• Corpus ID: 7797043

Pure and Hybrid Evolutionary Computing in Global Optimization of Chemical Structures: from Atoms and Molecules to Clusters and Crystals

  title={Pure and Hybrid Evolutionary Computing in Global Optimization of Chemical Structures: from Atoms and Molecules to Clusters and Crystals},
  author={Kanchan Sarkar and S. P. Bhattacharyya},
The growth of evolutionary computing (EC) methods in the exploration of complex potential energy landscapes of atomic and molecular clusters, as well as crystals over the last decade or so is reviewed. The trend of growth indicates that pure as well as hybrid evolutionary computing techniques in conjunction of DFT has been emerging as a powerful tool, although work on molecular clusters has been rather limited so far. Some attempts to solve the atomic/molecular Schrodinger Equation (SE… 

Figures from this paper



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

A robust hybrid algorithm is proposed that is able to quickly discover the arrangement of the cluster’s particles that correspond to optimal or near-optimal solutions.

Towards an effective evolutionary approach for binary Lennard-Jones clusters

A hybrid approach that combines a steady-state evolutionary algorithm with a local search procedure and is able to deal with an optimization situation where both the composition of the aggregate and the spatial distribution of the particles must be determined is presented.

Computational and Theoretical Chemistry

Modern research in the chemical sciences seeks not only to make useful molecules and materials but to understand, design, and control their properties. Theory is at the very center of this effort,

The genetic algorithms in optimization of silicon clusters

This paper chooses genetic algorithms (GA) to optimize the structure of silicon clusters because it has a higher efficiency in finding the global optimal.

Empirical review of standard benchmark functions using evolutionary global optimization

We have employed a recent implementation of genetic algorithms to study a range of standard benchmark functions for global optimization. It turns out that some of them are not very useful as

Unbiased geometry optimisation of Morse atomic clusters

  • W. Pullan
  • Business
    IEEE Congress on Evolutionary Computation
  • 2010
The PBS algorithm incorporates and extends key techniques that have been developed in other cluster optimisation algorithms over the last decade, including the use of cut and paste operators, structure niching and a new operator, Directed Optimisation, which extends the previous concept of directed mutation.

Analysis of Crossover Operators for Cluster Geometry Optimization

Results show that operators that are sensitive to the phenotypical properties of the solutions help to enhance the performance of the optimization algorithm and increase the likelihood of performing a meaningful exploration of the search space.

Computational Methods for Predicting the Structures of Nanoalloys

Determining the geometric structure and chemical ordering of alloy nanoparticles is a crucial step for understanding and tailoring their properties. Here we review the methods for exploring the

Population Induced Instabilities in Genetic Algorithms for Constrained Optimization

This work proposes a different treatment of the constraints: instabilities in the evolving population are induced in a way that infeasible solution cannot survive as they are.