Continental crust is the long-term achievements of Earth's evolution across billions of years.The continental rocks could have been modified by various types of geological processes,such as metamorphism,weathering...Continental crust is the long-term achievements of Earth's evolution across billions of years.The continental rocks could have been modified by various types of geological processes,such as metamorphism,weathering,and reworking.Therefore,physical or chemical properties of rocks through time record the composite effects of geological,biological,hydrological,and climatological processes.Temporal variations in these time series datasets could provide important clues for understanding the co-evolution of different layers on Earth.However,deciphering Earth's evolution in deep time is challenged by incompleteness,singularity,and intermittence of geological records associated with extreme geological events,hindering a rigorous assessment of the underlying coupling mechanisms.Here,we applied the recently developed local singularity analysis and wavelet analysis method to deep-time U-Pb age spectra and sedimentary abundance record across the past 3.5 Gyrs.Standard cross-correlation analysis suggests that the singularity records of marine sediment accumulations and magmatism intensity at continental margin are correlated negatively(R^(2)=0.8),with a delay of~100 Myr.Specifically,wavelet coherence analysis suggests a~500-800 Myr cycle of correlation between two records,implying a coupling between the major downward processes(subduction and recycling sediments)and upward processes(magmatic events)related to the aggregation and segregation of supercontinents.The results clearly reveal the long-term cyclic feedback mechanism between sediment accumulation and magmatism intensity through aggregation of supercontinents.展开更多
Due to the fact that conventional heuristic attribute reduction algorithms are poor in running efficiency and difficult in accomplishing the co-evolutionary reduction mechanism in the decision table, an adaptive multi...Due to the fact that conventional heuristic attribute reduction algorithms are poor in running efficiency and difficult in accomplishing the co-evolutionary reduction mechanism in the decision table, an adaptive multicascade attribute reduction algorithm based on quantum-inspired mixed co-evolution is proposed. First, a novel and efficient self- adaptive quantum rotation angle strategy is designed to direct the participating populations to mutual adaptive evolution and to accelerate convergence speed. Then, a multicascade model of cooperative and competitive mixed co-evolution is adopted to decompose the evolutionary attribute species into subpopulations according to their historical performance records, which can increase the diversity of subpopulations and select some elitist individuals so as to strengthen the sharing ability of their searching experience. So the global optimization reduction set can be obtained quickly. The experimental results show that, compared with the existing algorithms, the proposed algorithm can achieve a higher performance for attribute reduction, and it can be considered as a more competitive heuristic algorithm on the efficiency and accuracy of minimum attribute reduction.展开更多
A design problem with deficient information is generally described as wicked or ill-defined.The information insufficiency leaves designers with loose settings,free environments,and a lack of strict boundaries,which pr...A design problem with deficient information is generally described as wicked or ill-defined.The information insufficiency leaves designers with loose settings,free environments,and a lack of strict boundaries,which provides them with more opportunities to facilitate innovation.Therefore,to capture the opportunity behind the uncertainty of a design problem,this study models an innovative design as a composite solving process,where the problem is clarified and resolved from fuzziness to satisfying solutions by interplay among design problems,knowledge,and solutions.Additionally,a triple-helix structured model for the innovative product design process is proposed based on the co-evolution of the problem,solution,and knowledge spaces,to provide designers with a distinct design strategy and method for innovative design.The three spaces interact and co-evolve through iterative mappings,including problem structuring,knowledge expansion,and solution generation.The mappings carry the information processing and decision-making activities of the design,and create the path to satisfying solutions.Finally,a case study of a reactor coolant flow distribution device is presented to demonstrate the practicability of this model and the method for innovative product design.展开更多
This paper proposes novel multiple-mobile-robot collision avoidance path planning based on cooperative co-evolution,which can be executed fully distributed and in parallel. A real valued co-evolutionary algorithm is d...This paper proposes novel multiple-mobile-robot collision avoidance path planning based on cooperative co-evolution,which can be executed fully distributed and in parallel. A real valued co-evolutionary algorithm is developed to coordinate the movement of multiple robots in 2D world, avoiding C-space or grid net searching. The collision avoidance is achieved by cooperatively co-evolving segments of paths and the time interval to pass them. Methods for constraint handling, which are developed for evolutionary algorithm, make the path planning easier. The effectiveness of the algorithm is demonstrated on a number of 2Dpath planning problems.展开更多
A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rat...A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.展开更多
Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differentia...Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differential evolution algorithm?based on ensemble of constraint handling techniques and multi-population?framework, called ECMPDE. First, handling three improved variants of differential evolution algorithms are dynamically matched with two constraint handling techniques through the constraint allocation mechanism. Each combination includes three variants with corresponding constraint handling technique?and these combinations are in the set. Second, the population is divided into three smaller subpopulations and one larger reward subpopulation. Then a combination with three constraint algorithms is randomly selected from the set, and the three constraint algorithms are run in three sub-populations respectively. According to the improvement of fitness value, the optimal constraint?algorithm is selected to run on the reward sub-population, which can share?information and close cooperation among populations. In order to verify the effectiveness of the proposed algorithm, 12 standard constraint optimization problems?and 10 engineering constraint optimization problems are tested. The experimental results show that ECMPDE is an effective algorithm for solving constraint optimization problems.展开更多
