The paper describes an approach based on estimation of distribution algorithms.
提出了一种基于混合因子分析的分布估计算法。
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。
Estimation of distribution algorithms in continuous domains is based on such assumption that the variables subject to Gauss distribution.
连续域分布估计算法普遍采用高斯概率模型，假设变量服从高斯分布。
The traditional Estimation of Distribution Algorithms (EDA) is limited by its inferior convergence reliability under limited calculation time.
该算法较好地克服了传统分布估计算法（EDA）在计算时间受限制时收敛可靠性不高的弊病不足。
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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