Scalability of a Hybrid Extended Compact Genetic Algorithm for Ground State Optimization of Clusters

ISSN： 1042-6914

Source： Materials and Manufacturing Processes, Vol.22, Iss.5, 2007-06, pp. : 570-576

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Abstract

We analyze the utility and scalability of extended compact genetic algorithm (eCGA) - a genetic algorithm (GA) that automatically and adaptively mines the regularities of the fitness landscape using machine learning methods and information theoretic measures - for ground state optimization of clusters. In order to reduce the computational time requirements while retaining the high reliability of predicting near-optimal structures, we employ two efficiency-enhancement techniques: (1) hybridizing eCGA with a local search method, and (2) seeding the initial population with lowest energy structures of a smaller cluster. The proposed method is exemplified by optimizing silicon clusters with 4-20 atoms. The results indicate that the population size required to obtain near-optimal solutions with 98% probability scales sub linearly (as Θ(n0.83)) with the cluster size. The total number of function evaluations (cluster energy calculations) scales sub-cubically (as Θ(n2.45)), which is a significant improvement over exponential scaling of poorly designed evolutionary algorithms.