Estimation of distribution algorithms (EDAs) is a new meta-heuristic algorithm.
提出了一种基于混合因子分析的分布估计算法。
Therefore, the core of EDAs is to estimate the probability distribution of the solution space.
因此，分布估计算法的核心在于估计解空间的概率分布。
In contrast to traditional estimate distribution algorithm, parallel EDAs has greatly improved the efficiency when optimizing continuous functions and real time questions.
相对于传统的概率分布估计算法，并行的概率分布估计算法在解决连续函数优化及实时优化问题时能提供极大程度的效率提高。
Estimation of Distribution algorithms (EDAs) are new evolutionary algorithms based on probabilistic model and have become a new focus in the field of evolutionary computation.
分布估计算法由于其较强的理论基础已成为进化计算研究的新热点。
Estimation of Distribution algorithms (EDAs) are new evolutionary algorithms based on probabilistic model and have become a new focus in the field of evolutionary computation.
分布估计算法由于其较强的理论基础已成为进化计算研究的新热点。
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