Gradient subspace approximation: a direct search method for memetic computing

• Auger A, Hansen N, Zerpa Perez J M, Ros R, Schoenauer M (2009) Experimental comparisons of derivative free optimization algorithms. In: Experimental algorithms, Springer, pp 3–15

• Bäck T, Schwefel HP (1993) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1(1):1–23

  Article  Google Scholar 

• Bao Y, Hu Z, Xiong T (2013) A PSO and pattern search based memetic algorithm for SVMs parameters optimization. Neurocomputing 117:98–106

• Beyer HG, Finck S (2012) Happycat a simple function class where well-known direct search algorithms do fail. In: Coello Coello CA, et al., (ed) Parallel problem solving from nature—PPSN XII, vol 7491 of Lecture Notes in Computer Science, Springer, Berlin, pp 367–376

• Beyer HG, Schwefel HP (2002) Evol strategies: a comprehensive introduction Nat Comput 1(1):3–52

  Google Scholar 

• Brent RP (1973) Algorithms for minimization without derivatives, 1st edn. Prentice-Hall, Upper Saddle River, NJ

  MATH  Google Scholar 

• Brown M, Smith RE (2005) Directed multi-objective optimisation. Int J Comput Syst Signals 6(1):3–17

  Google Scholar 

• Caraffini F, Neri F, Iacca G (2013) Parallel memetic structures. Inf Sci 227:60–82

  Article  MathSciNet  Google Scholar 

• Caraffini F, Neri F, Picinali L (2014) An analysis on separability for memetic computing automatic design. Inf Sci 225:1–22

  Article  MathSciNet  Google Scholar 

• Chao G, Detong Z (2011) A secant algorithm with line search filter method for nonlinear optimization. Appl Math Model 35(2):879–894

  Article  MathSciNet  MATH  Google Scholar 

• Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31

  Article  Google Scholar 

• Dennis JE, Schnabel RB (1983) Numerical methods for unconstrained optimization and nonlinear equations. Prentice-Hall, Upper Saddle River

  MATH  Google Scholar 

• Domínguez IS, Aguirre AH, Valdez SI (2014) A new EDA by a gradient-driven density. In: Parallel problem solving from nature—PPSN XIII—13th international conference, pp 352–361

• Durillo JJ, Nebro AJ, Coello Coello CA, Garcia-Nieto J, Luna F, Alba E (2010) A study of multiobjective metaheuristics when solving parameter scalable problems. IEEE Trans Evol Comput 14(4):618–635

  Article  MATH  Google Scholar 

• Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, NewYork

  Book  MATH  Google Scholar 

• Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Natural computing series. Springer, NewYork

  Book  MATH  Google Scholar 

• Gong W, Cai Z, Ling CX (2006) ODE: a fast and robust differential evolution based on orthogonal design. In: Sattar A, Kang BH (eds) AI 2006: advances in artificial intelligence, vol 4304. Lecture Notes in Computer Science, Springer, Berlin, pp 709–718

• Griewank A (2000) Evaluating derivatives: principles and techniques of algorithmic differentiation. Number 19 in Frontiers in Applied Mathematics SIAM, Philadelphia, PA

• Hazen M, Gupta MR (2006) A multiresolutional estimated gradient architecture for global optimization. In: IEEE international conference on evolutionary computation, CEC, pp 3013–3020

• Hooke R, Jeeves TA (1961) Direct search solution of numerical and statistical problems. J ACM 8(2):212–229

  Article  MATH  Google Scholar 

• Junhua Z, Yan X, Luo L, ZhaoYang D, Yaoyao P (2014) Power system fault diagnosis based on history driven differential evolution and stochastic time domain simulation. Inf Sci 275:13–29

  Article  MathSciNet  Google Scholar 

• Kleijnen J P C (2015) Response surface methodology. In: Fu MC (ed) Handbook of simulation optimization, vol 216 of international series in operations research & management science, Springer, New York, pp 81–104

• Kukkonen S, Lampinen J (2006) Constrained real-parameter optimization with generalized differential evolution. In: Evolutionary computation, 2006. CEC 2006. IEEE Congress on IEEE, pp 207–214

• Lara A, Sanchez G, Coello Coello CA, Schütze O (2010) HCS: a new local search strategy for memetic multiobjective evolutionary algorithms. IEEE Trans Evol Comput 14(1):112–132

  Article  Google Scholar 

• LaTorre A (2009) A framework for hybrid dynamic evolutionary algorithms: multiple offspring sampling (MOS). Ph.D. thesis

• LaTorre A, Muelas S, Pena JM (2011) A mos-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test. Soft Comput 15(11):2187–2199

  Article  Google Scholar 

• Li X, Yao X (2012) Cooperatively coevolving particle swarms for large scale optimization. Evol Comput IEEE Trans 16(2):210–224

  Article  MathSciNet  Google Scholar 

• Liang JJ, Runarsson TP, Mezura-Montes E, Clerc M, Suganthan PN, Coello Coello CA, Deb K (2006) Problem definitions and evaluation criteria for the cec 2006 special session on constrained real-parameter optimization. Technical report, Nanyang Technological University, Singapore

• Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Technical report C3P Report 826, California Institute of Technology

• Neri F, Cotta C, Moscato P (eds) (2012) Handbook of memetic algorithms, vol 379 of Studies in Computational Intelligence. Springer

• Nocedal J, Wright S (2006) Numerical optimization. Springer series in operations research and financial engineering. Springer, NewYork

  Google Scholar 

• Omidvar MN, Mei Y, Li X (2014) Effective decomposition of large-scale separable continuous functions for cooperative co-evolutionary algorithms. In: Evolutionary computation (CEC), 2014 IEEE congress on IEEE, pp 1305–1312

• Osyczka A, Krenich S (2006) Evolutionary algorithms for global optimization. In: Pintér JD (ed) Global optimization, vol 85. Springer, NewYork, pp 267–300

  Chapter  Google Scholar 

• Polak E, Mayne DQA Robust secant method for optimization problems with inequality constraints. J Optim Theory Appl 33(4):463–477

• Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. In IEEE congress on evolution computation 2005 (CEC’05), vol 2, pp 1785–1791

• Schütze O, Martín A, Lara A, Alvarado S, Salinas E, Coello Coello CA (2015) The directed search method for multi-objective memetic algorithms. Comput Optim Appl 63(2):1–28

• Schwefel HP (1993) Evolution and optimum seeking. Wiley, New York, NY

  MATH  Google Scholar 

• Shiwen Y, Anyong Q (2005) Design of high-power millimeter-wave TM\(_{01}\) - TE\(_{11}\) mode converters by the differential evolution algorithm. IEEE Trans Plasma Sci 33(4):1372–1376

  Article  Google Scholar 

• Sivanandam SN, Deepa SN (2007) Introduction to genetic algorithms. Springer, NewYork

  MATH  Google Scholar 

• Storn R, Price K (1995) Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, International Computer Science Institute, Berkeley, Technical report TR95012

• Talbi EG (2002) A taxonomy of hybrid metaheuristics. J. Heuristics 8(5):541–564

  Article  Google Scholar 

• Tseng L-Y, Chen C (2008) Multiple trajectory search for large scale global optimization. In: Evolutionary computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress, pp 3052–3059

• Zapotecas Martínez S, Coello Coello CA, (2012) A direct local search mechanism for decomposition-based multi-objective evolutionary algorithms. (2012). In: IEEE congress on evolutionary computation (CEC’2012). IEEE Press, Brisbane

Download references