Genetic algorithms, efficiency enhancement, and deciding well with differing fitness variances

This study investigates the decision making between fitness function with differing vari-ance and computational-cost values. The ob-jective of this decision making is to provide evaluation relaxation and thus enhance the efficiency of the genetic search. A decision-making strategy has been developed to maxi-mize speed-up using facetwise models for the convergence time and population sizing. Re-sults indicate that using this decision making, significant speed-up can be obtained.