Augmented Gene Expression Programming: A Population Diversifying Paradigm

摘要

Gene Expression Programming, a popular evolutionary paradigm, has acquired great attention from researchers in the domain of mathematical modeling. In view of its insufficiencies arising due to premature convergence, this paper presents an Augmented Gene Expression Programming (AGEP) algorithm. Improvements suggested over classical GEP mechanism are (1) Opposition Based Learning to initialize the population of individuals to speed up convergence, (2) A diversifying clonal selection algorithm to eliminate bias towards fitter individuals, and (3) A population upliftment step to counter stagnancy over generations. A set of experiments related to function finding was conducted using AGEP and the results show a prominent improvement by AGEP over its classical counterpart, GEP and an improved version from authoritative literature (Niche technology of Outbreeding Fusion-OFN-GEP). The results have been used to reason that AGEP gives more accurate solutions at a better convergence rate.