The selection pressure of genetic algorithm reveals the degree of balance between the global exploration and local optimization.A novel algorithm called the hybrid multi-population cellular genetic algorithm(HCGA)is p...The selection pressure of genetic algorithm reveals the degree of balance between the global exploration and local optimization.A novel algorithm called the hybrid multi-population cellular genetic algorithm(HCGA)is proposed,which combines population segmentation with particle swarm optimization(PSO).The control parameters are the number of individuals in the population and the number of subpopulations.By varying these control parameters,changes in selection pressure can be investigated.Population division is found to reduce the selection pressure.In particular,low selection pressure emerges in small and highly divided populations.Besides,slight or mild selection pressure reduces the convergence speed,and thus a new mutation operator accelerates the system.HPCGA is tested in the optimization of four typical functions and the results are compared with those of the conventional cellular genetic algorithm.HPCGA is found to significantly improve global convergence rate,convergence speed and stability.Population diversity is also investigated by HPCGA.Appropriate numbers of subpopulations not only achieve a better tradeoff between global exploration and local exploitation,but also greatly improve the optimization performance of HPCGA.It is concluded that HPCGA can elucidate the scientific basis for selecting the efficient numbers of subpopulations.展开更多
The aim of this study is to develop two-dimensional cellular automata model of HIV infection that depicts the dynamics involved in the interactions between acquired immune system and HIV infection in the peripheral bl...The aim of this study is to develop two-dimensional cellular automata model of HIV infection that depicts the dynamics involved in the interactions between acquired immune system and HIV infection in the peripheral blood stream. The appropriate biological rules of cellular automata model have been extracted from expert knowledge and the model has been simulated with determined initial conditions. Obtained results have been validated through comparing with the accepted AIDS reference curve. The new rules and states were added to the proposed model to show the effects of applying combined antiretroviral therapy. Our results showed that by applying RTI and PI drugs with maximum drug effectiveness, comparing with cases in which no treatment was applied, the steady state concentrations of healthy (infected) CD4+T cells were increased (decreased) 53% (41%). Also, the use of cART with maximum drug effectiveness led to a 69% reduction in the steady state level of viral load. At this time, obtained results have been validated through comparing with available clinical data. Our results showed good agreement with both reference curve and the clinical data. In the second phase of this study, by applying genetic algorithms, a therapeutic schedule has been provided that its use, while maintaining the quality of the treatment, leads to a 47% reduction in both drug dosage and the side effects of antiretroviral drugs.展开更多
This paper presents a new,bi-criteria mixed_integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system.The objective of this model is to minimize the makespan and int...This paper presents a new,bi-criteria mixed_integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system.The objective of this model is to minimize the makespan and intercell movements simultaneously,while considering sequence-dependent cell setup times.In the cellular manufacturing systems design and planning,three main steps must be considered,namely cell formation(i.e,piece families and machine grouping),inter and intra-cell layouts,and scheduling issue.Due to the fact that the cellular manufacturing systems problem is NP-Hard,a genetic algorithm as an efficient meta-heuristic method is proposed to solve such a hard problem.Finally,a number of test problems are solved to show the efficiency of the proposed genetic algorithm and the related computational results are compared with the results obtained by the use of an optimization tool.展开更多
Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul ar...Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network （ML-CNN） is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.展开更多
In this paper we describe an implementation of some kinds of parallel genetic algorithms on the PVM,Parallel Virtual Machine, a portable parallel environment. We give details of a genetic algorithm running On many sma...In this paper we describe an implementation of some kinds of parallel genetic algorithms on the PVM,Parallel Virtual Machine, a portable parallel environment. We give details of a genetic algorithm running On many small subpopulations with an occasional identification and exchange of their useful information among subpopulations by means of message-passing functions of PVM. In this work, experiments were done to compare the parallel genetic algorithm and traditional sequential genetic algorithms.展开更多
Due to the combinatorial nature of cell formation problem and the characteristics of multi-objective and multi-constrain, a novel method of evolutionary algorithm with preference is proposed. The analytic hierarchy pr...Due to the combinatorial nature of cell formation problem and the characteristics of multi-objective and multi-constrain, a novel method of evolutionary algorithm with preference is proposed. The analytic hierarchy process （AHP） is adopted to determine scientifically the weights of the sub-objective functions. The satisfaction of constraints is considered as a new objective, the ratio of the population which doesn＇t satisfy all constraints is assigned as the weight of new objective. In addition, the self-adaptation of weights is applied in order to converge more easily towards the feasible domain. Therefore, both features multi-criteria and constrains are dealt with simultaneously. Finally, an example is selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method in designing the manufacturing cells.展开更多
