Sparse Large-scale Multi-objective Optimization Problems(sparse LMOPs)widely exist in various optimization applications,such as neural network training,portfolio optimization,and feature selection of classification.Al...Sparse Large-scale Multi-objective Optimization Problems(sparse LMOPs)widely exist in various optimization applications,such as neural network training,portfolio optimization,and feature selection of classification.Although numerous methods exist,automatically selecting efficient solving strategies for sparse LMOPs remains highly challenging.Given this,we propose a reinforcement learning assisted autonomous sparse multi-objective evolutionary algorithm,which aims to effectively utilize sparse knowledge for designing diversified genetic operators,and automatically select appropriate genetic operators for various problems or different situations within the same optimization process.Specifically,three sparsity-aware genetic operators are designed by utilizing sparsity statistic,sparsity clustering,and sparsity logic operation.They possess distinct advantages in terms of convergence speed,solution quality,and diversity.Furthermore,the utilization of deep Q-network enables the automatic selection of suitable operators for offspring reproduction based on the current sparse state of the population.The proposed algorithm is compared with five state-of-the-art algorithms on eight benchmark and three real-world problems.Experimental results demonstrate the superiority of the proposed algorithm and the effectiveness of the proposed sparse genetic operators for solving sparse LMOPs.展开更多
In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objectiv...In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.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
The underground water quality model with non-linear inversion problem is ill-posed, and boils down to solving the minimum of nonlinear function. Genetic algorithms are adopted in a number of individuals of groups by i...The underground water quality model with non-linear inversion problem is ill-posed, and boils down to solving the minimum of nonlinear function. Genetic algorithms are adopted in a number of individuals of groups by iterative search to find the optimal solution of the problem, the encoding strings as its operational objective, and achieving the iterative calculations by the genetic operators. It is an effective method of inverse problems of groundwater, with incomparable advantages and practical significances.展开更多
The genetic algorithms represent a family of algorithms using some of genetic principles being present in nature,in order to solve particular computational problems.These natural principles are:inheritance,crossover,m...The genetic algorithms represent a family of algorithms using some of genetic principles being present in nature,in order to solve particular computational problems.These natural principles are:inheritance,crossover,mutation,survival of the fittest,migrations and so on.The paper describes the most important aspects of a genetic algorithm as a stochastic method for solving various classes of optimization problems.It also describes the basic genetic operator selection,crossover and mutation,serving for a new generation of individuals to achieve an optimal or a good enough solution of an optimization problem being in question.展开更多
An improved genetic algorithm for searching optimal parameters in n-dimensional space is presented, which encodes movement direction and distance and searches from coarse to precise. The algorithm can realize global o...An improved genetic algorithm for searching optimal parameters in n-dimensional space is presented, which encodes movement direction and distance and searches from coarse to precise. The algorithm can realize global optimization and improve the search efficiency, and can be applied effectively in industrial optimization, data mining and pattern recognition.展开更多
This paper discusses the convergence rates about a class of evolutionary algorithms in general search spaces by means of the ergodic theory in Markov chain and some techniques in Banach algebra.Under certain condition...This paper discusses the convergence rates about a class of evolutionary algorithms in general search spaces by means of the ergodic theory in Markov chain and some techniques in Banach algebra.Under certain conditions that transition probability functions of Markov chains corresponding to evolutionary algorithms satisfy,the authors obtain the convergence rates of the exponential order.Furthermore,they also analyze the characteristics of the conditions which can be met by genetic operators and selection strategies.展开更多
This paper is concerned with evolving objects method for software design that can adapt to the changing environments and requirements automatically. We present system architecture with objects library, where there are...This paper is concerned with evolving objects method for software design that can adapt to the changing environments and requirements automatically. We present system architecture with objects library, where there are objects based on domain ontologies. We define some genetic operators for objects, and discuss how to apply these genetic operators on objects to get new objects, which can satisfy new requirements.展开更多
The utilization of industrial biomanufacturing has emerged as a viable and sustainable alternative to fossil-based resources for producing functional chemicals.Moreover,advancements in synthetic biology have created n...The utilization of industrial biomanufacturing has emerged as a viable and sustainable alternative to fossil-based resources for producing functional chemicals.Moreover,advancements in synthetic biology have created new opportunities for the development of innovative cell factories.Notably,Yarrowia lipolytica,an oleaginous yeast that is generally regarded as safe,possesses several advantageous characteristics,including the ability to utilize inexpensive renewable carbon sources,well-established genetic backgrounds,and mature genetic manipulation methods.Consequently,there is increasing interest in manipulating the metabolism of this yeast to enhance its potential as a biomanufacturing platform.Here,we reviewed the latest developments in genetic expression strategies and manipulation tools related to Y.lipolytica,particularly focusing on gene expression,chromosomal operation,CRISPR-based tool,and dynamic biosensors.The purpose of this review is to serve as a valuable reference for those interested in the development of a Y.lipolytica microbial factory.展开更多
Tourism development in emerging destinations requires balancing economic benefits with ecological sustainability.In this study,we investigate the case of multi-attraction tourism planning in Qujing City,where the dual...Tourism development in emerging destinations requires balancing economic benefits with ecological sustainability.In this study,we investigate the case of multi-attraction tourism planning in Qujing City,where the dual challenge lies in maximizing economic-experiential value while minimizing congestion-eco-logical stress.We formulate this problem as a bi-objective optimization model,integrating attraction revenues,visitor preferences,route costs,and site capacities into a unified framework.To solve the model,we employ NSGA-II en-hanced with customized crossover and mutation operators specifically designed for route structures and visitor allocations.These operators enable efficient exploration of feasible solutions while maintaining capacity and time-window constraints.Extensive experiments across different scales of scenic scenarios demonstrate that our method consistently outperforms greedy and randomized baselines in terms of hypervolume and sustainability indicators.The results highlight the effectiveness of incorporating problem-specific operators into evo-lutionary algorithms and provide practical insights for sustainable tourism man-agement in Qujing and other similar destinations.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62303013,62276001,and U21A20512).
