Cluster energy optimizing genetic algorithm

Authors:
Vera A. Kazakova;Annie S. Wu;Talat S. Rahman
Affiliations:
University of Central Florida, Orlando, FL, USA;University of Central Florida, Orlando, FL, USA;University of Central Florida, Orlando, FL, USA
Venue:
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Year:
2013

Citing 3
Cited 0

On the limited memory BFGS method for large scale optimization

Mathematical Programming: Series A and B
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Evolving neural networks through augmenting topologies

Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Nanoclusters are small clumps of atoms of one or several materials. A cluster possesses a unique set of material properties depending on its configuration (i.e. the number of atoms, their types, and their exact relative positioning). Finding and subsequently testing these configurations is of great interest to physicists in search of new advantageous material properties. To facilitate the discovery of ideal cluster configurations, we propose the Cluster Energy Optimizing GA (CEO-GA), which combines the strengths of Johnston's BCGA [18], Pereira's H-C&S crossover [25], and two new mutation operators: Local Spherical and Center of Mass Spherical. The advantage of CEO-GA is its ability to evolve optimally stable clusters (those with lowest potential energy) without relying on local optimization methods, as do other commonly used cluster evolving GAs, such as BCGA.