Web quality of service (QoS) awareness requires not only the selection of specific services to complete specific tasks, but also the comprehensive quality of service of the whole web service composition. How to select...Web quality of service (QoS) awareness requires not only the selection of specific services to complete specific tasks, but also the comprehensive quality of service of the whole web service composition. How to select the web service composition with the highest comprehensive QoS is a NP hard problem. In this paper, an improved multi population genetic algorithm is proposed. Cosine adaptive operator is added to the algorithm to avoid premature algorithm caused by improper genetic operator and the disadvantage of destroying excellent individuals in later period. Experimental results show that compared with the common genetic algorithm and multi population genetic algorithm, this algorithm has the advantages of shorter time consumption and higher accuracy, and effectively avoids the loss of effective genes in the population.展开更多
Co-evolution has been shown to result in an adaptive reciprocal modification in the respective behaviors of interacting populations over time. In the case of host-parasite co-evolution,the adaptive behavior is most ev...Co-evolution has been shown to result in an adaptive reciprocal modification in the respective behaviors of interacting populations over time. In the case of host-parasite co-evolution,the adaptive behavior is most evident from the reciprocal change in fitness of host and parasite-manifested in terms of pathogen survival versus host resistance. Cytomegaloviruses and their hosts represent a pairing of populations that has co-evolved over hundreds of years. This review explores the pathogenetic consequences emerging from the behavioral changes caused by co-evolutionary forces on the virus and its host.展开更多
We study evolutionary prisoner＇s dilemma game on adaptive networks where a population of players co-evolves with their interaction networks. During the co-evolution process, interacted players with opposite strategie...We study evolutionary prisoner＇s dilemma game on adaptive networks where a population of players co-evolves with their interaction networks. During the co-evolution process, interacted players with opposite strategies either rewire the link between them with probability p or update their strategies with probability 1 - p depending on their payoffs. Numerical simulation shows that the final network is either split into some disconnected communities whose players share the same strategy within each community or forms a single connected network in which all nodes are in the same strategy. Interestingly, the density of cooperators in the final state can be maximised in an intermediate range of p via the competition between time scale of the network dynamics and that of the node dynamics. Finally, the mean-field analysis helps to understand the results of numerical simulation. Our results may provide some insight into understanding the emergence of cooperation in the real situation where the individuals＇ behaviour and their relationship adaptively co-evolve.展开更多
A co-evolutional immune algorithm for the optimization of a function with real parameters is de-scribed.It uses a cooperative co-evolution of two populations,one is a population of antibodies and theother is a populat...A co-evolutional immune algorithm for the optimization of a function with real parameters is de-scribed.It uses a cooperative co-evolution of two populations,one is a population of antibodies and theother is a population of successful mutation vectors.These two population evolve together to improve thediversity of the antibodies.The algorithm described is then tested on a suite of optimization problems.The results show that on most of test functions,this algorithm can converge to the global optimum atquicker rate in a given range,the performance of optimization is improved effetely.展开更多
It is important to harmonize effectively the behaviors of the agents in the multi-agent system (MAS) to complete the solution process. The co-evolution computing techniques, inspired by natural selection and genetics,...It is important to harmonize effectively the behaviors of the agents in the multi-agent system (MAS) to complete the solution process. The co-evolution computing techniques, inspired by natural selection and genetics, are usually used to solve these problems. Based on learning and evolution mechanisms of the biological systems, an adaptive co-evolution model was proposed in this paper. Inner-population, inter-population, and community learning operators were presented. The adaptive co-evolution algorithm (ACEA) was designed in detail. Some simulation experiments were done to evaluate the performance of the ACEA. The results show that the ACEA is more effective and feasible than the genetic algorithm to solve the optimization problems.展开更多
Individual behaviors, such as drinking, smoking, screen time, and physical activity, can be strongly influenced by the behavior of friends. At the same time, the choice of friends can be influenced by shared behaviora...Individual behaviors, such as drinking, smoking, screen time, and physical activity, can be strongly influenced by the behavior of friends. At the same time, the choice of friends can be influenced by shared behavioral preferences. The actor-based stochastic models (ABSM) are developed to study the interdependence of social networks and behavior. These methods are efficient and useful for analysis of discrete behaviors, such as drinking and smoking;however, since the behavior evolution function is in an exponential format, the ABSM can generate inconsistent and unrealistic results when the behavior variable is continuous or has a large range, such as hours of television watched or body mass index. To more realistically model continuous behavior variables, we propose a co-evolution process based on a linear model which is consistent over time and has an intuitive interpretation. In the simulation study, we applied the expectation maximization (EM) and Markov chain Monte Carlo (MCMC) algorithms to find the maximum likelihood estimate (MLE) of parameter values. Additionally, we show that our assumptions are reasonable using data from the National Longitudinal Study of Adolescent Health (Add Health).展开更多
Accurate and timely monthly rainfall forecasting is a major challenge for the scientific community in hydrological research such as river management project and design of flood warning systems. Support Vector Regressi...Accurate and timely monthly rainfall forecasting is a major challenge for the scientific community in hydrological research such as river management project and design of flood warning systems. Support Vector Regression (SVR) is a very useful precipitation prediction model. In this paper, a novel parallel co-evolution algorithm is presented to determine the appropriate parameters of the SVR in rainfall prediction based on parallel co-evolution by hybrid Genetic Algorithm and Particle Swarm Optimization algorithm, namely SVRGAPSO, for monthly rainfall prediction. The framework of the parallel co-evolutionary algorithm is to iterate two GA and PSO populations simultaneously, which is a mechanism for information exchange between GA and PSO populations to overcome premature local optimum. Our methodology adopts a hybrid PSO and GA for the optimal parameters of SVR by parallel co-evolving. The proposed technique is applied over rainfall forecasting to test its generalization capability as well as to make comparative evaluations with the several competing techniques, such as the other alternative methods, namely SVRPSO (SVR with PSO), SVRGA (SVR with GA), and SVR model. The empirical results indicate that the SVRGAPSO results have a superior generalization capability with the lowest prediction error values in rainfall forecasting. The SVRGAPSO can significantly improve the rainfall forecasting accuracy. Therefore, the SVRGAPSO model is a promising alternative for rainfall forecasting.展开更多
Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple modalities.Despite these achievements,LLMs have in...Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple modalities.Despite these achievements,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific accuracy.To address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model co-evolution.First,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge prompting.Second,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge injection.Third,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual learning.We illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various domains.We conclude by outlining future pathways for further advancement and applications.展开更多
Generally,the interaction between the East Asian subtropical westerly jet(EASWJ)and the East Asian summer monsoon(EASM)is regarded as a critical dynamic factor in the evolution of precipitation patterns in China.Using...Generally,the interaction between the East Asian subtropical westerly jet(EASWJ)and the East Asian summer monsoon(EASM)is regarded as a critical dynamic factor in the evolution of precipitation patterns in China.Using simulation results from the transient climate evolution since the last glacial maximum,this study applies the multivariate empirical orthogonal function(MVEOF)analysis method to investigate the co-evolution relationships of the EASWJ and the EASM during the early,middle,and late Holocene,as well as their influence on precipitation patterns in China.The results indicate that all the first MVEOF modes in different time periods of the Holocene display an out-of-phase relationship in the intensity anomaly between the EASWJ and the EASM.However,the jet stream is wider and more tilted in the late Holocene.Under the influence of secondary circulations formed during their co-evolution,a north-south dipolar precipitation pattern in eastern China(“flood in the south and drought in the north”or“drought in the south and flood in the north”)and an east-west dipolar pattern in northern China(“wet in the west and dry in the east”or“wet in the east and dry in the west”)are found in the early and middle Holocene,while in the late Holocene a regionally-consistent precipitation pattern is witnessed across the whole region.In this mode,the precipitation in the middle and late Holocene is primarily dominated by trend changes.The second MVEOF mode reveals that the EASM weakens when the EASWJ shifts eastward and the jet steam axis shortens during the early Holocene,resulting in a north-south dipolar precipitation pattern in eastern China and a regionally-consistent pattern in northern China.In the middle Holocene,dipolar precipitation patterns are also observed in both eastern China and northern China when the EASWJ moves northward and the EASM strengthens,while the moisture condition in North China is less pronounced,and vice versa.In the late Holocene,the intensity anomalies of the EASWJ and the EASM exhibit an out-of-phase relationship in the temperate zone and an in-phase relationship in the subtropical zone,leading to a tripolar precipitation pattern in eastern China and a dipolar pattern in northern China.In this mode,the precipitation during the middle and late Holocene is primarily dominated by centennial oscillations.The precipitation patterns influenced by the co-evolution relationship between EASWJ and EASM correspond well with the reconstructed precipitation data,providing an explanation for the precipitation patterns observed in the reconstructed data from the perspective of dynamical mechanisms.展开更多
In this research,a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm(DHICCA)is proposed for addressing the distributed lot-streaming flowshop scheduling problem(DLSFSP)with the objec...In this research,a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm(DHICCA)is proposed for addressing the distributed lot-streaming flowshop scheduling problem(DLSFSP)with the objective to minimize the makespan.A two-layer-vector representation is devised to bridge the solution space of DLSFSP and the search space of DHICCA.In the evolution of DHICCA,population individuals are endowed with heterogeneous identities according to their quality,including superior individuals,ordinary individuals,and inferior individuals,which serve local exploitation,global exploration,and diversified restart,respectively.Because individuals with different identities require different evolutionary mechanisms to fully unleash their respective potentials,identity-specific evolutionary operators are devised to evolve them in a cooperative co-evolutionary way.This is important to use limited population resources to solve complex optimization problems.Specifically,exploitation is carried out on superior individuals by devising three exploitative operators with different intensities based on techniques of variable neighborhood,destruction-construction,and gene targeting.Exploration is executed on ordinary individuals by a newly constructed discrete Jaya algorithm and a probability crossover strategy.In addition,restart is performed on inferior individuals to introduce new evolutionary individuals to the population.After the cooperative co-evolution,all individuals with different identities are merged as a population again,and their identities are dynamically adjusted by new evaluation.The influence of parameters on the algorithm is investigated based on design-of-experiment and comprehensive computational experiments are used to evaluate the performance of all algorithms.The results validate the effectiveness of special designs and show that DHICCA performs more efficient than the existing state-of-the-art algorithms in solving the DLSFSP.展开更多
