Self-crossover – a new genetic operator and its application to feature selection

Crossover is an important genetic operation that helps in random recombination of structured information to locate new points in the search space, in order to achieve a good solution to an optimization problem. The conventional crossover operation when applied on a pair of binary strings will usually not retain the total number of Is in the offsprings to be the same as that of their parents, but there are many optimization problems which require such a constraint. In this article, we propose a new crossover technique called ‘self-crossover’, which satisfies this constraint as well as retaining the stochastic and evolutionary characteristics of genetic algorithms. This new operator serves the combined role of crossover and mutation. We have proved that self-crossover can generate any permutation of a given string. As an illustration, the effectiveness of this new technique has been demonstrated for the feature selection problem of pattern recognition. Performance of self-crossover for feature selection is also compared with that of ordinary crossover.