A hybrid evolutionary approach is proposed for the combined problem of feature selection (using a genetic algorithm with Intersection/Union recombination and a fitness function based on a counter-propagation artificial neural network) and subsequent classifier construction (using strongly-typed genetic programming), for use in nonlinear association studies with relatively large potential feature sets and noisy class data. The method was tested using synthetic data with various degrees of injected noise, based on a proposed mental health database. Results show the algorithm has good potential for feature selection, classification and function characterization.
The pattern matching with wildcards and length constraints problem is an interesting problem in the literature whose computational complexity is still open. There are polynomial time exact algorithms for its special cases. There are heuristic algorithms, and online algorithms that do not guarantee an optimal solution to the original problem. We consider two special cases of the problem for which we develop offline solutions. We give an algorithm for one case with provably better worst case time complexity compared to existing algorithms. We present the first exact algorithm for the second case. This algorithm uses integer linear programming (ILP) and it takes polynomial time under certain conditions.
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