Genetic algorithm problem formulation

Representation requires that the designer of a typical evolutionary computation algorithm (EA) formulates one inadaptable blueprint for the solution of some problem, then present the variables of that blueprint in a form that is amenable to manipulation by the genetic operators of the EA. Fitness evaluation, on the other hand, has limited GA in two distinct ways (1) it has limited environmental feedback to the confines of a formula or algorithm, which reflects accurately and exclusively the quality of the complete candidate solution from the perspective of the human designer. In addition, (2) fitness evaluation has proven to be the most computationally costly part of a typical EA. Note that elaborate developmental mappings actually increase that computational cost. However, our interest here lies in the limiting effects of representation. 

The third maj or class of search methods are genetic algorithms (GAs), which are widely used for docking purposes. GAs are stochastic optimization methods inspired by the concepts of evolution (172-174). The optimization problem is generally formulated in the Ian-... 

More recently, Lavan and Dargush (2009) examined a multi-objective seismic design optimization in which the maximum interstorey drift andmaximiun acceleration were considered as the primary control parameters. The multi-objective problem was formulated in Pareto optimal sense (Pareto 1927) and a genetic algorithm based approach was adopted to identify the Pareto front. The endresultofthis multi-objective optimization is a family of Pareto front solutions providing the decisionmakers with an opportunity to understand the tradeoff between the drift and acceleration. 

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