Evolutionary algorithms find applications but the theory algorithms is not well developed.

演化算法在工程领域取得了广泛的应用,但是其基础理论尚未完全建立.

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Ant Colony Optimization is a novel optimization algorithm of simulated evolutionary algorithms.

蚁群优化算法是一种新型模拟进化的优化算法.

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It proposed a unified fundamental structure of evolutionary algorithms and basic models.

在统一的框架下给出了演化算法的设计方法和基本结构.

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Soft computing includes Artificial Neural Network, Fuzzy Logic, Evolutionary Algorithms, Rough Set ( RS ) Theory, etc.

软计算工具主要包括人工神经网络 、 模糊集理论 、 进化算法和粗糙集理论等.

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A general approach based on evolutionary algorithms to inverse parameter identification problems of PDEs is introduced.

给出了一种利用演化计算方法求解微分方程中的参数识别类型反问题的方法.

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Ant colony algorithm is to optimize the emerging field of evolutionary algorithms a bionic.

蚁群算法是优化领域中新出现的一种仿生进化算法.

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