Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations

In evolutionary games,most studies on finite populations have focused on a single updating mechanism.However,given the differences in individual cognition,individuals may change their strategies according to different updating mechanisms.For this reason,we consider two different aspiration-driven updating mechanisms in structured populations:satisfied-stay unsatisfied shift(SSUS)and satisfied-cooperate unsatisfied defect(SCUD).To simulate the game player’s learning process,this paper improves the particle swarm optimization algorithm,which will be used to simulate the game player’s strategy selection,i.e.,population particle swarm optimization(PPSO)algorithms.We find that in the prisoner’s dilemma,the conditions that SSUS facilitates the evolution of cooperation do not enable cooperation to emerge.In contrast,SCUD conditions that promote the evolution of cooperation enable cooperation to emerge.In addition,the invasion of SCUD individuals helps promote cooperation among SSUS individuals.Simulated by the PPSO algorithm,the theoretical approximation results are found to be consistent with the trend of change in the simulation results.

Characterization of SARS-CoV-2 worldwide transmission based on evolutionary dynamics and specific viral mutations in the spike protein

Background:The coronavirus disease 2019(COVID-19)caused by severe acute respiratory syndrome-related coronavirus-2(SARS-CoV-2)is pandemic.However,the origins and global transmission pattern of SARS-CoV-2 remain largely unknown.We aimed to characterize the origination and transmission of SARS-CoV-2 based on evolutionary dynamics.Methods:Using the full-length sequences of SARS-CoV-2 with intact geographic,demographic,and temporal information worldwide from the GISAID database during 26 December 2019 and 30 November 2020,we constructed the transmission tree to depict the evolutionary process by the R package"outbreaker".The affinity of the mutated receptor-binding region of the spike protein to angiotensin-converting enzyme 2(ACE2)was predicted using mCSM-PPI2 software.Viral infectivity and antigenicity were tested in ACE2-transfected HEK293T cells by pseudovirus transfection and neutralizing antibody test.Results:From 26 December 2019 to 8 March 2020,early stage of the COVID-19 pandemic,SARS-CoV-2 strains identified worldwide were mainly composed of three clusters:the Europe-based cluster including two USA-based subclusters;the Asia-based cluster including isolates in China,Japan,the USA,Singapore,Australia,Malaysia,and Italy;and the USA-based cluster.The SARS-CoV-2 strains identified in the USA formed four independent clades while those identified in China formed one clade.After 8 March 2020,the clusters of SARS-CoV-2 strains tended to be independent and became"pure"in each of the major countries.Twenty-two of 60 mutations in the receptor-binding domain of the spike protein were predicted to increase the binding affinity of SARS-CoV-2 to ACE2.Of all predicted mutants,the number of E484K was the largest one with 86585 sequences,followed by S477N with 55442 sequences worldwide.In more than ten countries,the frequencies of the isolates with E484K and S477N increased significantly.V367F and N354D mutations increased the infectivity of SARS-CoV-2 pseudoviruses(P<0.001).SARS-CoV-2 with V367F was more sensitive to the S1-targeting neutralizing antibody than the wild-type counterpart(P<0.001).Conclusions:SARS-CoV-2 strains might have originated in several countries simultaneously under certain evolutionary pressure.Travel restrictions might cause location-specific SARS-CoV-2 clustering.The SARS-CoV-2 evolution appears to facilitate its transmission via altering the affinity to ACE2 or immune evasion.

Evolutionary games and spatial periodicity

Spatial interactions are considered an important factor influencing a variety of evolutionary processes that take place in structured populations.It still remains an open problem to fully understand evolutionary game dynamics on networks except for certain limiting scenarios such as weak selection.Here we study the evolutionary dynamics of spatial games under strong selection where strategy evolution of individuals becomes deterministic in a fashion of winners taking all.We show that the long term behavior of the evolutionary process eventually converges to a particular basin of attraction,which is either a periodic cycle or a single fixed state depending on specific initial conditions and model parameters.In particular,we find that symmetric starting configurations can induce an exceedingly long transient phase encompassing a large number of aesthetic spatial patterns including the prominent kaleidoscopic cooperation.Our finding holds for any population structure and a broad class of finite games beyond the Prisoner’s Dilemma.Our work offers insights into understanding evolutionary dynamics of spatially extended systems ubiquitous in biology and ecology.

