Description: 一个类似于blast算法的基因数据快速搜索算法-an algorithm similar to the blast of genetic data Fast Search Algorithm Platform: |
Size: 91136 |
Author:董骝焕 |
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Description: 单纯形法是一种不错的随机搜索算法，但是其受初始值的选取，步长大小的影响较大，容易陷入局部收敛。程序中列出了基本单纯形法，和几种改进的单纯形法，包括变步长的单纯形法，单纯形加遗传算法等。-simplex method is a good random search algorithm, but its initial value by the selection, step in a larger size, easy to be trapped into local convergence. Procedures set out the basic simplex method, and several improved simplex method, including variable step of the simplex method. Simplex increase genetic algorithm. Platform: |
Size: 59392 |
Author:xcq |
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Description: TSP问题是组合优化中的经典问题。其解决方法有局部优化方法和一些启发式算法,局部搜索方法充分考虑问题
的邻域结构,遗传算法有很好的全局搜索能力,memetic算法把遗传算法和局部优化算法相结合,试验结果证明,能很好地解
决TSP问题。-TSP problem is a classic combinatorial optimization problem. Its solution has a number of local optimization methods and heuristic algorithms, local search methods take full account of the issue of neighborhood structure, genetic algorithm has good ability of global search, memetic algorithm for the genetic algorithm and local optimization algorithm combining test results proved that well positioned to solve the TSP problem. Platform: |
Size: 102400 |
Author:文龙 |
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Description: 遗传算法，是模拟达尔文的遗传选择和自然淘汰的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法.遗传算法是一类可用于复杂系统优化的具有鲁棒性的搜索算法-Genetic algorithm, is a simulation of Darwinian natural selection to genetic selection and biological evolution of the computing model is a natural evolutionary process by simulating the optimal solution search methods. Is a kind of genetic algorithm can be used for optimization of complex systems is robust The search algorithm Platform: |
Size: 1024 |
Author:曹睿 |
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Description: < MATLAB遗传算法工具箱及应用>>介绍了如何在MATLAB中完成遗传算法的应用。遗传算法[Genetic Arithmatic，简称GA]是以自然选择和遗传理论为基础，将生物进化过程中适者生存规则与群体内部染色体的随机信息交换机制相结合的高效全局寻优搜索算法。GA摒弃传统的搜索方式，模拟自然界生物进化过程，采用人工进化的方式对目标空间进行随机优化搜索。MATLAB是MATHWORKS公司的一套高性能的数值计算和可视化软件。MATLAB遗传算法工具箱及应用
-Genetic Algorithm [Genetic Arithmatic, referred to as GA] is based on natural selection and genetic theory, the process of biological evolution survival of the fittest rules and groups of chromosomes within the clearing-house mechanism of the random combination of efficient global optimization search algorithm. GA to abandon the traditional search methods to simulate the process of natural biological evolution, artificial evolution approach on the target stochastic optimization search space. Mathworks Inc. MATLAB is a high-performance numerical computation and visualization software. MATLAB genetic algorithm toolbox and its application Platform: |
Size: 6146048 |
Author:吴晓晖 |
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Description: Simulated Annealing SA
Hill Climbing HC
Local Beam Search LBS
Genetic Algorithm GA
-Simulated Annealing SA
Hill Climbing HC
Local Beam Search LBS
Genetic Algorithm GA
Platform: |
Size: 3072 |
Author:mashomid |
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Description: Best First Search Algorithm
to Puzzle game- Best First Search Algorithm
to Puzzle game
Platform: |
Size: 88064 |
Author:dewi_irianti |
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Description: SVM方法的基本思想是：定义最优线性超平面，并把寻找最优线性超平面的算法归结为求解一个凸规划问题。进而基于Mercer核展开定理，通过非线性映射φ，把样本空间映射到一个高维乃至于无穷维的特征空间（Hilbert空间），使在特征空间中可以应用线性学习机的方法解决样本空间中的高度非线性分类和回归等问题。svm 程序，即支持向量机的代码。-The basic idea of SVM method are: the definition of the optimal linear hyperplane, and the search algorithm for optimal linear hyperplane by solving a convex programming problem. Then based on Mercer nuclear expansion theorem, through a nonlinear mapping φ, the sample space is mapped to a high-dimensional and even infinite dimensional feature space (Hilbert space), so that in the feature space can be applied to solve the linear learning machine method, the sample space The highly nonlinear classification and regression problems. svm procedures that support vector machine code. Platform: |
Size: 117760 |
Author:秀 |
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Description: 现代最优化算法（有170多页的PPT，2010年的）
分为三个部分
Part 1 概论
Part 2 模拟退火算法
Part 3 遗传算法
现在常用的优化算法
禁忌搜索算法
模拟退火算法
遗传算法
人工神经网络
蚁群算法
粒子群算法
混合算法-Modern optimization algorithm is divided into three parts Part 1 Part 2 Introduction Part 3 simulated annealing genetic algorithm optimization is now commonly used in tabu search algorithm simulated annealing genetic algorithm, artificial neural network hybrid particle swarm ant colony optimization algorithm Platform: |
Size: 4639744 |
Author:zhang |
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Description: a design methodology is introduced that
blends the classical PID and the fuzzy controllers in an
intelligent way and thus a new intelligent hybrid
controller has been achieved. Basically, in this design
methodology, the classical PID and fuzzy controller
have been combined by a blending mechanism that
depends on a certain function of actuating error.
