In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction(CCAEMR) algorith...In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction(CCAEMR) algorithm is proposed.First, a co-evolutionary cloud framework is designed under the M apReduce mechanism to divide the entire population into different co-evolutionary subpopulations with a self-adaptive scale. Meanwhile, these subpopulations will share their rewards to accelerate attribute reduction implementation.Secondly, a multi-agent ensemble strategy of co-evolutionary elitist optimization is constructed to ensure that subpopulations can exploit any correlation and interdependency between interacting attribute subsets with reinforcing noise tolerance.Hence, these agents are kept within the stable elitist region to achieve the optimal profit. The experimental results show that the proposed CCAEMR algorithm has better efficiency and feasibility to solve large-scale and uncertain dataset problems with complex noise.展开更多
This paper proposes a co-evolutionary recurrent neural network (CERNN) for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structu...This paper proposes a co-evolutionary recurrent neural network (CERNN) for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent neural networks by coevolutionary strategy. The searching space was separated into two subspaces and the individuals are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability of recurrent neural network to incorporate past experience due to internal recurrence. The effectiveness of CERNN is evaluated by using three benchmark chaotic time series data sets: the Lorenz series, Mackey-Glass series and real-world sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic time series.展开更多
The article discusses the payment for ecosystem or environmental services markets in Brazil with a critical review, based on the ecological economics literature and focused on the concept of co-evolution. It is argued...The article discusses the payment for ecosystem or environmental services markets in Brazil with a critical review, based on the ecological economics literature and focused on the concept of co-evolution. It is argued that the mainstream approach which considers ecosystem services as an externality has many shortcomings and fails to consider institutional and political aspects---all very critical for the design and implementation of a PES (Payment for ecosystem services) project or program. The complexity and the diversity of co-evolutionary relations between ecosystem services and socioeconomic activities are spatially or territorially specific. In this sense, different types of PES market have to adapt and coevolve with different ongoing development processes.展开更多
The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborativ...The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.展开更多
Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D pa...Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D path planning, a modified ant colony optimization is proposed in this paper. Firstly, the grid method for environment modeling is adopted. Heuristic information is connected with the planning space. A semi-iterative global pheromone update mechanism is proposed. Secondly, the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number. Thirdly, co-evolutionary algorithm is used. Finally, the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.展开更多
The co-evolutionary dynamics of a cyclic game system is investigated in a two-dimensional square lattice with the asymmetrical rates for three species. Different with the well-mixed system, coexistence and extinction ...The co-evolutionary dynamics of a cyclic game system is investigated in a two-dimensional square lattice with the asymmetrical rates for three species. Different with the well-mixed system, coexistence and extinction emerge alternately in the system, where a "zero-one" behavior is robust for a small population size, whereas, the system is predominated by coexistence for a big population one. We study in detail the influence about the fluctuation to the change of the state, and find that the difference between the maximal amplitude about the fluctuation and the average intensity determines which state the system is ultimately. In addition, we introduce Ports energy to explain the reason of the "zero-one" behavior. It is shown that the average Ports energy per site is the distance to the "zero-one" behavior in the model.展开更多
A competitive co-evolutionary Multi-Objective Genetic Algorithm (cc-MOGA) was used to approximate a Pareto front of efficient silvicultural regimes for Eucalyptus fastigata. The three objectives to be maximised includ...A competitive co-evolutionary Multi-Objective Genetic Algorithm (cc-MOGA) was used to approximate a Pareto front of efficient silvicultural regimes for Eucalyptus fastigata. The three objectives to be maximised included, sawlog, pulpwood and carbon sequestration payment. Three carbon price scenarios (3CPS), i.e. NZ $25, NZ $50 and NZ $100 for a tonne of CO2 sequestered, were used to assess the impact on silvicultural regimes, against a fourth non-carbon Pareto set of efficient regimes (nonCPS), determined from a cc-MOGA with two objectives, i.e. competing sawlog and pulpwood productions. Carbon prices included in stand valuation were found to influence the silvicultural regimes by increasing the rotation length and lowering the final crop number before clearfell. However, there were no significant changes in the frequency, timing, and intensity of thinning operations amongst all the four Pareto sets of solutions. However, the 3CPS were not significantly different from each other, which meant that these silvicultural regimes were insensitive to the price of carbon. This was because maximising carbon sequestration was directly related to the biological growth rate. As such an optimal mix of frequency, intensity, and timing of thinning maintained maximum growth rate for as long as possible for any one rotation.展开更多
