Hybrid-coded crossover for binary-coded genetic algorithms in constrained optimization

The success of genetic searches greatly depends on appropriate selections for many search parameters. This situation becomes more critical for optimization problems involving sharp constraint surfaces. When penalty function appproaches are used to handle the constraints, the genetic algorithm conducts the search on a design space which has continually changing contours during the evolution. This often leads to incorrect gene extinctions at early stages of the search, and thus prevents the search from attaining the final global solution. The paper aims to devise a hybrid-coded crossover strategy for binary-coded genetic algorithms (BGA) which combines the major content of two-point crossover and the full domain representation characteristics embedded in many crossover schemes of real-coded genetic algorithms. It is found that the unique hybrid-coded crossover scheme can largely prevent the binary-coded genetic searches from falsely converging to sub-optimal solutions. Comparisons between BGA with the hybrid-coded crossover and the traditional two-point crossover are conducted in a number of illustrative problems with varied combinations of constraints.