Fitness-probability cloud and a measure of problem hardness for evolutionary algorithms

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6622 LNCS, Issue , 2011, Pages 108-117

  Lu, Guanzhou ^a   Li, Jinlong ^b   Yao, Xin ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ESCAPE PROBABILITY; EVOLVABILITY; FITNESS EVALUATIONS; PROBLEM DIFFICULTY; SUBSET SUM; TEST PROBLEM; EVOLUTIONARY ALGORITHMS (EAS); IMPORTANT FEATURES; PROBLEM HARDNESS;

COMBINATORIAL OPTIMIZATION; HARDNESS; HEALTH; OPTIMIZATION; PROBABILITY;

EVOLUTIONARY ALGORITHMS;

EID: 79955770169     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-20364-0_10     Document Type: Conference Paper

References (16)

1. Vassilev, V.K., Fogarty, T.C., Miller, J.F.: Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application, pp. 3-44. Springer, Inc., New York (2003)
2. Borenstein, Y., Poli, R.: Information landscapes and problem hardness. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 1425-1431. ACM, New York (2005)
3. Wright, S.: The roles of mutation, inbreeding, crossbreeding, and selection in evolution. In: Proc. 6th Congr. Genetics, vol. 1, p. 365 (1932)
4. Jones, T., Forrest, S.: Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 184-192. Morgan Kaufmann Publishers Inc., San Francisco (1995)
5. Collard, P., Vérel, S., Clergue, M.: Local search heuristics: Fitness cloud versus fitness landscape. CoRR, abs/0709.4010 (2007)
6. Vanneschi, L., Tomassini, M., Collard, P., Vérel, S.: Negative slope coefficient: A measure to characterize genetic programming fitness landscapes. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 178-189. Springer, Heidelberg (2006)
7. Smithand, T., Husbands, P., Layzell, P., O'Shea, M.: Fitness landscapes and evolvability. Evol. Comput. 10, 1-34 (2002)
8. Vanneschi, L., Clergue, M., Collard, P., Tomassini, M., Vérel, S.: Fitness clouds and problem hardness in genetic programming. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 690-701. Springer, Heidelberg (2004)
9. Vanneschi, L., Vérel, S., Tomassini, M., Collard, P.: NK landscapes difficulty and negative slope coefficient: How sampling influences the results. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 645-654. Springer, Heidelberg (2009)
10. Merz, P.: Advanced fitness landscape analysis and the performance of memetic algorithms. Evol. Comput. 12, 303-325 (2004)
11. He, J., Yao, X.: Towards an analytic framework for analysing the computation time of evolutionary algorithms. Artificial Intelligence 145, 59-97 (2003)
12. Madras, N.: Lectures on Monte Carlo Methods. American Mathematical Society, Rhode Island (2002)
13. Mengshoel, O.J., Goldberg, D.E., Wilkins, D.C.: Deceptive and other functions of unitation as bayesian networks (1998)
14. Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. In: Foundations of Genetic Algorithms, vol. 2, pp. 93-108 (1993)
15. Rivest, R., Stein, C., Cormen, T., Leiserson, C.: Introduction to Algorithms. MIT Press and McGraw-Hill (1990)
16. Naudts, B., Kallel, L.: A comparison of predictive measures of problem difficulty in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 4(1), 1-15 (2000)