摘要 :
Three different kinds of the novel enhanced genetic algorithm procedures including the hybrid genetic algorithm, interval genetic algorithm and hybrid interval genetic algorithm are respectively presented. As the results of the pr...
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Three different kinds of the novel enhanced genetic algorithm procedures including the hybrid genetic algorithm, interval genetic algorithm and hybrid interval genetic algorithm are respectively presented. As the results of the proven systems show, the hybrid genetic algorithm can determines the better optimum design than the traditional optimization algorithms and genetic algorithm. The interval genetic algorithm and hybrid interval genetic algorithm can avoid calculating system slope in traditional interval analysis and determines the optimum interval range of the parameters under allowable corresponding objective error boundary. It is the first time that genetic algorithm has been applied to interval optimization process.
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摘要 :
Three different kinds of the novel enhanced genetic algorithm procedures including the hybrid genetic algorithm, interval genetic algorithm and hybrid interval genetic algorithm are respectively presented. As the results of the pr...
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Three different kinds of the novel enhanced genetic algorithm procedures including the hybrid genetic algorithm, interval genetic algorithm and hybrid interval genetic algorithm are respectively presented. As the results of the proven systems show, the hybrid genetic algorithm can determines the better optimum design than the traditional optimization algorithms and genetic algorithm. The interval genetic algorithm and hybrid interval genetic algorithm can avoid calculating system slope in traditional interval analysis and determines the optimum interval range of the parameters under allowable corresponding objective error boundary. It is the first time that genetic algorithm has been applied to interval optimization process.
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In this paper we evaluates the effectiveness of three different distributed genetic algorithms (DGAs). The first one is DGA with Diversity Guided Migration, second one is DGA with Automated Adaptive Migration and the last one is D...
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In this paper we evaluates the effectiveness of three different distributed genetic algorithms (DGAs). The first one is DGA with Diversity Guided Migration, second one is DGA with Automated Adaptive Migration and the last one is DGA with Bi-coded chromosomes and confidence rates. All these algorithms were investigated to improve the overall quality of solutions in the distributed genetic algorithm for different problems. Our comparison between those algorithms depended on some important factors; like, achieving diversity in selecting individuals, process of replacing the individuals between subpopulations, computational time and memory space. As a result, DGA with Diversity Guided Migration (DGM), was nominated to be better than the other DGAs. It improves the performance for many problems and search spaces.
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摘要 :
In this paper we evaluates the effectiveness of three different distributed genetic algorithms (DGAs). The first one is DGA with Diversity Guided Migration, second one is DGA with Automated Adaptive Migration and the last one is D...
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In this paper we evaluates the effectiveness of three different distributed genetic algorithms (DGAs). The first one is DGA with Diversity Guided Migration, second one is DGA with Automated Adaptive Migration and the last one is DGA with Bi-coded chromosomes and confidence rates. All these algorithms were investigated to improve the overall quality of solutions in the distributed genetic algorithm for different problems. Our comparison between those algorithms depended on some important factors ;like, achieving diversity in selecting individuals, process of replacing the individuals between subpopulations, computational time and memory space. As a result, DGA with Diversity Guided Migration (DGM), was nominated to be better than the other DGAs. It improves the performance for many problems and search spaces.
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It is usually difficult to find a balance among some of the important parameters when using an evolutionary algorithm (EA) (number of runs, population size and generations) and at the same time saving computing time. Recently, som...
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It is usually difficult to find a balance among some of the important parameters when using an evolutionary algorithm (EA) (number of runs, population size and generations) and at the same time saving computing time. Recently, some papers have dealt with population size and optimal numbers of populations, while others have instead focused on a different couple of parameters, and scarcely the three parameters have been considered simultaneously. In this paper we consider simultaneously all of them. Computing effort is used through experimental results section to evaluate the proposed alternatives. Experimental results confirm some conclusions obtained on previous works with only two parameters and also give some guidelines on the way of distributing efficiently resources when designing parallel implementations of EAs.
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摘要 :
It is usually difficult to find a balance among some of the important parameters when using an evolutionary algorithm (EA) (number of runs, population size and generations) and at the same time saving computing time. Recently, som...
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It is usually difficult to find a balance among some of the important parameters when using an evolutionary algorithm (EA) (number of runs, population size and generations) and at the same time saving computing time. Recently, some papers have dealt with population size and optimal numbers of populations, while others have instead focused on a different couple of parameters, and scarcely the three parameters have been considered simultaneously. In this paper we consider simultaneously all of them. Computing effort is used through experimental results section to evaluate the proposed alternatives. Experimental results confirm some conclusions obtained on previous works with only two parameters and also give some guidelines on the way of distributing efficiently resources when designing parallel implementations of EAs.
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By using the feedback of genetic information and heuristic rules, and incorporating local searching algorithms, rational genetic algorithm (RGA) is proposed to overcome the drawbacks of conventional genetic algorithms (GAs) such a...
