Accelerating optimization using probabilistic affinity evaluation and Clonal Selection Principle

The performance of evolutionary algorithms in optimization is tightly coupled to the computational effort required by the evaluation of the objective function. If the objective function is too expensive to evaluate, then, the elaboration of the procedures of the search algorithm alone may not result in the required improvement in algorithm's performance. However, if there is a way to speed up or decrease the number of objective function evaluations, even a basic algorithms can potentially achieve better results due to the increased number of generation run in given time. This paper considers a probabilistic objective function evaluation scheme in which the candidate solutions are evaluated and evolved based on their objective function value.