Analysis of Crossover Operators for Cluster Geometry Optimization

Pereira, Francisco B.; Marques, Jorge M. C.

doi:10.1007/978-94-007-0093-2_5

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…GA coupled with a tight-binding potential is employed by Ping et al [194] to optimize neutral lead clusters Pb n (n = 2-20). Pereira et al [195] made a study on the effectiveness of different crossover operators in the global optimization of atomic clusters. They came up with a 'Cut and Splice' crossover operator which proved to be extremely useful.…”

Section: Metal Clusters and Nanoalloysmentioning

confidence: 99%

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

Sarkar,

Bhattacharyya

2015

Preprint

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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) directly by genetic algorithms (GA) are available in literature. At the Born-Oppenheimer level of approximation GA-density methods appear to be a viable tool which could be more extensively explored in the coming years, specially in the context of designing molecules and materials with targeted properties.

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Section: Metal Clusters and Nanoalloysmentioning

confidence: 99%

“…"Splice" joins the head of one string with the tail of the other one. Pereira et al [195] modified this operator by the way it determines the sub-clusters to be exchanged. Here a random atom from the parent string is selected and placed on the offspring.…”

Section: Metal Clusters and Nanoalloysmentioning

confidence: 99%

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

Sarkar,

Bhattacharyya

2015

Preprint

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A sphere-cut-splice crossover for the evolution of cluster structures

Chen

Jiang

et al. 2013

The Journal of Chemical Physics

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A new crossover operator is proposed to evolve the structures of the atomic clusters. It uses a sphere rather than a plane to cut and splice the parent structures. The child cluster is constructed by the atoms of one parent which lie inside the sphere, and the atoms of the other parent which lie outside the sphere. It can reliably produce reasonable offspring and preserve the good schemata in parent structures, avoiding the drawbacks of the classical plane-cut-splice crossover in the global searching ability and the local optimization speed. Results of Lennard-Jones clusters (30 ≤ N ≤ 500) show that at the same settings the genetic algorithm with the sphere-cut-splice crossover exhibits better performance than the one with the plane-cut-splice crossover. The average number of local minimizations needed to find the global minima and the average number of energy evaluation of each local minimization in the sphere scheme is 0.8075 and 0.8386 of that in the plane scheme, respectively. The mean speed-up ratio for the entire testing clusters reaches 1.8207. Moreover, the sphere scheme is particularly suitable for large clusters and the mean speed-up ratio reaches 2.3520 for the clusters with 110 ≤ N ≤ 500. The comparison with other successful methods in previous studies also demonstrates its good performance. Finally, a further analysis is presented on the statistical features of the cutting sphere and a modified strategy that reduces the probability of using tiny and large spheres exhibits better global search.

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Cluster energy optimizing genetic algorithm

Kazakova

Rahman

2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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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.

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Analysis of Crossover Operators for Cluster Geometry Optimization

Cited by 3 publications

References 20 publications

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

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

A sphere-cut-splice crossover for the evolution of cluster structures

Cluster energy optimizing genetic algorithm

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