Zinn Peter
Ruhr-Universität Bochum, Lehrstuhl für Analytische Chemie, 44780 Bochum, Germany.
J Chem Inf Model. 2005 Jul-Aug;45(4):880-7. doi: 10.1021/ci049763m.
The applicability of genetic algorithms for solving multicomponent analyses is systematically examined. As a genetic algorithm (GA), the basic proposal of Goldberg is implemented in a straightforward manner to simulate multicomponent analyses in analogy to the well-established UV-vis or IR methods, especially multicomponent regression. The main focus of the study is to investigate the behavior of the genetic algorithm in order to compare it with the well-known behavior of multicomponent regression. A remarkable difference between the two methods is that the genetic algorithm method does not need any calibration procedure because of its pure searching characteristic. As important features of multicomponent systems, the degree of signal overlap (selectivity), the behavior of systems with known and unknown component numbers and qualities, and linear as well as nonlinear relationships between the analytical signal and concentration are varied within the simulations. According to multicomponent regression, recovering concentrations by a genetic algorithm is of limited applicability with the exception of systems at a low degree of signal overlap. On the other hand, the recovery of a probe spectrum in the analytical process always gives satisfactory results independent of the features of the probe system. The genetic algorithm obviously shows autoadaptive behavior in probe spectrum recovery. The quality and quantity of the resulting components may dramatically differ from the given probe, although the resulting spectrum is nearly the same. In such cases, the resulting component mixture can be interpreted as an imitation of the probe. As well probe spectra, theoretically designed spectra can also be autoadapted by genetic algorithms. The only limitation is that the desired spectrum must, of course, be incorporated into the search space defined by the involved components. Furthermore, a spectral signal is only one single property of a chemical compound or mixture. Because of the nonlinear search characteristic of genetic algorithms, any other chemical or physical property can also be treated as a desired property. Therefore, the conclusion of the study is well-founded that an old challenge of applied chemistry, namely, the development of new chemical products with desired properties, seems to be reachable under the control of genetic algorithms.
系统地研究了遗传算法在解决多组分分析问题中的适用性。作为一种遗传算法（GA），戈德堡的基本提议以一种直接的方式得以实现，以便类似于成熟的紫外 - 可见或红外方法（尤其是多组分回归）来模拟多组分分析。该研究的主要重点是研究遗传算法的行为，以便将其与多组分回归的已知行为进行比较。这两种方法之间的一个显著差异在于，遗传算法方法因其纯粹的搜索特性而不需要任何校准程序。作为多组分系统的重要特征，信号重叠程度（选择性）、具有已知和未知组分数量及性质的系统的行为，以及分析信号与浓度之间的线性和非线性关系在模拟中有所变化。根据多组分回归，除了信号重叠程度较低的系统外，用遗传算法恢复浓度的适用性有限。另一方面，在分析过程中恢复探针光谱总是能得到令人满意的结果，而与探针系统的特征无关。遗传算法在探针光谱恢复中明显表现出自适应行为。尽管得到的光谱几乎相同，但所得组分的质量和数量可能与给定的探针有很大差异。在这种情况下，所得的组分混合物可以被解释为对探针的一种模拟。除了探针光谱外，理论设计的光谱也可以通过遗传算法进行自适应调整。唯一的限制是，当然，所需光谱必须包含在所涉及组分定义的搜索空间内。此外，光谱信号只是化合物或混合物的一个单一属性。由于遗传算法的非线性搜索特性，任何其他化学或物理属性也都可以被视为所需属性。因此，该研究的结论是有充分依据的，即在遗传算法的控制下，应用化学的一个古老挑战，即开发具有所需性质的新化学产品，似乎是可以实现的。
