Genetic analyses optimization methods

The yield of the expected reaction product was used in an example as the feedback to a genetic algorithm (GA) driven method that proposes a new set of reaction conditions. After some cycles of synthesis and analysis the yield of this reaction was significantly improved by using better reaction conditions. In a second step, a set of different MCRs using a set of different conditions for each of them was carried out in parallel and optimized with a GA to find common optimal conditions [29]. 

Linear Models. Variable selection approaches can be applied in combination with both linear and nonlinear optimization algorithms. Exhaustive analysis of all possible combinations of descriptor subsets to find a specific subset of variables that affords the best correlation with the target property is practically impossible because of the combinatorial nature of this problem. Thus, stochastic sampling approaches such as genetic or evolutionary algorithms (GA or EA) or simulated annealing (SA) are employed. To illustrate one such application we shall consider the GA-PLS method, which was implemented as follows (136). 

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