate-dependent models; (ii) makes real-time prediction of reliability more realistic, and allows for real-time prediction of fault-tolerance; (iii) sets the foundation for integrating hybrid fault models with reliability and survivability analyses by integrating them with Evolutionary Game modeling; and (iv) extends Evolutionary Game Theory by stochastically modeling the survival (or fitness) and behavior of `Game players.' To analyse survivability, we extend dynamic hybrid fault modeling with a third-layer, operational level modeling, to develop the three-layer survivability analysis approach (dynamic hybrid fault modeling constitutes the tactical and strategic levels). From the perspective of Evolutionary Game modeling, the two mathematical fields, i.e., survival analysis and agreement algorithms, which we applied for developing dynamic hybrid fault modeling, can also be utilized to extend the power of Evolutionary Game Theory in modeling complex engineering, biological (ecological), and social systems. Indeed, a common property of the areas where our extensions to Evolutionary Game Theory can be advantageous is that the risk analysis and management are a core issue. Survival analysis (including competing risks analysis, and multivariate survival analysis) offers powerful modeling tools to analyse time-, space-, and/or covariate-dependent uncertainty, vulnerability, and/or frailty which `Game players' may experience. The agreement algorithms, which are not limited to the agreement algorithms from distributed computing, when applied to extend Evolutionary Game modeling, can be any problem (Game system) specific rules (algorithms or models) that can be utilized to dynamically check the consensus among Game players. We expect that the modeling architecture and approaches discussed in the study should be implemented as a software environment to deal with the necessary sophistication. Evolutionary computing should be particularly convenient to serve as the core optimization engine, and should simplify the implementation. Accordingly, a brief discussion on the software architecture is presented.
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dynamic hybrid fault modeling and extended Evolutionary Game Theory for reliability survivability and fault tolerance analyses
IEEE Transactions on Reliability, 2011Co-Authors: Axel KringsAbstract:We introduce a new layered modeling architecture consisting of dynamic hybrid fault modeling and extended Evolutionary Game Theory for reliability, survivability, and fault tolerance analyses. The architecture extends traditional hybrid fault models and their relevant constraints in the Agreement algorithms with survival analysis, and Evolutionary Game Theory. The dynamic hybrid fault modeling (i) transforms hybrid fault models into time- and covariate-dependent models; (ii) makes real-time prediction of reliability more realistic, and allows for real-time prediction of fault-tolerance; (iii) sets the foundation for integrating hybrid fault models with reliability and survivability analyses by integrating them with Evolutionary Game modeling; and (iv) extends Evolutionary Game Theory by stochastically modeling the survival (or fitness) and behavior of `Game players.' To analyse survivability, we extend dynamic hybrid fault modeling with a third-layer, operational level modeling, to develop the three-layer survivability analysis approach (dynamic hybrid fault modeling constitutes the tactical and strategic levels). From the perspective of Evolutionary Game modeling, the two mathematical fields, i.e., survival analysis and agreement algorithms, which we applied for developing dynamic hybrid fault modeling, can also be utilized to extend the power of Evolutionary Game Theory in modeling complex engineering, biological (ecological), and social systems. Indeed, a common property of the areas where our extensions to Evolutionary Game Theory can be advantageous is that the risk analysis and management are a core issue. Survival analysis (including competing risks analysis, and multivariate survival analysis) offers powerful modeling tools to analyse time-, space-, and/or covariate-dependent uncertainty, vulnerability, and/or frailty which `Game players' may experience. The agreement algorithms, which are not limited to the agreement algorithms from distributed computing, when applied to extend Evolutionary Game modeling, can be any problem (Game system) specific rules (algorithms or models) that can be utilized to dynamically check the consensus among Game players. We expect that the modeling architecture and approaches discussed in the study should be implemented as a software environment to deal with the necessary sophistication. Evolutionary computing should be particularly convenient to serve as the core optimization engine, and should simplify the implementation. Accordingly, a brief discussion on the software architecture is presented.
