Designing Efficient Evolutionary Algorithms for Cluster Optimization: A Study on Locality



Cluster geometry optimization is an important problem from the
Chemistry area. Hybrid approaches combining evolutionary
algorithms and gradient-driven local search methods are one of the
most efficient techniques to perform a meaningful exploration of
the solution space to ensure the discovery of low energy
geometries. Here we perform a comprehensive study on the locality
properties of this approach to gain insight on the algorithm's
strengths and weaknesses. The analysis is accomplished through the
application of several static measures to randomly generated
solutions in order to establish the main properties of an extended
set of mutation and crossover operators. Locality analysis is
complemented with additional results obtained from optimization
runs. The combination of the outcomes allows us to propose a
robust hybrid algorithm that is able to quickly discover the
arrangement of the cluster's particles that correspond to optimal
or near-optimal solutions.


cluster geometry optimization, locality


Evolutionary Optimization

Book Chapter

Advances in Metaheuristics for Hard Optimization, 10, pp. 223-250, Spinger-Verlag, December 2007

Cited by

Year 2009 : 1 citations

 P. Cristea (2009). Application of Neural Networks in Image Processing and Visualization. In Geo-Spatial Visual Analytics: Geographic Information Processing and Visual Analytics for Environmental Security, R. Amicis et. at. (Eds.), pp. 59-71, Springer.