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For the last few decades, optimization has been developing at a fast rate.Bio-inspired optimization algorithms are metaheuristics inspired by nature.These algorithms have been applied to solve different problems in engineering,economics, and other domains. Bio-inspired algorithms have also been applied indifferent branches of information technology such as networking and softwareengineering. Time series data mining is a field of information technology thathas its share of these applications too. In previous works we showed howbio-inspired algorithms such as the genetic algorithms and differentialevolution can be used to find the locations of the breakpoints used in thesymbolic aggregate approximation of time series representation, and in anotherwork we showed how we can utilize the particle swarm optimization, one of thefamous bio-inspired algorithms, to set weights to the different segments in thesymbolic aggregate approximation representation. In this paper we present, intwo different approaches, a new meta optimization process that produces optimallocations of the breakpoints in addition to optimal weights of the segments.The experiments of time series classification task that we conducted show aninteresting example of how the overfitting phenomenon, a frequently encounteredproblem in data mining which happens when the model overfits the training set,can interfere in the optimization process and hide the superior performance ofan optimization algorithm.

One-Step or Two-Step Optimization and the Overfitting Phenomenon: A Case Study on Time Series Classification