Plant-pathogen interactions involve complex biological processes that operate across molecular,cellular,microbiome,and ecological levels,significantly influencing plant health and agricultural productivity.In response...Plant-pathogen interactions involve complex biological processes that operate across molecular,cellular,microbiome,and ecological levels,significantly influencing plant health and agricultural productivity.In response to pathogenic threats,plants have developed sophisticated defense mechanisms,such as pattern-triggered immunity(PTI)and effector-triggered immunity(ETI),which rely on specialized recognition systems such as pattern recognition receptors(PRRs)and nucleotide-binding leucine-rich repeat(NLR)proteins.These immune responses activate intricate signaling pathways involving mitogen-activated protein kinase cascades,calcium fluxes,reactive oxygen species production,and hormonal cross-talk among salicylic acid,jasmonic acid,and ethylene.Furthermore,structural barriers such as callose deposition and lignification,along with the synthesis of secondary metabolites and antimicrobial enzymes,play crucial roles in inhibiting pathogen invasion and proliferation.The plant microbiome further enhances host immunity through beneficial associations with plant growth-promoting rhizobacteria(PGPR)and mycorrhizal fungi,which facilitate induced systemic resistance(ISR)and improve nutrient acquisition.As climate change exacerbates the impact of pathogens,these molecular and microbiome-driven defenses influence disease distribution and plant resilience,highlighting the importance of integrating ecological insights for sustainable disease management Advancements in microbiome engineering,including the application of synthetic microbial communities and commercial bio-inoculants,offer promising strategies for sustainable disease management.However,the impacts of climate change on pathogen virulence,host susceptibility,and disease distribution complicate these interactions,emphasizing the need for resilient and adaptive agricultural practices.This review highlights the necessity of a holistic,interdisciplinary approach that integrates multi-omics technologies,microbiome research,and ecological insights to develop effective and sustainable solutions for managing plant diseases and ensuring global food security.展开更多
An investigation of sucking lice on the body surface of small mammals was carried out in the surrounding areas of Erhai Lake in Dali, Yunnan from 2003 to 2004. From investigation sites, 3 303 small mammal hosts were c...An investigation of sucking lice on the body surface of small mammals was carried out in the surrounding areas of Erhai Lake in Dali, Yunnan from 2003 to 2004. From investigation sites, 3 303 small mammal hosts were captured and identified into 7 families, 15 genera and 21 species in 4 orders （Rodentia, Insectivora, Scandentia and Carnivora）, while t4 635 individuals of sucking lice collected from the body surface of the small mammal hosts are identified into 5 families, 6 genera and 21 species in the Order Anoplura, The sites stand alongside three cordilleras surrounding the Erhai Lake, namely Eastern Wuliang Mountain, Southern Ailao Mountain and Western Cangshan Mountain. The three confined oriented areas are different landscapes within the same zone where the longitude, latitude, altitude and fauna are homologous but isolated by Erhai Lake as inartificial barrier. The aim of this study was to recognize features of the species diversity, abundance, community structure, similarity and distribution of sucking lice in different landscapes within the same zone. The results showed the species diversity of sucking lice was very low with a very simple community structure. The distribution of sucking lice and their corresponding hosts are quite uneven among different oriented areas and this may imply that ecological environment influences the species composition and distribution of sucking lice and their corresponding hosts. A certain species of hosts usually have their fixed louse species. The similarity of sucking louse communities is highly consistent with the affinity of small mammal hosts in taxonomy. Species of sucking lice on the same small mammal host in different oriented areas of Erhai Lake are homologous. The results strongly suggest a close relationship of co-evolution between sucking lice and their hosts.展开更多
多种群方法已被证明是提高演化算法动态优化性能的重要方法之一。提出了多种群热力学遗传算法(multi-population based thermodynamic genetic algorithm,MPTDGA)。该算法使用一个概率向量在热力学遗传算法迭代过程中不断演化优化与竞...多种群方法已被证明是提高演化算法动态优化性能的重要方法之一。提出了多种群热力学遗传算法(multi-population based thermodynamic genetic algorithm,MPTDGA)。该算法使用一个概率向量在热力学遗传算法迭代过程中不断演化优化与竞争学习,环境变化时分化成三个概率向量,并分别抽样产生原对偶和随机迁入三个子种群,依据这三个种群和记忆种群最好解的情况,选择新的工作概率向量进入新环境进行学习。在动态背包问题上的实验结果表明,MPTDGA比原对偶遗传算法跟踪最优解的能力更强,有很好的多样性,非常适合求解0-1动态优化问题。展开更多
Helicobacter pylori(H.pylori)is present in roughly 50%of the human population worldwide and infection levels reach over 70%in developing countries.The infection has classically been associated with different gastro-in...Helicobacter pylori(H.pylori)is present in roughly 50%of the human population worldwide and infection levels reach over 70%in developing countries.The infection has classically been associated with different gastro-intestinal diseases,but also with extra gastric diseases.Despite such associations,the bacterium frequently persists in the human host without inducing disease,and it has been suggested that H.pylori may also play a beneficial role in health.To understand how H.pylori can produce such diverse effects in the human host,several studies have focused on understanding the local and systemic effects triggered by this bacterium.One of the main mechanisms by which H.pylori is thought to damage the host is by inducing local and systemic inflammation.However,more recently,studies are beginning to focus on the effects of H.pylori and its metabolism on the gastric and intestinal microbiome.The objective of this review is to discuss how H.pylori has co-evolved with humans,how H.pylori presence is associated with positive and negative effects in human health and how inflammation and/or changes in the microbiome are associated with the observed outcomes.展开更多
基金supported by the National Natural Science Foundation of China(No.42050103)。
文摘Continental crust is the long-term achievements of Earth's evolution across billions of years.The continental rocks could have been modified by various types of geological processes,such as metamorphism,weathering,and reworking.Therefore,physical or chemical properties of rocks through time record the composite effects of geological,biological,hydrological,and climatological processes.Temporal variations in these time series datasets could provide important clues for understanding the co-evolution of different layers on Earth.However,deciphering Earth's evolution in deep time is challenged by incompleteness,singularity,and intermittence of geological records associated with extreme geological events,hindering a rigorous assessment of the underlying coupling mechanisms.Here,we applied the recently developed local singularity analysis and wavelet analysis method to deep-time U-Pb age spectra and sedimentary abundance record across the past 3.5 Gyrs.Standard cross-correlation analysis suggests that the singularity records of marine sediment accumulations and magmatism intensity at continental margin are correlated negatively(R^(2)=0.8),with a delay of~100 Myr.Specifically,wavelet coherence analysis suggests a~500-800 Myr cycle of correlation between two records,implying a coupling between the major downward processes(subduction and recycling sediments)and upward processes(magmatic events)related to the aggregation and segregation of supercontinents.The results clearly reveal the long-term cyclic feedback mechanism between sediment accumulation and magmatism intensity through aggregation of supercontinents.