The discipline of plant immunity has developed rapidly in the 21st century and is marked by substantial progress toward understanding the molecular mechanisms of plant immune recognition and the pathogenicity of micro...The discipline of plant immunity has developed rapidly in the 21st century and is marked by substantial progress toward understanding the molecular mechanisms of plant immune recognition and the pathogenicity of microorganisms.Here,we highlight the recent advances in plant immunity studies made by Yule Liu and his team at Tsinghua University in Beijing,China.展开更多
Nestling rejection is a rare type of host defense against brood parasitism compared with egg rejection.Theoretically,host defenses at both egg and nestling stages could be based on similar underlying discrimination me...Nestling rejection is a rare type of host defense against brood parasitism compared with egg rejection.Theoretically,host defenses at both egg and nestling stages could be based on similar underlying discrimination mechanisms but,due to the rarity of nestling rejector hosts,few studies have actually tested this hypothesis.We investigated egg and nestling discrimination by the fan-tailed gerygone Gerygone flavolateralis,a host that seemingly accepts nonmimetic eggs of its parasite,the shining bronze-cuckoo Chalcites lucidus,but ejects mimetic parasite nestlings.We introduced artificial eggs or nestlings and foreign gerygone nestlings in gerygone nests and compared begging calls of parasite and host nestlings.We found that the gerygone ejected artificial eggs only if their size was smaller than the parasite or host eggs.Ejection of artificial nestlings did not depend on whether their color matched that of the brood.The frequency of ejection increased during the course of the breeding season mirroring the increase in ejection frequency of parasite nestlings by the host.Cross-fostered gerygone nestlings were frequently ejected when lacking natal down and when introduced in the nest before hatching of the foster brood,but only occasionally when they did not match the color of the foster brood.Begging calls differed significantly between parasite and host nestlings throughout the nestling period.Our results suggest that the fan-tailed gerygone accepts eggs within the size range of gerygone and cuckoo eggs and that nestling discrimination is based on auditory and visual cues other than skin color.This highlights the importance of using a combined approach to study discrimination mechanisms of hosts.展开更多
基金The National Natural Science Foundation of China(No.61300167)the Open Project Program of State Key Laboratory for Novel Software Technology of Nanjing University(No.KFKT2015B17)+3 种基金the Natural Science Foundation of Jiangsu Province(No.BK20151274)Qing Lan Project of Jiangsu Provincethe Open Project Program of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education(No.JYB201606)the Program for Special Talent in Six Fields of Jiangsu Province(No.XYDXXJS-048)
文摘In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction(CCAEMR) algorithm is proposed.First, a co-evolutionary cloud framework is designed under the M apReduce mechanism to divide the entire population into different co-evolutionary subpopulations with a self-adaptive scale. Meanwhile, these subpopulations will share their rewards to accelerate attribute reduction implementation.Secondly, a multi-agent ensemble strategy of co-evolutionary elitist optimization is constructed to ensure that subpopulations can exploit any correlation and interdependency between interacting attribute subsets with reinforcing noise tolerance.Hence, these agents are kept within the stable elitist region to achieve the optimal profit. The experimental results show that the proposed CCAEMR algorithm has better efficiency and feasibility to solve large-scale and uncertain dataset problems with complex noise.
基金Project supported by the State Key Program of National Natural Science of China (Grant No 30230350)the Natural Science Foundation of Guangdong Province,China (Grant No 07006474)
文摘This paper proposes a co-evolutionary recurrent neural network (CERNN) for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent neural networks by coevolutionary strategy. The searching space was separated into two subspaces and the individuals are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability of recurrent neural network to incorporate past experience due to internal recurrence. The effectiveness of CERNN is evaluated by using three benchmark chaotic time series data sets: the Lorenz series, Mackey-Glass series and real-world sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic time series.
文摘The article discusses the payment for ecosystem or environmental services markets in Brazil with a critical review, based on the ecological economics literature and focused on the concept of co-evolution. It is argued that the mainstream approach which considers ecosystem services as an externality has many shortcomings and fails to consider institutional and political aspects---all very critical for the design and implementation of a PES (Payment for ecosystem services) project or program. The complexity and the diversity of co-evolutionary relations between ecosystem services and socioeconomic activities are spatially or territorially specific. In this sense, different types of PES market have to adapt and coevolve with different ongoing development processes.
基金supported by the National Key R&D Program of China(2018AAA0101700)the Program for HUST Academic Frontier Youth Team(2017QYTD04).