Antonios Tsourdos - One of the best experts on this subject based on the ideXlab platform.
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an enhanced particle swarm optimization method integrated with Evolutionary Game Theory
IEEE Transactions on Games, 2018Co-Authors: Cedric Leboucher, Hyosang Shin, Stephane Le Menec, Rachid Chelouah, Patrick Siarry, Antonios Tsourdos, Mathias Formoso, Alexandre KotenkoffAbstract:This paper describes a novel particle swarm optimizer algorithm. The focus of this study is how to improve the performance of the classical particle swarm optimization approach, i.e., how to enhance its convergence speed and capacity to solve complex problems while reducing the computational load. The proposed approach is based on an improvement of particle swarm optimization using Evolutionary Game Theory. This method maintains the capability of the particle swarm optimizer to diversify the particles’ exploration in the solution space. Moreover, the proposed approach provides an important ability to the optimization algorithm, that is, adaptation of the search direction, which improves the quality of the particles based on their experience. The proposed algorithm is tested on a representative set of continuous benchmark optimization problems and compared with some other classical optimization approaches. Based on the test results of each benchmark problem, its performance is analyzed and discussed.
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convergence proof of an enhanced particle swarm optimisation method integrated with Evolutionary Game Theory
Information Sciences, 2016Co-Authors: Cedric Leboucher, Hyosang Shin, Stephane Le Menec, Rachid Chelouah, Patrick Siarry, Antonios TsourdosAbstract:This paper proposes an enhanced Particle Swarm Optimisation (PSO) algorithm and examines its performance. In the proposed PSO approach, PSO is combined with Evolutionary Game Theory to improve convergence. One of the main challenges of such stochastic optimisation algorithms is the difficulty in the theoretical analysis of the convergence and performance. Therefore, this paper analytically investigates the convergence and performance of the proposed PSO algorithm. The analysis results show that convergence speed of the proposed PSO is superior to that of the Standard PSO approach. This paper also develops another algorithm combining the proposed PSO with the Standard PSO algorithm to mitigate the potential premature convergence issue in the proposed PSO algorithm. The combined approach consists of two types of particles, one follows Standard PSO and the other follows the proposed PSO. This enables exploitation of both diversification of the particles' exploration and adaptation of the search direction.
Cedric Leboucher - One of the best experts on this subject based on the ideXlab platform.
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an enhanced particle swarm optimization method integrated with Evolutionary Game Theory
IEEE Transactions on Games, 2018Co-Authors: Cedric Leboucher, Hyosang Shin, Stephane Le Menec, Rachid Chelouah, Patrick Siarry, Antonios Tsourdos, Mathias Formoso, Alexandre KotenkoffAbstract:This paper describes a novel particle swarm optimizer algorithm. The focus of this study is how to improve the performance of the classical particle swarm optimization approach, i.e., how to enhance its convergence speed and capacity to solve complex problems while reducing the computational load. The proposed approach is based on an improvement of particle swarm optimization using Evolutionary Game Theory. This method maintains the capability of the particle swarm optimizer to diversify the particles’ exploration in the solution space. Moreover, the proposed approach provides an important ability to the optimization algorithm, that is, adaptation of the search direction, which improves the quality of the particles based on their experience. The proposed algorithm is tested on a representative set of continuous benchmark optimization problems and compared with some other classical optimization approaches. Based on the test results of each benchmark problem, its performance is analyzed and discussed.
