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Genetic algorithms applied to nonlinear and complex domains.

机译:遗传算法应用于非线性和复杂域。

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The dissertation, titled “Genetic Algorithms Applied to Nonlinear and Complex Domains”, describes and then applies a new class of powerful search algorithms (GAs) to certain domains. GAs are capable of solving complex and nonlinear problems where many parameters interact to produce a ‘final’ result such as the optimization of the laser pulse in the interaction of an atom with an intense laser field. GAs can very efficiently locate the global maximum by searching parameter space in problems which are unsuitable for a search using traditional methods. In particular, the dissertation contains new scientific findings in two areas.; First, the dissertation examines the interaction of an ultra-intense short laser pulse with atoms. GAs are used to find the optimal frequency for stabilizing atoms in the ionization process. This leads to a new theoretical formulation, to explain what is happening during the ionization process and how the electron is responding to finite (real-life) laser pulse shapes. It is shown that the dynamics of the process can be very sensitive to the ramp of the pulse at high frequencies. The new theory which is formulated, also uses a novel concept (known as the (t,t) method) to numerically solve the time-dependent Schrödinger equation. Second, the dissertation also examines the use of GAs in modeling decision making problems. It compares GAs with traditional techniques to solve a class of problems known as Markov Decision Processes. The conclusion of the dissertation should give a clear idea of where GAs are applicable, especially in the physical sciences, in problems which are nonlinear and complex, i.e. difficult to analyze by other means.
机译:本文的标题为“应用于非线性和复杂域的遗传算法”,描述了然后将一类新的强大搜索算法(GA)应用于某些域。遗传算法能够解决许多参数相互作用以产生“最终”结果的复杂和非线性问题,例如在原子与强激光场相互作用中优化激光脉冲。遗传算法可以通过在不适合使用传统方法进行搜索的问题中搜索参数空间来非常有效地定位全局最大值。特别是,论文在两个方面包含了新的科学发现。首先,本文研究了超强短激光脉冲与原子的相互作用。 GA用于在电离过程中找到稳定原子的最佳频率。这导致了一种新的理论公式,用以解释电离过程中发生的事情以及电子如何响应有限(真实)的激光脉冲形状。结果表明,该过程的动力学对高频下的脉冲斜率非常敏感。提出的新理论还使用了一种新颖的概念(称为(t,t ')方法)来数值求解时间相关的Schrödinger方程。其次,本文还研究了遗传算法在决策问题建模中的应用。它将遗传算法与传统技术进行比较,以解决称为“马尔可夫决策过程”的一类问题。论文的结论应该给出一个清晰的概念,即遗传算法适用于哪些领域,特别是在物理科学中,适用于非线性和复杂的问题,即难以通过其他方法进行分析。

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