Evolutionary Games in Two-Layer Networks with the Introduction of Dominant Strategy

We study evolutionary games in two-layer networks by introducing the correlation between two layers through the C-dominance or the D-dominance. We assume that individuals play prisoner＇s dilemma game （PDG） in one layer and snowdrift game （SDG） in the other. We explore the dependences of the fraction of the strategy cooperation in different layers on the game parameter and initial conditions. The results on two-layer square lattices show that, when cooperation is the dominant strategy, initial conditions strongly influence cooperation in the PDG layer while have no impact in the SDG layer. Moreover, in contrast to the result for PDG in single-layer square lattices, the parameter regime where cooperation could be maintained expands significantly in the PDG layer. We also investigate the effects of mutation and network topology. We find that different mutation rates do not change the cooperation behaviors. Moreover, similar behaviors on cooperation could be found in two-layer random networks.

A Matrix Approach to the Modeling and Analysis of Networked Evolutionary Games With Time Delays

Using the semi-tensor product method, this paper investigates the modeling and analysis of networked evolutionary games(NEGs) with finite memories, and presents a number of new results. Firstly, a kind of algebraic expression is formulated for the networked evolutionary games with finite memories, based on which the behavior of the corresponding evolutionary game is analyzed. Secondly, under a proper assumption, the existence of Nash equilibrium of the given networked evolutionary games is proved and a free-type strategy sequence is designed for the convergence to the Nash equilibrium. Finally, an illustrative example is worked out to support the obtained new results.

Evolutionary GAN–Based Data Augmentation for Cardiac Magnetic Resonance Image

Generative adversarial networks(GANs)have considerable potential to alleviate challenges linked to data scarcity.Recent research has demonstrated the good performance of this method for data augmentation because GANs synthesize semantically meaningful data from standard signal distribution.The goal of this study was to solve the overfitting problem that is caused by the training process of convolution networks with a small dataset.In this context,we propose a data augmentation method based on an evolutionary generative adversarial network for cardiac magnetic resonance images to extend the training data.In our structure of the evolutionary GAN,the most optimal generator is chosen that considers the quality and diversity of generated images simultaneously from many generator mutations.Also,to expand the distribution of the whole training set,we combine the linear interpolation of eigenvectors to synthesize new training samples and synthesize related linear interpolation labels.This approach makes the discrete sample space become continuous and improves the smoothness between domains.The visual quality of the augmented cardiac magnetic resonance images is improved by our proposed method as shown by the data-augmented experiments.In addition,the effectiveness of our proposed method is verified by the classification experiments.The influence of the proportion of synthesized samples on the classification results of cardiac magnetic resonance images is also explored.

Smart Service System(SSS)：A Novel Architecture Enabling Coordination of Heterogeneous Networking Technologies and Devices for Internet of Things

In Internet of Things(IoT), the devices or terminals are connected with each other, which can be very diverse over the wireless networks. Unfortunately, the current devices are not designed to communicate with the collocated devices which employ different communication technologies. Consequently, the communication between these devices will be realized only by using the gateway nodes. This will cause the inefficient use of wireless resources. Therefore, in this paper, a smart service system(SSS) architecture is proposed, which consists of smart service terminal(SST), and smart service network(SSN), to realize the Io T in a general environment with diverse communication networks, devices, and services. The proposed architecture has the following advantages: i) the devices in this architecture cover multiple types of terminals and sensor-actuator devices; ii) the communications network therein is a converged network, and will coordinate multiple kinds of existing and emerging networks. This converged network offers ubiquitous access for various sensors and terminals; iii) the architecture has services and applications covering all smart service areas. It also provides theadaptability to new services and applications. A SSS architecture-based smart campus system was developed and deployed. Evaluation experiments of the proposed smart campus system demonstrate the SSS's advantages over the existing counterparts, and verify the effectiveness of the proposed architecture.