文摘Sparse Large-scale Multi-objective Optimization Problems(sparse LMOPs)widely exist in various optimization applications,such as neural network training,portfolio optimization,and feature selection of classification.Although numerous methods exist,automatically selecting efficient solving strategies for sparse LMOPs remains highly challenging.Given this,we propose a reinforcement learning assisted autonomous sparse multi-objective evolutionary algorithm,which aims to effectively utilize sparse knowledge for designing diversified genetic operators,and automatically select appropriate genetic operators for various problems or different situations within the same optimization process.Specifically,three sparsity-aware genetic operators are designed by utilizing sparsity statistic,sparsity clustering,and sparsity logic operation.They possess distinct advantages in terms of convergence speed,solution quality,and diversity.Furthermore,the utilization of deep Q-network enables the automatic selection of suitable operators for offspring reproduction based on the current sparse state of the population.The proposed algorithm is compared with five state-of-the-art algorithms on eight benchmark and three real-world problems.Experimental results demonstrate the superiority of the proposed algorithm and the effectiveness of the proposed sparse genetic operators for solving sparse LMOPs.
文摘In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.
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.
文摘The underground water quality model with non-linear inversion problem is ill-posed, and boils down to solving the minimum of nonlinear function. Genetic algorithms are adopted in a number of individuals of groups by iterative search to find the optimal solution of the problem, the encoding strings as its operational objective, and achieving the iterative calculations by the genetic operators. It is an effective method of inverse problems of groundwater, with incomparable advantages and practical significances.
文摘The genetic algorithms represent a family of algorithms using some of genetic principles being present in nature,in order to solve particular computational problems.These natural principles are:inheritance,crossover,mutation,survival of the fittest,migrations and so on.The paper describes the most important aspects of a genetic algorithm as a stochastic method for solving various classes of optimization problems.It also describes the basic genetic operator selection,crossover and mutation,serving for a new generation of individuals to achieve an optimal or a good enough solution of an optimization problem being in question.
文摘An improved genetic algorithm for searching optimal parameters in n-dimensional space is presented, which encodes movement direction and distance and searches from coarse to precise. The algorithm can realize global optimization and improve the search efficiency, and can be applied effectively in industrial optimization, data mining and pattern recognition.
基金This work is supported by the National Natural Science Foundation of ChinaVisiting Scholar Foundation of Key Lab,in Univers
文摘This paper discusses the convergence rates about a class of evolutionary algorithms in general search spaces by means of the ergodic theory in Markov chain and some techniques in Banach algebra.Under certain conditions that transition probability functions of Markov chains corresponding to evolutionary algorithms satisfy,the authors obtain the convergence rates of the exponential order.Furthermore,they also analyze the characteristics of the conditions which can be met by genetic operators and selection strategies.
文摘This paper is concerned with evolving objects method for software design that can adapt to the changing environments and requirements automatically. We present system architecture with objects library, where there are objects based on domain ontologies. We define some genetic operators for objects, and discuss how to apply these genetic operators on objects to get new objects, which can satisfy new requirements.
基金supported by the National Natural Science Foundation of China 22108126 to Y.G.The China Postdoctoral Science Foundation 2022M721362 to Y.Gthe Natural Science Foundation of Jiangsu Province BK20210572 and BK20202002 and to Y.G.and H.H.,respectively.
文摘The utilization of industrial biomanufacturing has emerged as a viable and sustainable alternative to fossil-based resources for producing functional chemicals.Moreover,advancements in synthetic biology have created new opportunities for the development of innovative cell factories.Notably,Yarrowia lipolytica,an oleaginous yeast that is generally regarded as safe,possesses several advantageous characteristics,including the ability to utilize inexpensive renewable carbon sources,well-established genetic backgrounds,and mature genetic manipulation methods.Consequently,there is increasing interest in manipulating the metabolism of this yeast to enhance its potential as a biomanufacturing platform.Here,we reviewed the latest developments in genetic expression strategies and manipulation tools related to Y.lipolytica,particularly focusing on gene expression,chromosomal operation,CRISPR-based tool,and dynamic biosensors.The purpose of this review is to serve as a valuable reference for those interested in the development of a Y.lipolytica microbial factory.
基金supported by Qujing Social Science Federation-Qujing Normal University Philosophy and Social Sciences Joint Special Project(ZSLH2023YB05).
文摘Tourism development in emerging destinations requires balancing economic benefits with ecological sustainability.In this study,we investigate the case of multi-attraction tourism planning in Qujing City,where the dual challenge lies in maximizing economic-experiential value while minimizing congestion-eco-logical stress.We formulate this problem as a bi-objective optimization model,integrating attraction revenues,visitor preferences,route costs,and site capacities into a unified framework.To solve the model,we employ NSGA-II en-hanced with customized crossover and mutation operators specifically designed for route structures and visitor allocations.These operators enable efficient exploration of feasible solutions while maintaining capacity and time-window constraints.Extensive experiments across different scales of scenic scenarios demonstrate that our method consistently outperforms greedy and randomized baselines in terms of hypervolume and sustainability indicators.The results highlight the effectiveness of incorporating problem-specific operators into evo-lutionary algorithms and provide practical insights for sustainable tourism man-agement in Qujing and other similar destinations.
