This operator significantly reduces the tendency of the genetic algorithm to prematurely converge when applied to an important class of functions. Furthermore, it does so with algorithmic complexity and execution time which are improved with respect to those of standard selection operators. This operator further reduces the tendency to premature convergence, and often improves load balancing and thereby overall execution time. A number of promising avenues for investigation of adaptive parameters have been identified, including the parallel meta-genetic algorithm and use of statistical convergence measures to control parameter adaptation and termination criteria. Another area of investigation is the application of genetic algorithms to multimodal functional optimizations problems.
Genetic algorithms tend to converge on only one local optimum of the function.
Various niching techniques exist which allow convergence towards multiple optima simultaneously be maintaining deverse sub-populations. One such technique is the execution of independent genetic algorithms on multiple processors. Research focuses on the identification of interprocessor communication techniques which maintain diversity.