A study on diversity for cluster geometry optimization

Evolutionary Intelligence

Volumn 2, Issue 3, 2009, Pages 121-140

  Pereira, Francisco B ^a,b   Marques, Jorge M C ^c  

^a POLYTECHNIC INSTITUTE OF COIMBRA   (Portugal)

^b UNIVERSITY OF COIMBRA   (Portugal)

^c UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

Cluster geometry optimization; Distance measures; Diversity; Hybrid evolutionary algorithm; Morse clusters

Indexed keywords

EVOLUTIONARY ALGORITHMS; GEOMETRY;

CLUSTER GEOMETRIES; DISTANCE MEASURE; DIVERSITY; HYBRID EVOLUTIONARY ALGORITHM; MORSE CLUSTERS;

CLUSTERING ALGORITHMS;

EID: 73349099174     PISSN: 18645909     EISSN: 18645917     Source Type: Journal    
DOI: 10.1007/s12065-009-0020-5     Document Type: Article

References (52)

1. Braier P, Berry R, Wales D (1990) How the range of pair interactions govern features of multidimensional potentials. J Chem Phys 93(12): 8745-8756.
2. Burke EK, Gustafson S, Kendall G, Krasnogor N (2002) Advanced population diversity measures in genetic programming. In: 7th International conference parallel problem solving from nature (PPSN-2002), vol 2439. Springer Lecture Notes in Computer Science. Springer, Heidelberg, pp 341-350.
3. Cassioli A, Locatelli M, Schoen F (2008) Dissimilarity measures for population-based global optimization algorithms. Comput Optim Appl, online July 2008. doi:10.1007/s10589-008-9194-5.
4. Cassioli A, Locatelli M, Schoen F (2008) Global optimization of binary Lennard-Jones clusters. Optim Methods Softw, online December 2008. doi:10.1080/10556780802614101.
5. Cheng L, Cai W, Shao X (2004) A connectivity table for cluster similarity checking in the evolutionary optimization method. Chem Phys Lett 389: 309-314.
6. Cheng L, Yang J (2007) Global minimum structures of morse clusters as a function of the range of the potential: 81 ≤ n ≤ 160. J Phys Chem A 111: 5287-5293.
7. Deaven D, Ho K (1995) Molecular geometry optimization with a genetic algorithm. Phys Rev Lett 75: 288-291.
8. Demsar J (2006) Statistical comparisons of classifiers over multiples data sets. J Mach Learn Res 7: 1-30.
9. Doye JPK (2006) Physical perspectives on the global optimization of atomic clusters. In: Global optimization: scientific and engineering case studies. Springer, Heidelberg, pp 103-139.
10. Doye JPK, Leary R, Locatelli M, Schoen F (2004) Global optimization of morse clusters by potential energy transformations. Informs J Comput 16: 371-379.
11. Doye JPK, Wales DJ (1997) Structural consequences of the range of the interatomic potential. A menagerie of clusters. J Chem Soc Faraday Trans 93: 4233-4243.
12. Goldberg D, Deb K (1991) A comparative analysis of selection schemes used in genetic algorithms. In: Foundations of genetic algorithms, pp 69-93.
13. Grigoryan V, Alamanova D, Springborg M (2005) Structure and energetics of nickel, copper, and gold clusters. Eur Phys J D 34: 187-190.
14. Grosso A, Locatelli M, Schoen F (2007) A population-based approach for hard global optimization problems based on dissimilarity measures. Math Program Ser A 110: 373-404.
15. Hart W, Krasnogor N, Smith J (2004) Recent advances in memetic algorithms, volume 166 of Studies in fuzziness and soft computing, chapter Memetic evolutionary algorithms. Springer, Heidelberg, pp 3-27.
16. Hartke B (1993) Global geometry optimization of clusters using genetic algorithms. J Phys Chem 97: 9973-9976.
17. Hartke B (1999) Global cluster geometry optimization by a phenotype algorithm with niches: location of elusive minima, and low-order scaling with cluster size. J Comput Chem 20(16): 1752-1759.
18. Hartke B (2001) Global geometry optimization of atomic and molecular clusters by genetic algorithms. In: Proceedings of the genetic and evolutionary computation conference (GECCO-2001). Morgan Kaufmann, San Francisco, pp 1284-1291.
19. Hartke B (2004) Application of evolutionary algorithms to global cluster geometry optimization. In: Applications of evolutionary computation in chemistry. Structure and Bonding. Springer, Heidelberg, pp 33-53.
20. Johnston RL (2003) Evolving better nanoparticles: genetic algorithms for optimising cluster geometries. Dalton Trans 22: 4193-4207.
21. Jones JE (1924) On the determination of molecular fields. ii. From the equation of state of a gas. Proc R Soc A 106: 463-477.
22. De Jong K (1975) An analysis of the behavior of a class of genetic adaptive systems. Ph.D. thesis, Un. Michigan.
23. De Jong K, Sarma J (1993) Generation gaps revisited. In: Foundations of genetic algorithms, vol 2, pp 19-28.
