Constraint handling in genetic search using expression strategies
P. Hajela, J. Yoo · AIAA Journal · 1996
A traditional penalty-function formulation for treatment of nonlinear constrained optimization problems in genetic search has been shown to be extremely sensitive to user-specified schedules of selecting penalty parameters. The sensitivity of such an approach is manifested in a biasing of the search toward suboptimal designs and a general increase in the number of function evaluations required to obtain a converged design. Alternative methods are described for handling constraints that are motivated by the fact that the structure of both feasible and infeasible designs is generally present in the population of designs at any generation of search. A preconditionin g of the infeasible designs prior to the genetic transformations, by an expression operation that is conceptually analogous to the theory of dominant and recessive genes in genetics, is shown to be highly effective in evolving feasible solutions, and with savings of computational resource. Two alternative implementations of this approach are described and a comparison is made of numerical efficiency vis-a-vis the penalty-function-based approach.