Evolutionary strategies

In evolutionary strategies, a parent string produces X offspring the fittest of the 1 + X individuals is selected to be the single parent for the next generation of offspring. There is no crossover operator in evolutionary strategies, only mutation. 

A separate class of experimental evaluation methods uses biological mechanisms. An artificial neural net (ANN) copies the process in the brain, especially its layered structure and its network of synapses. On a very basic level such a network can learn rules, for example, the relations between activity and component ratio or process parameters. An evolutionary strategy has been proposed by Miro-datos et al. [97] (see also Chapter 10 for related work). They combined a genetic algorithm with a knowledge-based system and added descriptors such as the catalyst pore size, the atomic or crystal ionic radius and electronegativity. This strategy enabled a reduction of the number of materials necessary for a study. 

To compare the efficiency of the two evolutionary strategies WGS 1 2, the same initial parameter space has been considered, i.e., pool of elements, range of concentrations, testing operating conditions and objective function to evaluate the fitness of catalysts [22]. 

Black Box Discovery and Optimisation of New Catalysts Using an Evolutionary Strategy... 

Recently, the evolutionary strategy (ES) has been successfully applied for the iterative optimisation of catalysts in a series of reactions [7, 10, 15, 27-30]. For all the case studies reported, the optimisation process was found to converge rapidly after typically four to six generations. 

Selecting the right variables often improves the models and makes interpretation easier. When there are too many descriptors, and especially when these descriptors do not have a clear physico-chemical meaning (e.g., connectivity indices and other 2D descriptors), stochastic methods such as genetic algorithms and evolutionary strategies can be used for finding an optimal subset of descriptors [91,92]. 

Serra et al. [122] used an evolutionary strategy for the design of catalyst libraries to evaluate a synthesis route for styrene from toluene. 

Figure 3.83 Schematic of the evolutionary strategy. GA = generic algorithm KBS = knowledge-based system [122] (by courtesy of Elsevier Ltd.).

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