基金The National Natural Science Foundation of China(No. 61139002,61171132)the Funding of Jiangsu Innovation Program for Graduate Education (No. CXZZ11_0219 )+2 种基金the Natural Science Foundation of Jiangsu Province (No. BK2010280)the Open Project of Jiangsu Provincial Key Laboratory of Computer Information Processing Technology (No. KJS1023)the Applying Study Foundation of Nantong(No. BK2011062)
文摘Due to the fact that conventional heuristic attribute reduction algorithms are poor in running efficiency and difficult in accomplishing the co-evolutionary reduction mechanism in the decision table, an adaptive multicascade attribute reduction algorithm based on quantum-inspired mixed co-evolution is proposed. First, a novel and efficient self- adaptive quantum rotation angle strategy is designed to direct the participating populations to mutual adaptive evolution and to accelerate convergence speed. Then, a multicascade model of cooperative and competitive mixed co-evolution is adopted to decompose the evolutionary attribute species into subpopulations according to their historical performance records, which can increase the diversity of subpopulations and select some elitist individuals so as to strengthen the sharing ability of their searching experience. So the global optimization reduction set can be obtained quickly. The experimental results show that, compared with the existing algorithms, the proposed algorithm can achieve a higher performance for attribute reduction, and it can be considered as a more competitive heuristic algorithm on the efficiency and accuracy of minimum attribute reduction.
基金Supported by National Natural Science Foundation of China(Grant No.51435011).
文摘A design problem with deficient information is generally described as wicked or ill-defined.The information insufficiency leaves designers with loose settings,free environments,and a lack of strict boundaries,which provides them with more opportunities to facilitate innovation.Therefore,to capture the opportunity behind the uncertainty of a design problem,this study models an innovative design as a composite solving process,where the problem is clarified and resolved from fuzziness to satisfying solutions by interplay among design problems,knowledge,and solutions.Additionally,a triple-helix structured model for the innovative product design process is proposed based on the co-evolution of the problem,solution,and knowledge spaces,to provide designers with a distinct design strategy and method for innovative design.The three spaces interact and co-evolve through iterative mappings,including problem structuring,knowledge expansion,and solution generation.The mappings carry the information processing and decision-making activities of the design,and create the path to satisfying solutions.Finally,a case study of a reactor coolant flow distribution device is presented to demonstrate the practicability of this model and the method for innovative product design.
基金Project (No.2002CB312200) supported by the National Basic Research Program (973) of China
文摘This paper proposes novel multiple-mobile-robot collision avoidance path planning based on cooperative co-evolution,which can be executed fully distributed and in parallel. A real valued co-evolutionary algorithm is developed to coordinate the movement of multiple robots in 2D world, avoiding C-space or grid net searching. The collision avoidance is achieved by cooperatively co-evolving segments of paths and the time interval to pass them. Methods for constraint handling, which are developed for evolutionary algorithm, make the path planning easier. The effectiveness of the algorithm is demonstrated on a number of 2Dpath planning problems.
基金Project(2013CB733605)supported by the National Basic Research Program of ChinaProject(21176073)supported by the National Natural Science Foundation of China
文摘A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.
文摘Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differential evolution algorithm?based on ensemble of constraint handling techniques and multi-population?framework, called ECMPDE. First, handling three improved variants of differential evolution algorithms are dynamically matched with two constraint handling techniques through the constraint allocation mechanism. Each combination includes three variants with corresponding constraint handling technique?and these combinations are in the set. Second, the population is divided into three smaller subpopulations and one larger reward subpopulation. Then a combination with three constraint algorithms is randomly selected from the set, and the three constraint algorithms are run in three sub-populations respectively. According to the improvement of fitness value, the optimal constraint?algorithm is selected to run on the reward sub-population, which can share?information and close cooperation among populations. In order to verify the effectiveness of the proposed algorithm, 12 standard constraint optimization problems?and 10 engineering constraint optimization problems are tested. The experimental results show that ECMPDE is an effective algorithm for solving constraint optimization problems.
