Applications of Flower Pollination Algorithm in Feature Selection and Knapsack Problems

This chapter presents one of the recently proposed bio-inspired optimization methods, namely, flower pollination algorithm (FPA). FPA for its capability to adaptively search a large search space with maybe many local optima has been employed to solve many real problems. FPA is used to handle the feature selection problem in wrapper-based approach where it is used to search the space of feature for an optimal feature set maximizing a given criteria. The used feature selection methodology was applied in classification and regression data sets and was found to be successful. Moreover, FPA was applied to handle the knapsack problem where different data sets with different dimensions were adopted to assess FPA performance. On all the mentioned problems FPA was benchmarked against bat algorithm (BA), genetic algorithm (GA), particle swarm optimization (PSO) and is found to be very competitive.

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