Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA
Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA
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DOI：
10.1007/3-540-45356-3_75
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发表时间：
2000-09
期刊：
影响因子：
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通讯作者：
P. Bosman;D. Thierens
中科院分区：
文献类型：
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作者：
P. Bosman;D. Thierens
在过去的几年里，统计数据在基于迭代密度估计的随机优化中的直接应用变得更加重要，并且出现在进化计算中。对选定样本的密度估计以及对结果分布的采样是进化算法中使用的重组和变异步骤的组合。我们引入了名为 IDA 的框架来形式化这个概念。通过将连续概率论与现有算法的技术相结合，该框架使我们能够定义新的连续进化优化算法。
The direct application of statistics to stochastic optimization based on iterated density estimation has become more important and present in evolutionary computation over the last few years. The estimation of densities over selected samples and the sampling from the resulting distributions, is a combination of the recombination and mutation steps used in evolutionary algorithms. We introduce the framework named IDA to formalize this notion. By combining continuous probability theory with techniques from existing algorithms, this framework allows us to define new continuous evolutionary optimization algorithms.