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By using the feedback of genetic information and heuristic rules, and incorporating local searching algorithms, rational genetic algorithm (RGA) is proposed to overcome the drawbacks of conventional genetic algorithms (GAs) such as slow convergence. Genetic Information was defined, which is the feed-back information derived from evolutionary process to supervise GA's operations. Furthermore, heuristic rules and local searching algorithms were also effectively incorporated in RGA to enhance the correctness of genetic operations. Finally, a general specification for the whole RGA was provided. RGA effectively incorporates inheriting and learning behaviors of knowledge and experiences in species into conventional GA. From the theoretical analysis of RGA and case studies in practical application to path planning problems of robots, it can be seen that the proposed RGA has faster convergence speed, and can converge to the global optimal solution.
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A Chaotic Genetic Algorithm (CGA) for Cognitive Radio spectrum allocation procedure is presented The development of the Cognitive radio system puts emphasis on the efficient utilization of spectrum for both primary and secondary u...
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A Chaotic Genetic Algorithm (CGA) for Cognitive Radio spectrum allocation procedure is presented The development of the Cognitive radio system puts emphasis on the efficient utilization of spectrum for both primary and secondary users. Secondary users make use of the spectrum without degrading the quality of service of the primary user(s). We assume that spectrum sensing has been done; thus a secondary user can specify the Quality of Service (QoS) requirements for a particular application at any given time. A Genetic Algorithm is used for the spectrum allocation. We have compared the performance of a Traditional Genetic Algorithm (TGA) with the chaotic counterpart. The simulation shows that the CGA converges faster with better fitness than the TGA. The simulation has been modeled using MATLAB.
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This paper presets a set of tools that can be used illustrate various forms of genetic algorithms. This includes both forms of genetic algorithms "Binary Genetic Algorithms" and "Continuous Parameter Genetic Algorithms". Genetic a...
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This paper presets a set of tools that can be used illustrate various forms of genetic algorithms. This includes both forms of genetic algorithms "Binary Genetic Algorithms" and "Continuous Parameter Genetic Algorithms". Genetic algorithms provide heuristic solutions for combinatorial-optimization problems that have found applications in many areas with outstanding success. In this package there are four optimization tools. The first one is to perform exhaustive search method; the second to implement approximation method; the third to implement binary genetic algorithm; and the fourth to implement continuous parameter genetic algorithm. Only the third and the fourth ones are discussed in this paper that are related to genetic algorithms. All the four software packages are developed using Borland C++ Builder 5.0. The tools are user-friendly with graphical user interface (GUI). Most of the genetic algorithmic parameters are changeable, making them useful in understanding the effect of various parameters on the evolution of the problem solution. The packages generate a complete solution trace detailing every aspect of the problem resolution using genetic algorithms. This includes various population generations -- input population, generation-to-generation populations, and mating populations; roulette wheel based parent selection, crossover techniques, and mutation techniques; cost function and fitness function computations. These tools can be excellent for instruction, I have used them in one of my graduate course on "Graph & Genetic Algorithms: for Physical VLSI Design". For interested readers, the tool-package will be available on a CD at the conference and the others may download the executable files from the author's website to run on any personal computer. Documentation files are included with the executables in pdf-format. URL setup will be complete by the conference time.
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摘要 :
This paper presets a set of tools that can be used illustrate various forms of genetic algorithms. This includes both forms of genetic algorithms "Binary Genetic Algorithms" and "Continuous Parameter Genetic Algorithms". Genetic a...
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This paper presets a set of tools that can be used illustrate various forms of genetic algorithms. This includes both forms of genetic algorithms "Binary Genetic Algorithms" and "Continuous Parameter Genetic Algorithms". Genetic algorithms provide heuristic solutions for combinatorial-optimization problems that have found applications in many areas with outstanding success. In this package there are four optimization tools. The first one is to perform exhaustive search method; the second to implement approximation method; the third to implement binary genetic algorithm; and the fourth to implement continuous parameter genetic algorithm. Only the third and the fourth ones are discussed in this paper that are related to genetic algorithms. All the four software packages are developed using Borland C++ Builder 5.0. The tools are user-friendly with graphical user interface (GUI). Most of the genetic algorithmic parameters are changeable, making them useful in understanding the effect of various parameters on the evolution of the problem solution. The packages generate a complete solution trace detailing every aspect of the problem resolution using genetic algorithms. This includes various population generations -- input population, generation-to-generation populations, and mating populations; roulette wheel based parent selection, crossover techniques, and mutation techniques; cost function and fitness function computations. These tools can be excellent for instruction, I have used them in one of my graduate course on "Graph & Genetic Algorithms: for Physical VLSI Design". For interested readers, the tool-package will be available on a CD at the conference and the others may download the executable files from the author's website to run on any personal computer. Documentation files are included with the executables in pdf-format. URL setup will be complete by the conference time.
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