Two widely noted deficiencies of the Genetic Algorithm (GA) are its tendency to get trapped at local maxima and the difficulty it has handling a changing environment after convergence has occurred. These two problems can be shown to be related; a solution to the former should help to alleviate the latter. In the 1930s, a mechanism was proposed by Sewall Wright to address the local maxima problem. He called this process the Shifting Balance Theory (SBT) of evolution. In this dissertation, it is conjectured that the same mechanisms that theoretically help the SBT prevent premature convergence in nature will improve the GA's performance in highly multimodal environments and when tracking a global optima in dynamic environments. This should especially be true for dynamic environments that have remained stationary for a long time allowing populations to prematurely converge.
When abstracting the SBT process to add it to the GA, the SBT had to be modified to remove defects inherent in its original formulation. This was carefully done to keep the properties of the SBT that were believed to both increase the adaptive abilities of the GA and prevent it from prematurely converging. The resulting mechanism, which is a multipopulational system, can be added as a 'plug-in' module to any evolutionary algorithm and therefore was named the Shifting Balance Evolutionary Algorithm (SBEA). When applied to the GA, it is called the SBGA.
While formulating the SBGA, some mathematical theories had to be developed: the definition of the distance between two populations, and a measure of the amount of overlap between them. These concepts were needed for the mechanisms used to control the multipopulations.
Using an implementation of the SBGA, experimental results and analysis are presented demonstrating that the SBGA does, in fact, lessen the problem of premature convergence and also improves performance under a dynamic environment, thereby mitigating both deficiencies.