When solving constrained optimization problems by evolutionary algorithms, the core issue is to balance constraints and objective function. This paper is the first attempt to utilize the correlation between constraints and objective function to keep this balance. First of all, the correlation between constraints and objective function is mined and represented by a correlation index. Afterward, a weighted sum updating approach and an archiving and replacement mechanism are proposed to make use of this correlation index to guide the evolution. By the above process, a novel constrained optimization evolutionary algorithm is presented. Experiments on a broad range of benchmark test functions indicate that the proposed method shows better or at least competitive performance against other state-of-the-art methods. Moreover, the proposed method is applied to the gait optimization of humanoid robots.

中文翻译：

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利用约束和目标函数之间的相关性进行约束进化优化

用进化算法求解约束优化问题时，核心问题是平衡约束和目标函数。本文首次尝试利用约束和目标函数之间的相关性来保持这种平衡。首先，挖掘约束和目标函数之间的相关性，并用相关指数表示。然后，提出了一种加权和更新方法以及一种归档和替换机制，以利用该相关性指标来指导演化。通过上述过程，提出了一种新的约束优化进化算法。在广泛的基准测试函数上的实验表明，与其他最先进的方法相比，所提出的方法显示出更好或至少具有竞争力的性能。而且，