文摘Web quality of service (QoS) awareness requires not only the selection of specific services to complete specific tasks, but also the comprehensive quality of service of the whole web service composition. How to select the web service composition with the highest comprehensive QoS is a NP hard problem. In this paper, an improved multi population genetic algorithm is proposed. Cosine adaptive operator is added to the algorithm to avoid premature algorithm caused by improper genetic operator and the disadvantage of destroying excellent individuals in later period. Experimental results show that compared with the common genetic algorithm and multi population genetic algorithm, this algorithm has the advantages of shorter time consumption and higher accuracy, and effectively avoids the loss of effective genes in the population.
基金supported by US Public Health Service (NIH grants AI041927,AI050468,DE014145,and DE016813)
文摘Co-evolution has been shown to result in an adaptive reciprocal modification in the respective behaviors of interacting populations over time. In the case of host-parasite co-evolution,the adaptive behavior is most evident from the reciprocal change in fitness of host and parasite-manifested in terms of pathogen survival versus host resistance. Cytomegaloviruses and their hosts represent a pairing of populations that has co-evolved over hundreds of years. This review explores the pathogenetic consequences emerging from the behavioral changes caused by co-evolutionary forces on the virus and its host.
基金Project supported by the National Natural Science Foundation of China (Grant No. 20873130)the Graduate Innovation Fund of USTC
文摘We study evolutionary prisoner＇s dilemma game on adaptive networks where a population of players co-evolves with their interaction networks. During the co-evolution process, interacted players with opposite strategies either rewire the link between them with probability p or update their strategies with probability 1 - p depending on their payoffs. Numerical simulation shows that the final network is either split into some disconnected communities whose players share the same strategy within each community or forms a single connected network in which all nodes are in the same strategy. Interestingly, the density of cooperators in the final state can be maximised in an intermediate range of p via the competition between time scale of the network dynamics and that of the node dynamics. Finally, the mean-field analysis helps to understand the results of numerical simulation. Our results may provide some insight into understanding the emergence of cooperation in the real situation where the individuals＇ behaviour and their relationship adaptively co-evolve.
基金Supported by the National Fundamental Research Project(A1420060159)
文摘A co-evolutional immune algorithm for the optimization of a function with real parameters is de-scribed.It uses a cooperative co-evolution of two populations,one is a population of antibodies and theother is a population of successful mutation vectors.These two population evolve together to improve thediversity of the antibodies.The algorithm described is then tested on a suite of optimization problems.The results show that on most of test functions,this algorithm can converge to the global optimum atquicker rate in a given range,the performance of optimization is improved effetely.
基金Project of Shanghai Committee of Science and Technology, China ( No.08JC1400100, No. QB081404100)Leading Academic Discipline Project of Shanghai Municipal Education Commission, China (No.J51901)
文摘It is important to harmonize effectively the behaviors of the agents in the multi-agent system (MAS) to complete the solution process. The co-evolution computing techniques, inspired by natural selection and genetics, are usually used to solve these problems. Based on learning and evolution mechanisms of the biological systems, an adaptive co-evolution model was proposed in this paper. Inner-population, inter-population, and community learning operators were presented. The adaptive co-evolution algorithm (ACEA) was designed in detail. Some simulation experiments were done to evaluate the performance of the ACEA. The results show that the ACEA is more effective and feasible than the genetic algorithm to solve the optimization problems.
文摘Individual behaviors, such as drinking, smoking, screen time, and physical activity, can be strongly influenced by the behavior of friends. At the same time, the choice of friends can be influenced by shared behavioral preferences. The actor-based stochastic models (ABSM) are developed to study the interdependence of social networks and behavior. These methods are efficient and useful for analysis of discrete behaviors, such as drinking and smoking;however, since the behavior evolution function is in an exponential format, the ABSM can generate inconsistent and unrealistic results when the behavior variable is continuous or has a large range, such as hours of television watched or body mass index. To more realistically model continuous behavior variables, we propose a co-evolution process based on a linear model which is consistent over time and has an intuitive interpretation. In the simulation study, we applied the expectation maximization (EM) and Markov chain Monte Carlo (MCMC) algorithms to find the maximum likelihood estimate (MLE) of parameter values. Additionally, we show that our assumptions are reasonable using data from the National Longitudinal Study of Adolescent Health (Add Health).
文摘Accurate and timely monthly rainfall forecasting is a major challenge for the scientific community in hydrological research such as river management project and design of flood warning systems. Support Vector Regression (SVR) is a very useful precipitation prediction model. In this paper, a novel parallel co-evolution algorithm is presented to determine the appropriate parameters of the SVR in rainfall prediction based on parallel co-evolution by hybrid Genetic Algorithm and Particle Swarm Optimization algorithm, namely SVRGAPSO, for monthly rainfall prediction. The framework of the parallel co-evolutionary algorithm is to iterate two GA and PSO populations simultaneously, which is a mechanism for information exchange between GA and PSO populations to overcome premature local optimum. Our methodology adopts a hybrid PSO and GA for the optimal parameters of SVR by parallel co-evolving. The proposed technique is applied over rainfall forecasting to test its generalization capability as well as to make comparative evaluations with the several competing techniques, such as the other alternative methods, namely SVRPSO (SVR with PSO), SVRGA (SVR with GA), and SVR model. The empirical results indicate that the SVRGAPSO results have a superior generalization capability with the lowest prediction error values in rainfall forecasting. The SVRGAPSO can significantly improve the rainfall forecasting accuracy. Therefore, the SVRGAPSO model is a promising alternative for rainfall forecasting.