An Evolutionary Approach for Personalized Therapy in Multiple Myeloma

Most patients with multiple myeloma (MM) respond well to initial therapy, but invariably relapse due to evolution of resistant phenotypes. Here we examine the evolutionary dynamics of proliferation of resistant MM phenotypes during therapy. By applying computational models to data from three clinical trials for newly diagnosed MM patients, we have quantified the size and level of chemoresistance of subpopulations within the tumor burden in 124 patients, prior to and during therapy. Subsequently, we used the computational models to explore an alternative strategy of “adaptive therapy” (AT), which includes defined treatment holidays, to improve the duration of “controlled disease” (CD). Simulations showed that AT could prolong CD in all three trials: 50.0% vs. 11.1% 50-month CD for a single agent approach in older adults (P = 0.0123), 80.4% vs. 58.8% 60-month CD for a multi-agent bortezomib based therapy (P = 0.0082), and 54.0% vs. 24.0% 60-month CD for a multi-agent lenalidomide based therapy (P < 0.0001). Increases in duration of CD resulted from the stabilization of tumor burden, which in turn would delay the growth of chemoresistant sub-populations in patients with partial (PR), or very good partial response (VGPR). These computational algorithms suggest that AT may provide an alternative and feasible therapeutic management strategy in MM.

Dynamic evolutionary community detection algorithms based on the modularity matrix

Motivated by the relationship of the dynamic behaviors and network structure, in this paper, we present two efficient dynamic community detection algorithms. The phases of the nodes in the network can evolve according to our proposed differential equations. In each iteration, the phases of the nodes are controlled by several parameters. It is found that the phases of the nodes are ultimately clustered into several communities after a short period of evolution. They can be adopted to detect the communities successfully. The second differential equation can dynamically adjust several parameters, so it can obtain satisfactory detection results. Simulations on some test networks have verified the efficiency of the presented algorithms.

Finding Global Minima with a New Dynamical Evolutionary Algorithm

A new dynamical evolutionary algorithm (DEA) based on the theory of statistical mechanics is presented. This algorithm is very different from the traditional evolutionary algorithm and the two novel features are the unique of selecting strategy and the determination of individuals that are selected to crossover and mutate. We use DEA to solve a lot of global optimization problems that are nonlinear, multimodal and multidimensional and obtain satisfactory results.

Reconstructing the Nonlinear Dynamical Systems by Evolutionary Computation Techniques

We introduce a new dynamical evolutionary algorithm（DEA） based on the theory of statistical mechanics and investigate the reconstruction problem for the nonlinear dynamical systems using observation data. The convergence of the algorithm is discussed. We make the numerical experiments and test our model using the two famous chaotic systems （mainly the Lorenz and Chen systems）. The results show the relatively accurate reconstruction of these chaotic systems based on observational data can be obtained. Therefore we may conclude that there are broad prospects using our method to model the nonlinear dynamical systems.

Exploring the dynamic evolutionary mechanism of game model on the protection of traditional villages

With the rapid improvement of urbanization and industrialization in countries around the world,how to effectively solve the rapid demise of traditional villages is a social dilemma faced by all countries,which is why a series of relevant protection regulations have been promulgated in different historical periods.However,the formulation of relevant policies is still not scientific,universal,and long-term.In this study,we constructed an evolutionary game model of local governments and residents based on the evolutionary game theory(EGT),which is used to explore the evolutionary stability strategy(ESS)and stability conditions of stakeholders under the premise of mutual influence and restriction.Besides,the study also included the analysis about the impacts of different influence factors on the evolution tendency of the game model.At the same time,numerical simulation examples were used to verify the theoretical results and three crucial conclusions have been drawn.Firstly,the strategic evolution of stakeholders is a dynamic process of continuous adjustment and optimization,and its results and speed show consistent interdependence.Secondly,the decision-making of stakeholders mainly depends on the basic cost,and the high cost of investment is not conducive to the protection of traditional villages.Thirdly,the dynamic evolutionary mechanism composed of different influence factors will have an impact on the direction and speed of decision-making of stakeholders,which provides the basis for them to effectively restrict the decision-making of each other.This study eliminates the weaknesses of existing research approaches and provides scientific and novel ideas for the protection of traditional villages,which can contribute to the formulation and improvement of the relevant laws and regulations.

Dynamic Behavior Modeling in Multi-Agent System By Evolutionary Programming

In a multi-agent system, each agent must adapt itself to the environment and coordinate with other agents dynamically. TO predict or cooperate with the behavior of oiller agents. An agent should dynamically establish and evolve the cooperative behavior model of itself. In this paper, we represent the behavior model of an agent as a f-mite state machine and propose a new method of dynamically evolving the behavior model of an agent by evolutionary programming.

