On the pathological behavior of adaptive differential evolution on hybrid objective functions
On the pathological behavior of adaptive differential evolution on hybrid objective functions
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DOI：
10.1145/2576768.2598322
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Ryoji Tanabe;A. Fukunaga
中科院分区：
文献类型：
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作者：
Ryoji Tanabe;A. Fukunaga
大多数最先进的差分进化（DE）算法是在线参数自适应的自适应DE。我们研究了自适应DE在一类混合函数上的行为，其中独立的变量组与不同的分量目标函数相关联。3个国家的最先进的自适应DE（JADE，SHADE，jDE）的实验评估表明，混合功能是“自适应DE硬”。也就是说，自适应DE在这些新功能上具有显著的失败率。深入分析的自适应行为的DE显示，他们的参数自适应机制表现在这类问题的病态方式，导致过度适应的混合动力车的组件之一和整体性能差。因此，这类欺骗性基准对DE构成了重大挑战。
Most state-of-the-art Differential Evolution (DE) algorithms are adaptive DEs with online parameter adaptation. We investigate the behavior of adaptive DE on a class of hybrid functions, where independent groups of variables are associated with different component objective functions. An experimental evaluation of 3 state-of-the-art adaptive DEs (JADE, SHADE, jDE) shows that hybrid functions are "adaptive-DE-hard". That is, adaptive DEs have significant failure rates on these new functions. In-depth analysis of the adaptive behavior of the DEs reveals that their parameter adaptation mechanisms behave in a pathological manner on this class of problems, resulting in over-adaptation for one of the components of the hybrids and poor overall performance. Thus, this class of deceptive benchmarks pose a significant challenge for DE.