基金supported in part by National Natural Science Foundation of China(62441605)。
文摘Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple modalities.Despite these achievements,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific accuracy.To address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model co-evolution.First,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge prompting.Second,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge injection.Third,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual learning.We illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various domains.We conclude by outlining future pathways for further advancement and applications.
基金supported by the Program of Global Change and Mitigation,the Ministry of Science and Technology of China(Grant No.2023YFF0804704)the National Natural Science Foundation of China(Grant Nos.42130604,42075049,42475051,42205055,41971108&41971021)+3 种基金the National Natural Science Foundation International(regional)Cooperation and Exchange Project(Grant No.42111530182)the Open Fund Project of State Key Laboratory of Loess and Quaternary Geology,Institute of Earth Environment,Chinese Academy of Sciences(Grant Nos.SKLLQG1930&SKLLQG1820)the Priority Academic Program Development of Jiangsu Higher Education Institutions(Grant No.164320H116)the Jiangsu Graduate Research and Practice Innovation Program Project(Grant No.KYCX22_1582)。
文摘Generally,the interaction between the East Asian subtropical westerly jet(EASWJ)and the East Asian summer monsoon(EASM)is regarded as a critical dynamic factor in the evolution of precipitation patterns in China.Using simulation results from the transient climate evolution since the last glacial maximum,this study applies the multivariate empirical orthogonal function(MVEOF)analysis method to investigate the co-evolution relationships of the EASWJ and the EASM during the early,middle,and late Holocene,as well as their influence on precipitation patterns in China.The results indicate that all the first MVEOF modes in different time periods of the Holocene display an out-of-phase relationship in the intensity anomaly between the EASWJ and the EASM.However,the jet stream is wider and more tilted in the late Holocene.Under the influence of secondary circulations formed during their co-evolution,a north-south dipolar precipitation pattern in eastern China(“flood in the south and drought in the north”or“drought in the south and flood in the north”)and an east-west dipolar pattern in northern China(“wet in the west and dry in the east”or“wet in the east and dry in the west”)are found in the early and middle Holocene,while in the late Holocene a regionally-consistent precipitation pattern is witnessed across the whole region.In this mode,the precipitation in the middle and late Holocene is primarily dominated by trend changes.The second MVEOF mode reveals that the EASM weakens when the EASWJ shifts eastward and the jet steam axis shortens during the early Holocene,resulting in a north-south dipolar precipitation pattern in eastern China and a regionally-consistent pattern in northern China.In the middle Holocene,dipolar precipitation patterns are also observed in both eastern China and northern China when the EASWJ moves northward and the EASM strengthens,while the moisture condition in North China is less pronounced,and vice versa.In the late Holocene,the intensity anomalies of the EASWJ and the EASM exhibit an out-of-phase relationship in the temperate zone and an in-phase relationship in the subtropical zone,leading to a tripolar precipitation pattern in eastern China and a dipolar pattern in northern China.In this mode,the precipitation during the middle and late Holocene is primarily dominated by centennial oscillations.The precipitation patterns influenced by the co-evolution relationship between EASWJ and EASM correspond well with the reconstructed precipitation data,providing an explanation for the precipitation patterns observed in the reconstructed data from the perspective of dynamical mechanisms.
基金supported by the National Natural Science Foundation of China(No.62003258)Natural Science Foundation of Hebei Province(No.F2024204007)Projection of State Key Laboratory for Manufacturing Systems Engineering of Xi’an Jiaotong University(No.sklms 2023002).
文摘In this research,a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm(DHICCA)is proposed for addressing the distributed lot-streaming flowshop scheduling problem(DLSFSP)with the objective to minimize the makespan.A two-layer-vector representation is devised to bridge the solution space of DLSFSP and the search space of DHICCA.In the evolution of DHICCA,population individuals are endowed with heterogeneous identities according to their quality,including superior individuals,ordinary individuals,and inferior individuals,which serve local exploitation,global exploration,and diversified restart,respectively.Because individuals with different identities require different evolutionary mechanisms to fully unleash their respective potentials,identity-specific evolutionary operators are devised to evolve them in a cooperative co-evolutionary way.This is important to use limited population resources to solve complex optimization problems.Specifically,exploitation is carried out on superior individuals by devising three exploitative operators with different intensities based on techniques of variable neighborhood,destruction-construction,and gene targeting.Exploration is executed on ordinary individuals by a newly constructed discrete Jaya algorithm and a probability crossover strategy.In addition,restart is performed on inferior individuals to introduce new evolutionary individuals to the population.After the cooperative co-evolution,all individuals with different identities are merged as a population again,and their identities are dynamically adjusted by new evaluation.The influence of parameters on the algorithm is investigated based on design-of-experiment and comprehensive computational experiments are used to evaluate the performance of all algorithms.The results validate the effectiveness of special designs and show that DHICCA performs more efficient than the existing state-of-the-art algorithms in solving the DLSFSP.