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convergence proof of an enhanced particle swarm optimisation method integrated with Evolutionary Game Theory
Information Sciences, 2016Co-Authors: Cedric Leboucher, Hyosang Shin, Stephane Le Menec, Rachid Chelouah, Patrick Siarry, Antonios TsourdosAbstract:This paper proposes an enhanced Particle Swarm Optimisation (PSO) algorithm and examines its performance. In the proposed PSO approach, PSO is combined with Evolutionary Game Theory to improve convergence. One of the main challenges of such stochastic optimisation algorithms is the difficulty in the theoretical analysis of the convergence and performance. Therefore, this paper analytically investigates the convergence and performance of the proposed PSO algorithm. The analysis results show that convergence speed of the proposed PSO is superior to that of the Standard PSO approach. This paper also develops another algorithm combining the proposed PSO with the Standard PSO algorithm to mitigate the potential premature convergence issue in the proposed PSO algorithm. The combined approach consists of two types of particles, one follows Standard PSO and the other follows the proposed PSO. This enables exploitation of both diversification of the particles' exploration and adaptation of the search direction.
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a swarm intelligence method combined to Evolutionary Game Theory applied to the resources allocation problem
International Journal of Swarm Intelligence Research, 2012Co-Authors: Cedric Leboucher, Rachid Chelouah, Patrick Siarry, Stephane Le MenecAbstract:This paper addresses an allocation problem and proposes a solution using a swarm intelligence method. The application of swarm intelligence has to be discrete. This allocation problem can be modelled as a multi-objective optimization problem where the authors minimize the time and the distance of the total travel in a logistic context. This study uses a hybrid Discrete Particle Swarm Optimization DPSO method combined to Evolutionary Game Theory EGT. One of the main implementation issues of DPSO is the choice of inertial, individual, and social coefficients. In order to resolve this problem, those coefficients are optimised by using a dynamical approach based on EGT. The strategies are either to keep going with only inertia, only with individual, or only with social coefficients. Since the optimal strategy is usually a mixture of the three, the fitness of the swarm can be maximized when an optimal rate for each coefficient is obtained. Evolutionary Game Theory studies the behaviour of large populations of agents who repeatedly engage in strategic interactions. Changes in behaviour in these populations are driven by natural selection via differences in birth and death rates. To test this algorithm, the authors create a problem whose solution is already known. This study checks whether this adapted DPSO method succeeds in providing an optimal solution for general allocation problems.
Mohsen Guizani - One of the best experts on this subject based on the ideXlab platform.
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evaluating reputation management schemes of internet of vehicles based on Evolutionary Game Theory
IEEE Transactions on Vehicular Technology, 2019Co-Authors: Zhihong Tian, Xiangsong Gao, Jing Qiu, Mohsen GuizaniAbstract:Conducting reputation management is very important for Internet of vehicles. However, most of the existing work evaluate the effectiveness of their schemes with settled attacking behaviors in their simulation, which cannot represent the real scenarios. In this paper, we propose to consider dynamical and diversity attacking strategies in the simulation of reputation management scheme evaluation. To that end, we apply Evolutionary Game Theory to model the evolution process of malicious users’ attacking strategies, and discuss the methodology of the evaluation simulations. We further apply our evaluation method to a reputation management scheme with multiple utility functions, and discuss the evaluation results. The results indicate that our evaluation method is able to depict the evolving process of the dynamic attacking strategies in a vehicular network, and the final state of the simulation could be used to quantify the protection effectiveness of the reputation management scheme.
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evaluating reputation management schemes of internet of vehicles based on Evolutionary Game Theory
arXiv: Computer Science and Game Theory, 2019Co-Authors: Zhihong Tian, Xiangsong Gao, Jing Qiu, Mohsen GuizaniAbstract:Conducting reputation management is very important for Internet of vehicles. However, most of the existing researches evaluate the effectiveness of their schemes with settled attacking behaviors in their simulation which cannot represent the scenarios in reality. In this paper, we propose to consider dynamical and diversity attacking strategies in the simulation of reputation management scheme evaluation. To that end, we apply Evolutionary Game Theory to model the evolution process of malicious users' attacking strategies, and discuss the methodology of the evaluation simulations. We further apply our evaluation method to a reputation management scheme with multiple utility functions, and discuss the evaluation results. The results indicate that our evaluation method is able to depict the evolving process of the dynamic attacking strategies in a vehicular network, and the final state of the simulation could be used to quantify the protection effectiveness of the reputation management scheme.
