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遗传算法是在生物进化的启示下得到的一种搜索和自适应算法。此程序包为GA算法的源代码。-Genetic algorithms are inspired by biological evolution to be a search and adaptive algorithm. This package for the GA algorithm source code.
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微分进化策略的C++源代码。该算法是寻找一个集合中最优个体的一种遗传算法。-Differential Evolution Strategy C++ source code. The algorithm is to find an optimal set of individuals of a genetic algorithm.
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这是并行遗传算法的实现与“环”狭隘的拓扑结构。算法在进化提供了一个动态的遗传算子的选择。该库支持的26个遗传算子。这是跨平台的遗传算法用C+ +编写的。-This is implementation of parallel genetic algorithm with "ring" insular topology. Algorithm provides a dynamic choice of genetic operators in the evolution of. The library
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优化设计算法:用微分进化法求目标函数的最优解和最优值,已经验证算法的正确性,visual C++6.0开发-Optimization Algorithm: Differential Evolution Method with the objective function of the optimal solution and optimal value, has verified the correctness of the algorithm, visual C++6.0 developmen
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优化设计算法:用微分进化法求目标函数的最优解和最优值,有约束条件,已经验证算法的正确性,visual C++6.0开发-Optimization Algorithm: Differential Evolution Method with the objective function of the optimal solution and optimal value, there are constraints, has verified the correctness of the algori
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提供最优参数集,用它更容易进行算法的微分进化。-his contribution provides functions for finding an optimum parameter set using the evolutionary algorithm of Differential Evolution.
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差分进化算法的一部分,希望对人有用,这个算法在处理数值反方面比较实用-Differential evolution part of the algorithm, and I hope to people useful, this algorithm in dealing with values of the more practical
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差分演化(DE)算法的源程序,包含有一些测试函数。-original procedure of differential evolution algorithm,including some test functions.
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针对传统的算法如遗传算法、粒子群算法等在TSP问题上求解精确性和求解规模上都还有一定的不足,本文提出了一种基于动态规划思想的粒子群优化算法。该算法用动态规划的方法实现粒子间的信息交互和粒子的进化,并且将粒子群中的粒子按无标度信息指导网络拓扑图的方式进行连接。仿真结果表明该方法能有效地减小误差率,提高解的精确,同时还保持了较低的计算复杂度,具有良好的稳健性。-TSP problem solving for the traditional algorithms such as genetic alg
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hansen函数de算法 差分进化算法-hansen function de algorithm differential evolution algorithm
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在目前常用的激光粒度仪反演算方法的优点和缺点基础上,为了实现各种方法的稳定性
与准确性的统一,提出一种基于退火演化算法和遗传算法组合的随机反演方法,该方法具有更
强的全局收敛性,也具有对粒径分布形状不敏感、抗噪声能力强等优点。该算法能对具有单峰
及双峰分布形式的颗粒尺寸进行精确求解,并获得合理的颗粒粒径分布结果。-, In order to achieve the unity of the stability and accuracy of the various method
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To finding an optimum parameter set using the evolutionary algorithm of Differential Evolution-This contribution provides functions for finding an optimum parameter set using the evolutionary algorithm of Differential Evolution. Simply speaking: If y
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Adaptive Composite Differential Evolution Algorithm 自适应复合差分进化算法-Differential Evolution with Composite Trial Vector
Generation Strategies and Control Parameters
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给出了一种新的差分进化算法求解最优化问题实验结果显示算法有效-Gives a new differential evolution algorithm results display algorithm for solving optimization problems effectively
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实现了无约束条件下的微分进化算法,对于微分几何有重要意义,参考价值极高。-Differential evolution algorithm
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实现差分进化算法,并通过了rastrigr函数的测试,在全局上搜索得到了正确的结果。-Achieve differential evolution algorithm, and passed the test rastrigr function in the global search to get the correct results.
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In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimizatio
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用C编写一个差分进化算法。欢迎各位大神指正。-Differential Evolution Algorithm
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差分进化算法,有测试函数,是遗传算法的改进算法,收敛快,精度高。-Differential evolution algorithm, a test function, is an improved genetic algorithm algorithm, fast convergence, high precision.
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基于Python编写的差分进化原理的带边界条件约束算法(A band constraint algorithm based on differential evolution algorithm)
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