An Intelligent Genetic Algorithm for Mining Classification Rules in Large Datasets

Genetic algorithm is a popular classification algorithm which creates a random population of candidate solutions and makes them to evolve into a suitable accurate solution for a given problem by processing them iteratively for several generations. During each generation the training data set is accessed by the genetic algorithm only for the population member's fitness calculation and no other extra knowledge about the problem domain is extracted from the training data set. Even the domain knowledge stored in the chromosome code of the population may be lost in the future generations due to genetic operations. All the genetic operations like crossover and mutation are probability based and they do not depend upon the domain knowledge. This phenomenon makes the genetic algorithm to converge slowly. This paper proposes a genetic algorithm which tries to gain maximum knowledge in between the generations and store them in the form of knowledge chromosomes. The gained knowledge is used to make predictions about the search space and to guide the search process to an area with potential solutions in the subsequent generations. This makes the genetic algorithm to converge quickly which in turn reduces the learning cost. The experiments show that the run time is reduced considerably when compared with the state-of-the-art evolutionary algorithm

Smart Crossover Operator with Multiple Parents for a Pittsburgh Learning Classifier System

This paper proposes a new smart crossover operator for a Pittsburgh Learning Classifier System. This operator, unlike other recent LCS approaches of smart recombination, does not learn the structure of the domain, but it merges the rules of N parents (N ≥ 2) to generate a new offspring. This merge process uses an heuristic that selects the minimum subset of candidate rules that obtains maximum training accuracy. Moreover the operator also includes a rule pruning scheme to avoid the inclusion of over-specific rules, and to guarantee as much as possible the robust behaviour of the LCS. This operator takes advantage from the fact that each individual in a Pittsburgh LCS is a complete solution, and the system has a global view of the solution space that the proposed rule selection algorithm exploits. We have empirically evaluated this operator using a recent LCS called GAssist. First with the standard LCS benchmark, the 11 bits multiplexer, and later using 25 standard real datasets. The results of the experiments over these datasets indicate that the new operator manages to increase the accuracy of the system over the classical crossover in 16 of the 25 datasets, and never having a significantly worse performance than the classic operator