CONSTRAINT HANDLING IN GENETIC SEARCH USING EXPRESSION STRATEGIES

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 penalt y parameters. The sensitivity of such an approach is manifested in a b iasing of the search toward suboptimal designs and a general increase in the number of function evaluations required to obtain a converged d esign, Alternative methods are described for handling constraints that are motivated by the fact that the structure of both feasible and inf easible designs is generally present in the population of designs at a ny generation of search, A preconditioning of the infeasible designs p rior to the genetic transformations, by an expression operation that i s conceptually analogous to the theory of dominant and recessive genes in genetics, is shown to be highly effective in evolving feasible sol utions, and with savings of computational resource, Two alternative im plementations of this approach are described and a comparison is made of numerical efficiency vis-h-vis the penalty-function-based approach.