This paper presents a chaos-genetic algorithm (CGA) that combines chaos and genetic algorithms. It can be used to avoid trapping in local optima profiting from chaos'randomness,ergodicity and regularity. Its prope...This paper presents a chaos-genetic algorithm (CGA) that combines chaos and genetic algorithms. It can be used to avoid trapping in local optima profiting from chaos'randomness,ergodicity and regularity. Its property of global asymptotical convergence has been proved with Markov chains in this paper. CGA was applied to the optimization of complex benchmark functions and artificial neural network's (ANN) training. In solving the complex benchmark functions,CGA needs less iterative number than GA and other chaotic optimization algorithms and always finds the optima of these functions. In training ANN,CGA uses less iterative number and shows strong generalization. It is proved that CGA is an efficient and convenient chaotic optimization algorithm.展开更多
This paper demonstrates that cellular automata(CA) can be a useful tool for analyzing the process of many geographical phenomena.There are many studies on using CA to simulate the evolution of cites.Urban dynamics is ...This paper demonstrates that cellular automata(CA) can be a useful tool for analyzing the process of many geographical phenomena.There are many studies on using CA to simulate the evolution of cites.Urban dynamics is determined by many spatial variables.The contribution of each spatial variable to the simulation is quantified by its parameter or weight.Calibration procedures are usually required for obtaining a suitable set of parameters so that the realistic urban forms can be simulated.Each pa-rameter has a unique role in controlling urban morphology in the simulation.In this paper,these pa-rameters for urban simulation are determined by using empirical data.Genetic algorithms are used to search for the optimal combination of these parameters.There are spatial variations for urban dynam-ics in a large region.Distinct sets of parameters can be used to represent the unique features of urban dynamics for various subregions.A further experiment is to evaluate each set of parameters based on the theories of compact cities.It is considered that the better set of parameters can be identified ac-cording to the utility function in terms of compact development.This set of parameters can be cloned to other regions to improve overall urban morphology.The original parameters can be also modified to produce more compact urban forms for planning purposes.This approach can provide a useful ex-ploratory tool for testing various planning scenarios for urban development.展开更多
In this paper, a new parallel-by-cell approach to the undistorteddata compression based on cellular automaton and genetic algorithm is presented.The local compression rules in a cellular automaton are obtained by usin...In this paper, a new parallel-by-cell approach to the undistorteddata compression based on cellular automaton and genetic algorithm is presented.The local compression rules in a cellular automaton are obtained by using a geneticevolutionary algorithm. The correctness of the hyper-parallel compression, the timecomplexity, and the relevant symbolic dynamic behaviour are discussed. In comparison with other traditional sequential or small-scale parallel methods for undistorteddata compression, the proposed approach shows much higher real-time performance,better suitability and feasibility for the systolic hardware implementation.展开更多
基金Supported by National Natural Science Foundation of China(61262019)the Aeronautical Science Foundation of China(2012ZA56001)+2 种基金the Natural Science Foundation of Jiangxi Province(20114BAB201046)the Science and Technology Research Project of Jiangxi Provincial Department of Education(GJJ12435)the Open-End Foundation of the Key Laboratory of Nondestructive Testing(Ministry of Education)
文摘The selection pressure of genetic algorithm reveals the degree of balance between the global exploration and local optimization.A novel algorithm called the hybrid multi-population cellular genetic algorithm(HCGA)is proposed,which combines population segmentation with particle swarm optimization(PSO).The control parameters are the number of individuals in the population and the number of subpopulations.By varying these control parameters,changes in selection pressure can be investigated.Population division is found to reduce the selection pressure.In particular,low selection pressure emerges in small and highly divided populations.Besides,slight or mild selection pressure reduces the convergence speed,and thus a new mutation operator accelerates the system.HPCGA is tested in the optimization of four typical functions and the results are compared with those of the conventional cellular genetic algorithm.HPCGA is found to significantly improve global convergence rate,convergence speed and stability.Population diversity is also investigated by HPCGA.Appropriate numbers of subpopulations not only achieve a better tradeoff between global exploration and local exploitation,but also greatly improve the optimization performance of HPCGA.It is concluded that HPCGA can elucidate the scientific basis for selecting the efficient numbers of subpopulations.
文摘The aim of this study is to develop two-dimensional cellular automata model of HIV infection that depicts the dynamics involved in the interactions between acquired immune system and HIV infection in the peripheral blood stream. The appropriate biological rules of cellular automata model have been extracted from expert knowledge and the model has been simulated with determined initial conditions. Obtained results have been validated through comparing with the accepted AIDS reference curve. The new rules and states were added to the proposed model to show the effects of applying combined antiretroviral therapy. Our results showed that by applying RTI and PI drugs with maximum drug effectiveness, comparing with cases in which no treatment was applied, the steady state concentrations of healthy (infected) CD4+T cells were increased (decreased) 53% (41%). Also, the use of cART with maximum drug effectiveness led to a 69% reduction in the steady state level of viral load. At this time, obtained results have been validated through comparing with available clinical data. Our results showed good agreement with both reference curve and the clinical data. In the second phase of this study, by applying genetic algorithms, a therapeutic schedule has been provided that its use, while maintaining the quality of the treatment, leads to a 47% reduction in both drug dosage and the side effects of antiretroviral drugs.