Neuroevolution-enabled adaptation of the Jacobi method for Poisson’s equation with density discontinuities

Lacking labeled examples of working numerical strategies,adapting an iterative solver to accommodate a numerical issue,e.g.,density discontinuities in the pressure Poisson equation,is non-trivial and usually involves a lot of trial and error.Here,we resort to evolutionary neural network.A evolutionary neural network observes the outcome of an action and adapts its strategy accordingly.The process requires no labeled data but only a measure of a network’s performance at a task.Applying neuro-evolution and adapting the Jacobi iterative method for the pressure Poisson equation with density discontinuities,we show that the adapted Jacobi method is able to accommodate density discontinuities.

Stability Analysis of Networked Evolutionary Games with Profile-Dependent Delays

This paper investigates the networked evolutionary games(NEGs)with profile-dependent delays,including modeling and stability analysis.Profile-dependent delay,which varies with the game profiles,slows the information transmission between participants.Firstly,the dynamics model is proposed for the profile-dependent delayed NEG,then the algebraic formulation is established using the algebraic state space approach.Secondly,the dynamic behavior of the game is discussed,involving general stability and evolutionarily stable profile analysis.Necessary and sufficient criteria are derived using the matrices,which can be easily verified by mathematical software.Finally,a numerical example is carried out to demonstrate the validity of the theoretical results.

Dynamic Self-Similar k_(c)-Center Network Based on Information Dissemination

This study mainly focused on the dynamic self-similar k_(c)-center network as a result of information distribution through social networks.Individual attraction with various preferences was characterized in the model as a result of reciprocal attraction among individuals and human multi-attribute.Additionally,the model incorporated the community network structure and network evolution mechanism,and a dynamic self-similar k_(c)-center network generation model was presented.Compared with the classical scale-free network generation algorithm,the generated network embodied not only the characteristics of the small-world and scale-free,but also the characteristics of dynamic self-similar k_(c)-center network.The experimental results were verified by comparing the real data with the experimental data.The results showed that there are dynamic self-similar k_(c)-center networks and their internal network relationship dynamics in the micro scale,meso scale and global perspective based on information dissemination.

Semi-tensor product approach to networked evolutionary games

In this paper a comprehensive introduction for modeling and control of networked evolutionary games （NEGs） via semi-tensor product （STP） approach is presented. First, we review the mathematical model of an NEG, which consists of three ingredients： network graph, fundamental network game, and strategy updating rule. Three kinds of network graphs are considered, which are i） undirected graph for symmetric games; ii） directed graph for asymmetric games, and iii） d-directed graph for symmetric games with partial neighborhood information. Three kinds of fundamental evolutionary games （FEGs） are discussed, which are i） two strategies and symmetric （S-2）; ii） two strategies and asymmetric （A-2）; and iii） three strategies and symmetric （S-3）. Three strategy updating rules （SUR） are introduced, which are i） Unconditional Imitation （UI）; ii） Fermi Rule（FR）; iii） Myopic Best Response Adjustment Rule （MBRA）. First, we review the fundamental evolutionary equation （FEE） and use it to construct network profile dynamics （NPD）of NEGs. To show how the dynamics of an NEG can be modeled as a discrete time dynamics within an algebraic state space, the fundamental evolutionary equation （FEE） of each player is discussed. Using FEEs, the network strategy profile dynamics （NSPD） is built by providing efficient algorithms. Finally, we consider three more complicated NEGs： i） NEG with different length historical information, ii） NEG with multi-species, and iii） NEG with time-varying payoffs. In all the cases, formulas are provided to construct the corresponding NSPDs. Using these NSPDs, certain properties are explored. Examples are presented to demonstrate the model constructing method, analysis and control design technique, and to reveal certain dynamic behaviors of NEGs.

Dynamics and Stability of Potential Hyper-networked Evolutionary Games

This paper considers the modeling and convergence of hyper-networked evolutionary games （HNEGs）. In an HNEG the network graph is a hypergraph, which allows the fundamental network game to be a multi-player one. Using semi-tensor product of matrices and the fundamental evolutionary equation, the dynamics of an HNEG is obtained and we extend the results about the networked evolutionary games to show whether an HNEG is potential and how to calculate the potential. Then we propose a new strategy updating rule, called the cascading myopic best response adjustment rule （MBRAR）, and prove that under the cascading MBRAR the strategies of an HNEG will converge to a pure Nash equilibrium. An example is presented and discussed in detail to demonstrate the theoretical and numerical results.

