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GA is a family of computational methods based upon the evolutionary theory of Darwinian/Russel Wallace, used to solve general problems through optimization. Caution is in order at this point! The use of biology-inspired terms in the GA system demonstrates genetics, which engineers considered several years ago as an optimization process. Since then, substantial progress in biology has shown that true genetic growth is many compounders but only a convenient metaphor remains the term “genetics” used in this book. Instead of looking from one design point to another in search of improved design, the GA shifts to a second population with a reduced meaning for the restricted purpose feature, from an existing set of design points called group. A replication and mutation process on the computer model of the plant design points achieves progress from generation to generation.

Although, the design team would like to see data from prior designs or preliminary studies in the application for engineering design from the original collections of design points. The question is not distinct from those used in the application of search methods. The objective design function and constraints at each design point of the population must be estimated. The experiments are independent such that the parallel treatment can be included. We now turn to the definition stage of data representation.