Introducing Local Optimization for Effective Initialization and Crossover of Genetic Decision Trees

May 2001

Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2001)

Volume

Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2001)

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We introduce a new genetic operator, Reduction, that rectifies decision trees not correct syntactically and at the same time removes the redundant sections within, while preserving its accuracy during operation. A novel approach to crossover is presented that uses the reduction operator to systematically extract building blocks spread out over the entire second parent to create a subtree that is valid and particularly useful in the context it replaces the subtree in the first parent. The crossover introduced also removes unexplored code from the offspring and hence prevents redundancy and bloating. Overall, reduction can be viewed as a local optimization step that directs the population, generated initially or over generations through crossovers, to potentially good regions in the search space so that reproduction is performed in a highly correlated landscape with a global structure. Lexical convergence is also ensured implying identical individuals always produce the same offspring.