On composing an algorithm portfolio

Shiu Yin Yuen1, Xin Zhang2
1Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
2College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

Tóm tắt

Từ khóa


Tài liệu tham khảo

Engelbrecht AP (2007) Computational intelligence, an introduction, 2nd edn. Wiley, New Jersey

Brownlee J (2011) Clever algorithms: nature-inspired programming pecipes, 1st edn. Lulu Enterprises, Australia

Lam AYS, Li VOK (2012) Real-coded chemical reaction optimization. IEEE Trans Evol Comput 16:339–353

Taha HA (1997) Operations research: an introduction, 6th edn. Prentice Hall, New Jersey

Nguyen QH, Ong Y-S, Lim MH (2009) A probabilistic memetic framework. IEEE Trans Evol Comput 13:604–623

Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82

Koehler GJ (2007) Conditions that obviate the no-free-lunch theorems for optimization. INFORMS J Comput 19:273–279

Vrugt JA, Robinson BA (2007) Improved evolutionary optimization from genetically adaptive multimethod search. Proc Natl Acad Sci 104:708–711

Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms: model, taxonomy and design issues. IEEE Transs Evol Comput 9:474–488

Munoz MA, Kirley M, Halgamuge SK (2013) The algorithm selection problem on the continuous optimization domain. In: Moewes C, Nurnberger A (eds) Computational intelligence in intelligent data analysis, SCI 445. Springer, Heidelberg, pp 75–89

Gomes CP, Selman B (2001) Algorithm portfolios. Artif Intell 126:43–62

Dietterich TG (2000) Ensemble methods in machine learning, vol 1857., Lecture notes in computer scienceSpringer, Heidelberg

Burke EK, Gendreau M, Hyde M, Kendall G, Ochoa G, Ozcan E, Qu R (2013) Hyper-heuristics: a survey of the state of the art. J Oper Res Soc 64:1695–1724

Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13:398–417

Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11:1679–1696

Du W, Li B (2008) Multi-strategy ensemble particle swarm optimization for dynamic optimization. Inf Sci 178:3096–3109

Vrugt JA, Robinson BA, Hyman JM (2009) Self-adaptive multimethod search for global optimization in real-parameter spaces. IEEE Trans Evol Comput 13:243–259

Peng F, Tang K, Chen G, Yao X (2010) Population-based algorithm portfolios for numerical optimization. IEEE Trans Evol Comput 14:782–800

Wang Y, Li B (2010) Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization. Memetic Comput 2:3–24

Grobler J, Engelbrecht AP, Kendall G, Yadavalli VSS (2013) Multi-method algorithms: investigating the entity-to-algorithm allocation problem. In: Proc. IEEE congress on evolutionary computation. IEEE Press, New York, pp 570–577

Hadka D, Reed P (2013) Borg: an auto-adaptive many-objective evolutionary computing framework. Evol Comput 21:231–259

Li K, Fialho A, Kwong S, Zhang Q (2014) Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 18:114–130

Hansen N (2011) The CMA evolutionary strategy: a tutorial. Tech. Rep., http://www.lri.fr/~hansen/cmatutorial.pdf . Accessed 23 Feb 2015

Black-Box Optimization Benchmarking (BBOB) (2013) http://coco.gforge.inria.fr/doku.php?id=bbob-2013 . Accessed 23 Feb 2015

Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15:4–31

Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15:55–66

Particle Swarm Central (2015) http://www.particleswarm.info/ . Accessed 23 Feb 2015

Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471

Yuen SY, Chow CK, Zhang X (2013) Which algorithm should I choose at any point of the search: an evolutionary portfolio approach. In: Proc. of the 14th Int. Conf. on Genetic and Evol. Comput. Conf (GECCO). ACM, New York, pp 567–574

Lobo FJ, Lima CF, Michalewicz Z (2007) Parameter setting in evolutionary algorithms. Springer, Berlin

Yuen SY, Zhang X (2013) On Composing an (evolutionary) algorithm portfolio. In: Proc. of the 14th Int. Conf. on Genetic and Evol. Comput. Conf (GECCO). ACM, New York, pp 83–84

Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-parameter Optimization”, Nanyang Technol. Univ. and IIT Kanpur, Singapore and Kanpur, India, Tech. Rep. 2005005

Garcia S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour a case study on the CEC’2005 special session on real parameter optimization. J Heuristics 15:617–644

Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18:50–60

Dowold K, Aderhold A, Scheidler A, Middendorf M (2011) Performance evaluation of artificial bee colony optimization and new selection schemes. Memetic Comput 3:149–162

Sörensen K (2015) Metaheuristics—the metaphor exposed. Int Trans Oper Res 22:3–18

Liang JJ, Qu B-Y, Suganthan PN, Hernández-Díaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session and competition on real-parameter optimization. Technical report, Nanyang Technological University, Singapore

Tang K, Peng F, Chen G, Yao X (2014) Population-based algorithm portfolios with automated constituent algorithms selection. Inf Sci 279:94–104