Investigating adaptive mutation in the generalized generation gap (G3) algorithm for unconstrained global optimization
Jason Teo · 2007
For function optimization problems in continuous search spaces, one of the main difficulties currently faced is that of locating high quality solutions. This problem is particularly pertinent for continuous multimodal problems where the quality rather than computational efficiency is more important as a test of the solver's ability to escape local optima and finding solutions near the global optimum [3]. Moreover, this difficulty is further compounded when the function involves large numbers of variables, which translates into a highly deceptive fitness landscape with very large numbers of local optima [2].