Moreover, an intelligent switching scheme is induced
on the blending mechanism that makes a decision upon
the priority of the two controller parts namely, the
classical PID and the fuzzy constituents. The
simulations done on various processes using the new
hybrid fuzzy PID controller provides ‘better’ system
responses in terms of transient and steady-state
performances when compared to the pure classical PID
or the pure fuzzy controller applications. The controller
parameters are all tuned by the aid of genetic search
algorithm.-a design methodology is introduced that
blends the classical PID and the fuzzy controllers in an
intelligent way and thus a new intelligent hybrid
controller has been achieved. Basically, in this design
methodology, the classical PID and fuzzy controller
have been combined by a blending mechanism that
depends on a certain function of actuating error.
Moreover, an intelligent switching scheme is induced
on the blending mechanism that makes a decision upon
the priority of the two controller parts namely, the
classical PID and the fuzzy constituents. The
simulations done on various processes using the new
hybrid fuzzy PID controller provides ‘better’ system
responses in terms of transient and steady-state
performances when compared to the pure classical PID
or the pure fuzzy controller applications. The controller
parameters are all tuned by the aid of genetic search
algorithm. Platform: |
Size: 258048 |
Author:mohaideen |
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Description: 提出一种基于改进粒子群动态搜索算法的网络重构方法，算法把初始粒子群按照适应度的大小分为两个互不交叉，且具有不同分工的子群，并进行动态搜索。通过引入了交叉和禁忌思想，减少了解陷入局部最优的可能性．与遗传、禁忌搜索算法重构的结果进行比较，表明本文算法具有更高的搜索效率，更容易找到全局最优解．-：An improved method based on improved particle swarm optimization dynamic search algodthm networkrcconfiguration
is presented in this paper．The initial particle swarm is divided into two different subgroups according to the size of fitness，with a division of labour between the different subgroups of dynamic search．Cross
thinking and ideological tabu ale ledinto this algorithm to reduce the possibility of local Optimum．Compared with genetic and tabu search algorithm,the results show that the proposed method possesses a hi．gher search efficiency andis easier tO find the global optimum solutions． Platform: |
Size: 419840 |
Author:yirufang |
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Description: 摘要：新型元启发式算法例如粒子群算法，萤火虫算法，和声搜索算法已经成为现今复杂的优化问题的有效解决方法。该文基于蝙
蝠的回声定位行为提出了一种新型的元启发式算法———蝙蝠算法，同时也将现有的一些算法的优点引入到该算法中。 改文对该算
法进行了详细的公式化表述并对其执行流程的作出了说明，并且将该算法与遗传算法、粒子群优化算法等算法进行了比较。仿真结
果表明,蝙蝠算法明显优于其他算法，并对进一步的研究作出了展望。-Summary: The new meta-heuristic algorithms such as particle swarm optimization, firefly algorithm, harmony search algorithm has become an effective solution to today' s complex optimization problems. In this paper, based on bat echolocation behavior presents a new meta-heuristic algorithm--- bat, but also some of the advantages of the existing algorithms is introduced into the algorithm. Change the text of the algorithm in detail and make a note formulaic expressions its implementation process, and the algorithm and genetic algorithms, particle swarm optimization algorithm are compared. Simulation results show that the algorithm is superior to other algorithms bat, and made the prospects for further research. Platform: |
Size: 629760 |
Author:薛云强 |
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Description: 遗传算法是计算数学中用于解决最佳化的搜索算法，是进化算法的一种。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等。遗传算法通常实现方式为一种计算机模拟。对于一个最优化问题，一定数量的候选解（称为个体）的抽象表示（称为染色体）的种群向更好的解进化。-Genetic algorithm is used to solve computational mathematics in optimizing search algorithm, an evolutionary algorithm. Evolutionary algorithm was originally borrowed evolutionary biology and developed a number of phenomena, these phenomena, including genetics, mutation, natural selection and hybridization. Genetic algorithms are usually implemented as a way of computer simulation. For an optimization problem, a number of candidate solutions (called individuals) abstract representation of the population (called chromosomes) to better solution evolution. Platform: |
Size: 2048 |
Author:james |
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Search - genetic search algorithm