1. A computer-implemented method for intelligent evolutionary optimization of a workflow, comprising: receiving, by a Big Data infrastructure from a client computer, a run request comprising workflow input parameters, an objective function, a pool of initial configuration parameters, and completion criteria; running, by the Big Data infrastructure, multiple parallel instances of a workflow based on the pool of initial configuration parameters resulting in corresponding parallel output results; storing, by the Big Data infrastructure, the workflow input parameters and the corresponding output results; modeling, by the Big Data infrastructure utilizing at least one of a statistical technique and a parametric technique, a relationship between changes in the workflow input parameters and the corresponding output results to gain insights into which input changes have the greatest impact and which input changes have the least impact on quality of the output results; determining, by the Big Data infrastructure based on at least one of the objective function and the completion criteria as compared to results of the modeling, that optimal output results have been achieved; tracking by the Big Data infrastructure the relationship between the change in the workflow input parameters and the corresponding output results and providing decisions regarding which workflow input parameters to use during additional workflow run requests which optimize the output results to reduce time, effort and computing expenses; and transmitting, by the Big Data infrastructure, the optimal output results and input-output variable relationships to the client computer.