文摘The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.
基金Supported by National Natural Science Foundation of China (50875165)
文摘Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D path planning, a modified ant colony optimization is proposed in this paper. Firstly, the grid method for environment modeling is adopted. Heuristic information is connected with the planning space. A semi-iterative global pheromone update mechanism is proposed. Secondly, the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number. Thirdly, co-evolutionary algorithm is used. Finally, the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.
基金Supported by Natural Science Foundation of China under Grant No.10974146the Zhejiang Natural Science Foundation of China under Grant No.Y6090222the Wenzhou Science & Technology Bureau under Grant No.R20080059
文摘The co-evolutionary dynamics of a cyclic game system is investigated in a two-dimensional square lattice with the asymmetrical rates for three species. Different with the well-mixed system, coexistence and extinction emerge alternately in the system, where a "zero-one" behavior is robust for a small population size, whereas, the system is predominated by coexistence for a big population one. We study in detail the influence about the fluctuation to the change of the state, and find that the difference between the maximal amplitude about the fluctuation and the average intensity determines which state the system is ultimately. In addition, we introduce Ports energy to explain the reason of the "zero-one" behavior. It is shown that the average Ports energy per site is the distance to the "zero-one" behavior in the model.
文摘A competitive co-evolutionary Multi-Objective Genetic Algorithm (cc-MOGA) was used to approximate a Pareto front of efficient silvicultural regimes for Eucalyptus fastigata. The three objectives to be maximised included, sawlog, pulpwood and carbon sequestration payment. Three carbon price scenarios (3CPS), i.e. NZ $25, NZ $50 and NZ $100 for a tonne of CO2 sequestered, were used to assess the impact on silvicultural regimes, against a fourth non-carbon Pareto set of efficient regimes (nonCPS), determined from a cc-MOGA with two objectives, i.e. competing sawlog and pulpwood productions. Carbon prices included in stand valuation were found to influence the silvicultural regimes by increasing the rotation length and lowering the final crop number before clearfell. However, there were no significant changes in the frequency, timing, and intensity of thinning operations amongst all the four Pareto sets of solutions. However, the 3CPS were not significantly different from each other, which meant that these silvicultural regimes were insensitive to the price of carbon. This was because maximising carbon sequestration was directly related to the biological growth rate. As such an optimal mix of frequency, intensity, and timing of thinning maintained maximum growth rate for as long as possible for any one rotation.
文摘The discipline of plant immunity has developed rapidly in the 21st century and is marked by substantial progress toward understanding the molecular mechanisms of plant immune recognition and the pathogenicity of microorganisms.Here,we highlight the recent advances in plant immunity studies made by Yule Liu and his team at Tsinghua University in Beijing,China.
基金This study was funded by the National Science Centre,Poland:NCN 2012/05/E/NZ8/02694 and NCN 2016/23/B/NZ8/03082the Japan Society for Promotion of Science(JSPS):grant no.24-4578(to N.J.S.)+1 种基金24770028(to K.D.T.),23255004(to K.U.)by Rikkyo University:SFR 11-54(to N.J.S.).
文摘Nestling rejection is a rare type of host defense against brood parasitism compared with egg rejection.Theoretically,host defenses at both egg and nestling stages could be based on similar underlying discrimination mechanisms but,due to the rarity of nestling rejector hosts,few studies have actually tested this hypothesis.We investigated egg and nestling discrimination by the fan-tailed gerygone Gerygone flavolateralis,a host that seemingly accepts nonmimetic eggs of its parasite,the shining bronze-cuckoo Chalcites lucidus,but ejects mimetic parasite nestlings.We introduced artificial eggs or nestlings and foreign gerygone nestlings in gerygone nests and compared begging calls of parasite and host nestlings.We found that the gerygone ejected artificial eggs only if their size was smaller than the parasite or host eggs.Ejection of artificial nestlings did not depend on whether their color matched that of the brood.The frequency of ejection increased during the course of the breeding season mirroring the increase in ejection frequency of parasite nestlings by the host.Cross-fostered gerygone nestlings were frequently ejected when lacking natal down and when introduced in the nest before hatching of the foster brood,but only occasionally when they did not match the color of the foster brood.Begging calls differed significantly between parasite and host nestlings throughout the nestling period.Our results suggest that the fan-tailed gerygone accepts eggs within the size range of gerygone and cuckoo eggs and that nestling discrimination is based on auditory and visual cues other than skin color.This highlights the importance of using a combined approach to study discrimination mechanisms of hosts.