Introducing Local Optimization for Effective Initialization and Crossover of Genetic Decision Trees
Arindam Basak, Sudeshna Sarkar · 2001
We introduce a new genetic operator, Reduction, that rectifies decision trees not correct syntactically and at the same time removes the redundant sec-tions within, while preserving its accuracy during operation. A novel approach to crossover is pre-sented that uses the reduction operator to system-atically 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 reproduc-tion is performed in a highly correlated landscape with a global structure. Lexical convergence is also ensured implying identical individuals always pro-duce the same offspring. 1