Designing Evolutionary Wavelet Neural Network for Estimating Foaming Slag Quality in Electric Arc Furnace Using Power Quality Indices

In the present study,a novel approach based on an evolutionary wavelet neural network(EWNN)is proposed to estimate the slag quality in an electric arc furnace(EAF)employing power quality indices.In the EWNN,an evolutionary method is applied to train the parameters for a combination of neural networks and wavelets.I For this purpose,all of the electrical parameters for six melting processes are measured with a power quality analyzer,attached to the secondary component of an EAF transformer at a Saba steel complex,to estimate the foaming slag quality.Experimental results on various combinations of measured electrical parameters,applying the designed EWNN estimator,demonstrate that utilizing five leading indicators leads to the highest precision.The obtained 99%accuracy for estimating the foaming slag quality by EWNN compared to the other methods illustrates the proposed method's efficiency.

A survey on potential evolutionary game and its applications

Basic concepts about the finite potential games and the networked evolutionary games(NEGs)are introduced.Some new developments are surveyed,including(i)formulas for verifying whether a finite game is(weighted)potential and for calculating the(weighted)potential function;and(ii)the fundamental network equation and strategy profile dynamics of NEGs.Then some applications are introduced,which include:(i)convergence of NEGs;(ii)congestion control;(iii)distributed coverage of graphs.

Set Stability of Probabilistic Time-Delay Boolean Networks with Impulsive Effect

This paper investigates the set stability of probabilistic time-delay Boolean networks(PTDBN)with impulsive effect.Firstly,using the algebraic state space representation,an equivalent stochastic system is established for PTDBN with impulsive effect.Then,based on the probabilistic state transition matrix,a necessary and sufficient condition is presented for the set stability of PTDBN with impulsive effect.Finally,the obtained new result is applied to the networked evolutionary game with memories.

Optimized parallel architecture of evolutionary neural network for mass spectrometry data processing

Evolutionary neural network(ENN)shows high performance in function optimization and in finding approximately global optima from searching large and complex spaces.It is one of the most efficient and adaptive optimization techniques used widely to provide candidate solutions that lead to the fitness of the problem.ENN has the extraordinary ability to search the global and learning the approximate optimal solution regardless of the gradient information of the error functions.However,ENN requires high computation and processing which requires parallel processing platforms such as field programmable gate arrays(FPGAs)and graphic processing units(GPUs)to achieve a good performance.This work involves different new implementations of ENN by exploring and adopting different techniques and opportunities for parallel processing.Different versions of ENN algorithm have also been implemented and parallelized on FPGAs platform for low latency by exploiting the parallelism and pipelining approaches.Real data form mass spectrometry data(MSD)application was tested to examine and verify our implementations.This is a very important and extensive computation application which needs to search and find the optimal features(peaks)in MSD in order to distinguish cancer patients from control patients.ENN algorithm is also implemented and parallelized on single core and GPU platforms for comparison purposes.The computation time of our optimized algorithm on FPGA and GPU has been improved by a factor of 6.75 and 6,respectively.

Binary sequences-based approach for construction of evolutionary network

Evolutionary studies have been of prime importance to life scientists since ancient times. The advancements in technology has made it possible to make available the massive amounts of genomic data. The abundance of genomic data poses new challenges for biologists, computer scientists and mathematicians to develop approaches for discovery of new relationships in data and evolutionary networks. In this work, nucleotide sequences are converted into binary sequences to explore the network among different species. A new approach based on binary sequences has been proposed to reconstruct the accurate phylogenetic network. The algorithm developed is validated by comparing the results with those obtained by already existing method of network construction. A program is also coded in C language on the Intel Core i3 Dell inspiron machine to obtain the evolutionary network. The new approach developed also provides the fast solutions as there is no need of aligning the sequences.