文摘This paper presents a new,bi-criteria mixed_integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system.The objective of this model is to minimize the makespan and intercell movements simultaneously,while considering sequence-dependent cell setup times.In the cellular manufacturing systems design and planning,three main steps must be considered,namely cell formation(i.e,piece families and machine grouping),inter and intra-cell layouts,and scheduling issue.Due to the fact that the cellular manufacturing systems problem is NP-Hard,a genetic algorithm as an efficient meta-heuristic method is proposed to solve such a hard problem.Finally,a number of test problems are solved to show the efficiency of the proposed genetic algorithm and the related computational results are compared with the results obtained by the use of an optimization tool.
文摘Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network （ML-CNN） is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.
文摘In this paper we describe an implementation of some kinds of parallel genetic algorithms on the PVM,Parallel Virtual Machine, a portable parallel environment. We give details of a genetic algorithm running On many small subpopulations with an occasional identification and exchange of their useful information among subpopulations by means of message-passing functions of PVM. In this work, experiments were done to compare the parallel genetic algorithm and traditional sequential genetic algorithms.
基金supported by National Natural Science Foundation of China(No. 50575026)Excellent Youth Talents Foundation of Liaoning Province, China. (No. 3040014).
文摘Due to the combinatorial nature of cell formation problem and the characteristics of multi-objective and multi-constrain, a novel method of evolutionary algorithm with preference is proposed. The analytic hierarchy process （AHP） is adopted to determine scientifically the weights of the sub-objective functions. The satisfaction of constraints is considered as a new objective, the ratio of the population which doesn＇t satisfy all constraints is assigned as the weight of new objective. In addition, the self-adaptation of weights is applied in order to converge more easily towards the feasible domain. Therefore, both features multi-criteria and constrains are dealt with simultaneously. Finally, an example is selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method in designing the manufacturing cells.
基金Sponsored by the National Natural Science Foundation of China(Grant No. 60674024)the Initial Foundation of Civil Aviation University of China(Grant No. 06QD04x)
文摘This paper presents a chaos-genetic algorithm (CGA) that combines chaos and genetic algorithms. It can be used to avoid trapping in local optima profiting from chaos'randomness,ergodicity and regularity. Its property of global asymptotical convergence has been proved with Markov chains in this paper. CGA was applied to the optimization of complex benchmark functions and artificial neural network's (ANN) training. In solving the complex benchmark functions,CGA needs less iterative number than GA and other chaotic optimization algorithms and always finds the optima of these functions. In training ANN,CGA uses less iterative number and shows strong generalization. It is proved that CGA is an efficient and convenient chaotic optimization algorithm.
基金Supported by the National Outstanding Youth Foundation of China (Grant No 40525002)the National Natural Science Foundation of China (Grant No 40471105)the Hi-tech Research and Development Program of China (863 Program) (Grant No 2006AA12Z206)
文摘This paper demonstrates that cellular automata(CA) can be a useful tool for analyzing the process of many geographical phenomena.There are many studies on using CA to simulate the evolution of cites.Urban dynamics is determined by many spatial variables.The contribution of each spatial variable to the simulation is quantified by its parameter or weight.Calibration procedures are usually required for obtaining a suitable set of parameters so that the realistic urban forms can be simulated.Each pa-rameter has a unique role in controlling urban morphology in the simulation.In this paper,these pa-rameters for urban simulation are determined by using empirical data.Genetic algorithms are used to search for the optimal combination of these parameters.There are spatial variations for urban dynam-ics in a large region.Distinct sets of parameters can be used to represent the unique features of urban dynamics for various subregions.A further experiment is to evaluate each set of parameters based on the theories of compact cities.It is considered that the better set of parameters can be identified ac-cording to the utility function in terms of compact development.This set of parameters can be cloned to other regions to improve overall urban morphology.The original parameters can be also modified to produce more compact urban forms for planning purposes.This approach can provide a useful ex-ploratory tool for testing various planning scenarios for urban development.
文摘In this paper, a new parallel-by-cell approach to the undistorteddata compression based on cellular automaton and genetic algorithm is presented.The local compression rules in a cellular automaton are obtained by using a geneticevolutionary algorithm. The correctness of the hyper-parallel compression, the timecomplexity, and the relevant symbolic dynamic behaviour are discussed. In comparison with other traditional sequential or small-scale parallel methods for undistorteddata compression, the proposed approach shows much higher real-time performance,better suitability and feasibility for the systolic hardware implementation.