A Game-Theoretic Modeling Approach to Comprehend the Advantage of Dynamic Health Interventions in Limiting the Transmission of Multi-Strain Epidemics

In the situation of inadequate vaccines and rapid mutation of virulent strains, alternative health interventions play a crucial role in the containment of emerging epidemics. This study elucidates the critical aspects of health interventions to control epidemic resurgence. Besides, human behavioral response to epidemics plays an instrumental role in bringing the success of control efforts. The appearance of multi-strain epidemics has become a global health concern that requires special attention. Here, we introduce a novel mean-field epidemic game approach to predict the evolutionary dynamics of flu-like epidemics having multiple disease strains. Our model illustrates the importance of multiple provisions alongside their timely execution for better disease attenuation. In addition to vaccination, we introduce self-protection as a potential alternative that yields safeguard against either strain. Both these imperfect provisions render better efficacy against primary (resident) strain than secondary (mutant) to contain epidemic transmission. The simulation-backed model analysis further sheds some light on the crucial impacts of control interventions to limit the invasion of virulent strains from qualitative and quantitative viewpoints. It explicates how vaccination and self-protection complement each other as per situation demands. Our full-fledged theoretical approach further illustrates the dynamic trade-off between the cost and efficacy of a certain intervention. We confirm that the disease dies out when the basic reproduction number of individual strains is less than one and becomes endemic if it is greater than one. Finally, the model addresses the evolutionary consequences when mutation takes place from primary to secondary strain. Some impressive facts while employing dual provisions have been reinforced using a game-theoretic framework.

Prevalence,variation,and transmission patterns of human respiratory syncytial virus from pediatric patients in Hubei,China during 2020–2021

Human respiratory syncytial virus(RSV)is a severe threat to children and a main cause of acute lower respiratory tract infections.Nevertheless,the intra-host evolution and inter-regional diffusion of RSV are little known.In this study,we performed a systematic surveillance in hospitalized children in Hubei during 2020–2021,in which 106 RSV-positive samples were detected both clinically and by metagenomic next generation sequencing(mNGS).RSV-A and RSV-B groups co-circulated during surveillance with RSV-B being predominant.About 46 high-quality genomes were used for further analyses.A total of 163 intra-host nucleotide variation(iSNV)sites distributed in 34 samples were detected,and glycoprotein(G)gene was the most enriched gene for iSNVs,with non-synonymous substitutions more than synonymous substitutions.Evolutionary dynamic analysis showed that the evolutionary rates of G and NS2 genes were higher,and the population size of RSV groups changed over time.We also found evidences of inter-regional diffusion from Europe and Oceania to Hubei for RSV-A and RSV-B,respectively.This study highlighted the intra-host and inter-host evolution of RSV,and provided some evi-dences for understanding the evolution of RSV.

BEESO:Multi-strategy Boosted Snake-Inspired Optimizer for Engineering Applications

This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering applications.As a newly mooted meta-heuristic algorithm,snake optimizer(SO)mathematically models the mating characteristics of snakes to find the optimal solution.SO has a simple structure and offers a delicate balance between exploitation and exploration.However,it also has some shortcomings to be improved.The proposed BEESO consequently aims to lighten the issues of lack of population diversity,convergence slowness,and the tendency to be stuck in local optima in SO.The presentation of Bi-Directional Search(BDS)is to approach the global optimal value along the direction guided by the best and the worst individuals,which makes the convergence speed faster.The increase in population diversity in BEESO benefits from Modified Evolutionary Population Dynamics(MEPD),and the replacement of poorer quality individuals improves population quality.The Elite Opposition-Based Learning(EOBL)provides improved local exploitation ability of BEESO by utilizing solid solutions with good performance.The performance of BEESO is illustrated by comparing its experimental results with several algorithms on benchmark functions and engineering designs.Additionally,the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank sum tests.The findings show that these introduced strategies provide some improvements in the performance of SO,and the accuracy and stability of the optimization results provided by the proposed BEESO are competitive among all algorithms.To conclude,the proposed BEESO offers a good alternative to solving optimization issues.

Climate shapes the spatiotemporal variation in color morph diversity and composition across the distribution range of Chrysomela lapponica leaf beetle

Color polymorphism offers rich opportunities for studying the eco-evolutionary mechanisms that drive the adaptations of local populations to heterogeneous and changing environments.We explored the color morph diversity and composition in a Chrysomela lapponica leaf beetle across its entire distribution range to test the hypothesis that environmental and climatic variables shape spatiotemporal variation in the phenotypic structure of a polymorphic species.We obtained information on 13617 specimens of this beetle from museums,private collections,and websites.These specimens(collected from 1830-2020)originated from 959 localities spanning 33°latitude,178°longitude,and 4200 m altitude.We classified the beetles into five color morphs and searched for environmental factors that could explain the variation in the level of polymorphism(quantified by the Shannon diversity index)and in the relative frequencies of individual color morphs.The highest level of polymorphism was found at high latitudes and altitudes.The color morphs differed in their climatic requirements;composition of colour morphs was independent of the geographic distance that separated populations but changed with collection year,longitude,mean July temperature and betweenyear temperature fluctuations.The proportion of melanic beetles,in line with the thermal melanism hypothesis,increased with increasing latitude and altitude and decreased with increasing climate seasonality.Melanic morph frequencies also declined during the past century,but only at high latitudes and altitudes where recent climate warming was especially strong.The observed patterns suggest that color polymorphism is especially advantageous for populations inhabiting unpredictable environments,presumably due to the different climatic requirements of coexisting color morphs.