Thermal Physics and Thermal Analysis by Jaroslav Šesták Pavel Hubík & Jiří J. Mareš

Author:Jaroslav Šesták, Pavel Hubík & Jiří J. Mareš
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

12.5 Evolutionary Approach to Chemical Equilibrium Determination

A good optimization algorithm should lead a calculation to the global minimum, and it should be sufficiently stable, versatile, and as fast as possible. These conditions are met very well by stochastic evolutionary (genetic) algorithms [36–38]. Their basic idea follows, as their name suggests, from the analogy with evolutionary biology. With it, it also shares terminology, e.g., gene, chromosome, individual (phenotype), population , generation, natural selection, replication, crossover , and mutation . Contrary to evolutionary biology, it possesses some unique features, e.g., elitism ; that is, the fittest individual is always automatically transferred (grafted) to the next generation. There is also possibility to combine the evolutionary approach with some other method; for example, in the first step, an evolutionary algorithm suggests some region where the global minimum is very probably located, and then, this minimum is precisely identified by a gradient-based algorithm.

The quality of the individual, which is a set of regression model parameters (variables), is given by the value of the fitness function . Allowable values of variables lay between upper and lower bounds defined at the beginning of calculation. The bounds should reflect physical properties of variables, e.g., molar fractions go from zero to unity and absolute temperature is positive. Each variable is encoded into a binary string of ones and zeros, which represents the gene. It can be done by many ways, for example, as follows: Step 1

Transformation onto <0; 1> interval

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