文摘Plant-pathogen interactions involve complex biological processes that operate across molecular,cellular,microbiome,and ecological levels,significantly influencing plant health and agricultural productivity.In response to pathogenic threats,plants have developed sophisticated defense mechanisms,such as pattern-triggered immunity(PTI)and effector-triggered immunity(ETI),which rely on specialized recognition systems such as pattern recognition receptors(PRRs)and nucleotide-binding leucine-rich repeat(NLR)proteins.These immune responses activate intricate signaling pathways involving mitogen-activated protein kinase cascades,calcium fluxes,reactive oxygen species production,and hormonal cross-talk among salicylic acid,jasmonic acid,and ethylene.Furthermore,structural barriers such as callose deposition and lignification,along with the synthesis of secondary metabolites and antimicrobial enzymes,play crucial roles in inhibiting pathogen invasion and proliferation.The plant microbiome further enhances host immunity through beneficial associations with plant growth-promoting rhizobacteria(PGPR)and mycorrhizal fungi,which facilitate induced systemic resistance(ISR)and improve nutrient acquisition.As climate change exacerbates the impact of pathogens,these molecular and microbiome-driven defenses influence disease distribution and plant resilience,highlighting the importance of integrating ecological insights for sustainable disease management Advancements in microbiome engineering,including the application of synthetic microbial communities and commercial bio-inoculants,offer promising strategies for sustainable disease management.However,the impacts of climate change on pathogen virulence,host susceptibility,and disease distribution complicate these interactions,emphasizing the need for resilient and adaptive agricultural practices.This review highlights the necessity of a holistic,interdisciplinary approach that integrates multi-omics technologies,microbiome research,and ecological insights to develop effective and sustainable solutions for managing plant diseases and ensuring global food security.
基金This project was supported by the Natural Science Foundation of China(30460125)
文摘An investigation of sucking lice on the body surface of small mammals was carried out in the surrounding areas of Erhai Lake in Dali, Yunnan from 2003 to 2004. From investigation sites, 3 303 small mammal hosts were captured and identified into 7 families, 15 genera and 21 species in 4 orders （Rodentia, Insectivora, Scandentia and Carnivora）, while t4 635 individuals of sucking lice collected from the body surface of the small mammal hosts are identified into 5 families, 6 genera and 21 species in the Order Anoplura, The sites stand alongside three cordilleras surrounding the Erhai Lake, namely Eastern Wuliang Mountain, Southern Ailao Mountain and Western Cangshan Mountain. The three confined oriented areas are different landscapes within the same zone where the longitude, latitude, altitude and fauna are homologous but isolated by Erhai Lake as inartificial barrier. The aim of this study was to recognize features of the species diversity, abundance, community structure, similarity and distribution of sucking lice in different landscapes within the same zone. The results showed the species diversity of sucking lice was very low with a very simple community structure. The distribution of sucking lice and their corresponding hosts are quite uneven among different oriented areas and this may imply that ecological environment influences the species composition and distribution of sucking lice and their corresponding hosts. A certain species of hosts usually have their fixed louse species. The similarity of sucking louse communities is highly consistent with the affinity of small mammal hosts in taxonomy. Species of sucking lice on the same small mammal host in different oriented areas of Erhai Lake are homologous. The results strongly suggest a close relationship of co-evolution between sucking lice and their hosts.
基金国家自然科学基金 Grant No.61070009国家高技术研究发展计划(863计划) Grant No.2007AA01Z290~~
文摘多种群方法已被证明是提高演化算法动态优化性能的重要方法之一。提出了多种群热力学遗传算法(multi-population based thermodynamic genetic algorithm,MPTDGA)。该算法使用一个概率向量在热力学遗传算法迭代过程中不断演化优化与竞争学习,环境变化时分化成三个概率向量,并分别抽样产生原对偶和随机迁入三个子种群,依据这三个种群和记忆种群最好解的情况,选择新的工作概率向量进入新环境进行学习。在动态背包问题上的实验结果表明,MPTDGA比原对偶遗传算法跟踪最优解的能力更强,有很好的多样性,非常适合求解0-1动态优化问题。
基金Supported by Comisión Nacional de Investigación Científica y Tecnológica-Fondos de Financiamiento de Centros de Investigación enáreas Prioritarias,No.15130011(to Quest AF)Fondo Nacional de Desarrollo Científico y Tecnológico,No.1170925(to Quest AF)and No.1171615(to Valenzuela MA)Fondo para la Investigación en Odontología Universidad de Chile,No.17/020(to Bravo D)
文摘Helicobacter pylori(H.pylori)is present in roughly 50%of the human population worldwide and infection levels reach over 70%in developing countries.The infection has classically been associated with different gastro-intestinal diseases,but also with extra gastric diseases.Despite such associations,the bacterium frequently persists in the human host without inducing disease,and it has been suggested that H.pylori may also play a beneficial role in health.To understand how H.pylori can produce such diverse effects in the human host,several studies have focused on understanding the local and systemic effects triggered by this bacterium.One of the main mechanisms by which H.pylori is thought to damage the host is by inducing local and systemic inflammation.However,more recently,studies are beginning to focus on the effects of H.pylori and its metabolism on the gastric and intestinal microbiome.The objective of this review is to discuss how H.pylori has co-evolved with humans,how H.pylori presence is associated with positive and negative effects in human health and how inflammation and/or changes in the microbiome are associated with the observed outcomes.
