Learning search procedure

A solution space, a, consisting of hyperrectangles defined in the decision space, X, is a basic characteristic common to all the learning methodologies that will be described in subsequent sections. The same does not happen with the specific performance criteria tfi, mapping models /, and search procedures 5, which obviously depend on the particular nature of the systems under analysis, and the type of the corresponding performance metric, y. 

The search procedure, S, used to uncover promising hyperrectangles in the decision space, X, associated with a desired y value (e.g., y = good ), is based on symbolic inductive learning algorithms, and leads to the identification of a final number of promising solutions, X, such as the ones in Fig. 2b. It is described in the following subsection. 

In the previous paragraphs we defined the solution format f, performance criterion i/r, mapping procedure /, and performance metric y that characterize our learning methodology for systems with a quantitative metric y. Here we will assemble all these pieces together and briefly discuss the search procedure, S (further details can be found in Saraiva... 

To support the application of the learning methodology, fix) was used to generate 500 (x, 2, w) records of simulated operational data, transformed by Eq. (26) into an equivalent number of (x, y) pairs. Finally, the following constraints were imposed to the search procedure, 5 ... 

First, we discuss the problem statements and key features of the learning architecture that are specific to complex systems. This is followed by a brief presentation of the search procedures that are used to build a final solution. The section ends with a summary of the application of the learning architecture to the analysis of a Kraft pulp mill. 

It is apparent that the use of enzymatic catalysis continues to grow Greater availabiUty of enzymes, development of new methodologies for thek utilization, investigation of enzymatic behavior in nonconventional environments, and the design and synthesis of new biocatalysts with altered selectivity and increased stabiUty are essential for the successhil development of this field. As more is learned about selectivity of enzymes toward unnatural substrates, the choice of an enzyme for a particular transformation will become easier to predict. It should simplify a search for an appropriate catalyst and help to estabhsh biocatalytic procedures as a usehil supplement to classical organic synthesis. 

Although much still needs to be learned, biotechnological advancements complement traditional procedures in the search for improved quality and regional character. 

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