  Premature convergence is one of the important issues while using Genetic
Programming for data modeling. It can be avoided by improving population
diversity. Intelligent genetic operators can help to improve the population
diversity. Crossover is an important operator in Genetic Programming. So, we
have analyzed number of intelligent crossover operators and proposed an
algorithm with the modification of soft brood crossover operator. It will help
to improve the population diversity and reduce the premature convergence. We
have performed experiments on three different symbolic regression problems.
Then we made the performance comparison of our proposed crossover (Modified
Soft Brood Crossover) with the existing soft brood crossover and subtree
crossover operators.
