Balancing the exploration and exploitation in an adaptive diversity guided genetic algorithm

Exploration and exploitation are the two cornerstones which characterize Evolutionary Algorithms (EAs) capabilities. Maintaining the reciprocal balance of the explorative and exploitative power is the key to the success of EA applications. Accordingly, this work is concerned with proposing a diversity-guided genetic algorithm with a new mutation scheme that is capable of exploring the unseen regions of the search space, as well as exploiting the already-found promising elements. The proposed mutation operator specifies different mutation rates for different sites of an encoded solution. These site-specific rates are carefully derived based on the underlying pattern of highly-fit solutions, adjusted to every single individual, and adapted throughout the evolution to retain a good ratio between exploration and exploitation. Furthermore, in order to more directly monitor the exploration vs. exploitation balance, the proposed method is augmented with a diversity control process assuring that the search process does not lose the required balance between the two forces.