Evolutionary support vector machines (ESVMs) are a novel technique that assimilates
    the learning engine of the state-of-the-art support vector
    machines (SVMs) but evolves the coefficients of the decision
    function by means of evolutionary algorithms (EAs). The new
    method has accomplished the purpose for which it has been
    initially developed, that of a simpler alternative to the canonical SVM approach
    for solving the optimization component of training. ESVMs,
    as SVMs, are natural tools for primary application to
    classification. However, since the latter had been further on extended
    to also handle regression, it is the scope of this
    paper to present the corresponding evolutionary paradigm. In particular, we consider
    the hybridization with the classical epsilon-support vector
    regression (epsilon-SVR) introduced by Vapnik and the
    subsequent evolution of the coefficients of the regression
    hyperplane. epsilon-evolutionary support regression (epsilon-ESVR) is validated on the Boston housing
    benchmark problem and the obtained results demonstrate
    the promise of ESVMs also as concerns regression.