24. Krasnogor N (2004) Recent advances in memetic algorithms, volume 166 of Studies in fuzziness and soft computing, chapter Towards robust memetic algorithms. Springer, Heidelberg, pp 185-207.
25. Krasnogor N, Blackburnem B, Hirst JD, Burke EK (2002) Multimeme algorithms for protein structure prediction. In: 7th International conference parallel problem solving from nature (PPSN-2002). Springer, Heidelberg, pp 769-778.
26. Lee J, Lee I-H, Lee J (2003) Unbiased global optimization of Lennard-Jones clusters for n ≤ 201 by conformational space annealing method. Phys Rev Lett 91(8): 080201. 1-080201. 4.
27. Lennard-Jones JE (1931) Cohesion. Proc Phys Soc 43: 461-482.
28. Liu DC, Nocedal J (1989) On the limited memory method for large scale optimization. Math Programm B 45: 503-528.
29. Locatelli M, Schoen F (2002) Fast global optimization of difficult Lennard-Jones clusters. Comput Optim Appl 21: 55-70.
30. Locatelli M, Schoen F (2003) Efficient algorithms for large scale global optimization: Lennard-Jones clusters. Comput Optim Appl 26: 173-190.
31. Lozano M, Herrera F, Krasnogor N, Molina D (2004) Real-coded memetic algorithms with crossover hill-climbing. Evol Comput 12: 273-302.
32. Mattiussi C, Waibel M, Floreano D (2004) Measures of diversity for populations and distances between individuals with highly reorganizable genomes. Evol Comput 12(4): 495-515.
33. Morse P (1929) Diatomic molecules according to the wave mechanics. ii. Vibrational levels. Phys Rev 34: 57-64.
34. Pelta D, Krasnogor N (2004) Recent advances in memetic algorithms, volume 166 of Studies in fuzziness and soft computing, chapter Multimeme algorithms using fuzzy logic based memes for protein structure prediction. Springer, Heidelberg, pp 49-64.
35. Pereira FB, Marques JMC (2008) A self-adaptive evolutionary algorithm for cluster geometry optimization. In: Proceedings of the eight international conference on hybrid intelligent systems. IEEE Press, New York, pp 678-683.
36. Pereira FB, Marques JMC, Leitao T, Tavares J (2006) Analysis of locality in hybrid evolutionary cluster optimization. In: Proceedings of the IEEE congress on evolutionary computation, vols 1-6. IEEE-Press, New York, pp 2270-2277.
37. Pereira FB, Marques JMC, Leitao T, Tavares J (2008) Efficient evolutionary algorithms for cluster optimization: a study on locality. In: Advances in metaheuristics for hard optimization. Springer, Heidelberg, pp 223-250.
38. Pullan W (2005) An unbiased population-based search for the geometry optimization of Lennard-Jones clusters: 2 ≤ n ≤ 372. J Comp Chem 26(9): 899-906.
39. Roberts C, Johnston RL, Wilson N (2000) A genetic algorithm for the structural optimization of morse clusters. Theor Chem Acc 104: 123-130.
40. Rogan J, Ramírez M, Munoz V, Valdivia J, García G, Ramírez R, Kiwi M (2009) Diversity driven unbiased search of minimum energy cluster configurations. J Phys Condens Matter 21: 084209.
41. Ronald S (1998) More distance functions for order-based encodings. In: Proceedings of the IEEE conference on evolutionary computation-CEC98, pp 558-563.
42. Smirnov B, Strizhev Y, Berry R (1999) Structures of large morse clusters. J Chem Phys 110(15): 7412-7420.
43. Smith J (2007) On replacement strategies in steady state evolutionary algorithms. Evol Comput 15(1): 29-59.
44. Spears W (1995) Adapting crossover in evolutionary algorithms. In: Proceedings of the fourth annual conference on evolutionary programming. MIT Press, Cambridge, pp 367-384.
45. Stillinger F (1999) Exponential multiplicity of inherent structures. Phys Rev E 59: 48-51.
46. Taillard E, Waelti P, Zuber J (2008) Few statistical tests for proportions comparison. Eur J Oper Res 185: 1336-1350.
47. Tsai CJ, Jordan KD (1993) Use of the histogram and jump walking methods for overcoming slow barrier crossing behavior in Monte Carlo simulations: applications to the phase transitions in the (ar)13 and (h2o)8 clusters. J Chem Phys 99: 6957-6970.
48. Whitley D (1989) The genitor algorithm and selection pressure: why ranked-based allocation of reproductive trials is best. In: Proceedings of the third international conference on genetic algorithms-ICGA89, pp 116-121.
49. Wineberg M, Oppacher F (2003) Distance between populations. In: Proceedings of the genetic and evolutionary computation conference-GECCO 2003, Part II, pp 1481-1492.
50. Xiao Y, Williams DE (1993) Genetic algorithms: a new approach to the prediction of the structure of molecular clusters. Chem Phys Lett 215: 17-24.
51. Zar J (1999) Biostatistical analysis, 4th edn. Prentice-Hall, Englewood Cliffs.
52. Zeiri Y (1995) Prediction of the lowest energy structure of clusters using a genetic algorithm. Phys Rev 51: 2769-2772.