Real-coded GA for High-dimensional k-tablet Structures
Real-coded GA for High-dimensional k-tablet Structures
复制标题
高维 k-片结构的实数编码遗传算法
DOI：
10.1527/tjsai.19.28
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发表时间：
2004
影响因子：
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通讯作者：
S. Kobayashi
中科院分区：
文献类型：
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作者：
J. Sakuma;S. Kobayashi
提出了一种实数编码遗传算法(RCGA)，它可以处理高维的非定标结构，即k片结构。K-Tablet结构是指在k维子空间和正交(n-k)维子空间中适应度函数的尺度不同的景观。当高维k片结构被包含在适应度函数中时，传统的RCGAs的搜索速度会降低。在这种结构中，杂交产生的后代很可能比亲本种群覆盖的区域分布得更广。这一现象导致了搜索的停滞。为了解决这个问题，我们提出了一种新的交叉LundX-m，它只使用m维隐变量。通过包括k-Tablet结构在内的多个基准函数对该方法的有效性进行了测试，结果表明该方法比传统的交叉方法具有更好的性能，尤其是当维度n大于100时。
This paper presents the Real-coded Genetic Algorithms(RCGA) which can treat with high-dimensional ill-scaled structures, what is called, k-tablet structure. The k-tablet structure is the landscape that the scale of the fitness function is different between the k-dimensional subspace and the orthogonal (n-k)-dimensional subspace. The search speed of traditional RCGAs degrades when high-dimensional k-tablet structures are included in the landscape of fitness function. In this structure, offspring generated by crossovers is likely to spread wider region than the region where the parental population covers. This phenomenon causes the stagnation of the search. To resolve this problem, we propose a new crossover LUNDX-m, which uses only m-dimensional latent variables. The effectiveness of the proposal method is tested with several benchmark functions including k-tablet structures and we show that our proposal method performs better than traditional crossovers especially when the dimensionality n is higher than 100.