ABSTRACT
This chapter presents a short description of the global version of the Chicken Swarm Optimization (CSO) algorithm. Binary Chicken Swarm Optimization version of the algorithm is used for solving discrete optimization problems in which the search space is discrete and the solution can be represented using a binary vector. The application of chaos theory has as the main objective the improvement of the performance of the CSO algorithm. The objective of the chaotic maps is to solve the problem of generation of values for the random variables which are used when the positions of the chickens are updated and two illustrative maps are the tent map and the logistic map. The values for the configuration parameters of Principal Component Analysis and Correlation Filter were selected after a series of experiments such that the number of selected features would be approximately equal to the number of features returned by the Binary Chicken Swarm Optimization algorithm.
