ABSTRACT Although Genetic Algorithms have found many successful applications in the field of exploration geophysics, the convergence speed remains a big challenge as Genetic Algorithms usually require a huge amount of fitness function evaluations. In this paper, we propose an efficiency‐improved Genetic Algorithm, which has both a good global search capability and a good local search capability, and is also capable of robustly handling the premature convergence challenge commonly seen in linear and directed non‐linear optimization methods. In our new genetic algorithm, the global search capability is performed via a modified island model, while the local search capability is provided by a novel self‐adaptive differential evolution fine tuning scheme. Premature convergence is dealt with via a local exhaustive search method. We first demonstrate the much improved convergence speed of this efficiency‐improved Genetic Algorithm over that of our previously proposed advanced Genetic Algorithm on several multimodal functions. We further demonstrate the effectiveness of our efficiency‐improved Genetic Algorithm by applying it to a two‐dimensional common reflection surface stacking problem, which is a highly nonlinear geophysical optimization problem, to obtain very encouraging results.

中文翻译：

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一种改进效率的遗传算法及其在多模态函数和二维共反射面叠加问题上的应用

摘要 虽然遗传算法在勘探地球物理领域有很多成功的应用，但由于遗传算法通常需要大量的适应度函数评估，因此收敛速度仍然是一个很大的挑战。在本文中，我们提出了一种提高效率的遗传算法，它具有良好的全局搜索能力和良好的局部搜索能力，并且能够稳健地处理线性和有向非线性优化方法中常见的早熟收敛挑战。 . 在我们的新遗传算法中，全局搜索能力是通过修改后的岛屿模型来执行的，而局部搜索能力是由一种新颖的自适应差分进化微调方案提供的。通过局部穷举搜索方法处理过早收敛。我们首先证明了这种效率改进的遗传算法的收敛速度比我们之前提出的几种多模态函数的高级遗传算法的收敛速度有很大提高。我们通过将其应用于二维公共反射表面叠加问题（这是一个高度非线性的地球物理优化问题），进一步证明了我们的效率改进遗传算法的有效性，并获得了非常令人鼓舞的结果。