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Re-sampled inheritance search: high performance despite the simplicity

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Abstract

This paper proposes re-sampled inheritance search (RIS), a novel algorithm for solving continuous optimization problems. The proposed method, belonging to the class of Memetic Computing, is very simple and low demanding in terms of memory employment and computational overhead. The RIS algorithm is composed of a stochastic sample mechanism and a deterministic local search. The first operator randomly generates a solution and then recombines it with the best solution detected so far (inheritance) while the second operator searches in an exploitative way within the neighbourhood indicated by the stochastic operator. This extremely simple scheme is shown to display a very good performance on various problems, including hard to solve multi-modal, highly-conditioned, large scale problems. Experimental results show that the proposed RIS is a robust scheme that competitively performs with respect to recent complex algorithms representing the-state-of-the-art in modern continuous optimization. In order to further prove its applicability in real-world cases, RIS has been used to perform the control system tuning for yaw operations on a helicopter robot. Experimental results on this real-world problem confirm the value of the proposed approach.

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References

  • Abbass HA (2002) An evolutionary artificial neural networks approach for breast cancer diagnosis. Artif Intell Med 25(3):265–281

    Article  Google Scholar 

  • Arnold DV, Beyer H-G (May 2003) On the benefits of populations for noisy optimization. Evol Comput 11:111–127

    Google Scholar 

  • Auger A, Teytaud O (2007) Continuous lunches are free! In: Proceedings of the 9th annual conference on genetic and evolutionary computation. ACM, pp 916–922

  • Bagnell J, Schneider J (2001) Autonomous helicopter control using reinforcement learning policy search methods. In: Proceedings of IEEE international conference on robotics and automation, vol 2

  • Cai G, Chen B, Lee T (2010) An overview on development of miniature unmanned rotorcraft systems. Fronti Electr Electron Eng China 5(1):1–14

    Article  Google Scholar 

  • Caponio A, Cascella GL, Neri F, Salvatore N, Sumner M (2007) A fast adaptive memetic algorithm for on-line and off-line control design of PMSM drives. IEEE Trans Syst Man Cybern part B 37(1):28–41

    Article  Google Scholar 

  • Caponio A, Kononova A, Neri F (2010) Differential evolution with scale factor local search for large scale problems. In: Tenne Y, Goh C-K (eds) Computational intelligence in expensive optimization problems, vol 2 of studies in evolutionary learning and optimization, chap. 12. Springer, Berlin, pp 297–323

  • Caraffini F, Iacca G, Neri F, Mininno E (2012) Three variants of three stage optimal memetic exploration for handling non-separable fitness landscapes. In: Proceedings of the UK workshop on computational iintelligence

  • Caraffini F, Iacca G, Neri F, Mininno E (2012) The importance of being structured: a comparative study on multi stage memetic approaches. In: Proceedings of the UK workshop on computational iintelligence

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

    Article  MathSciNet  Google Scholar 

  • Cyber Dyne Srl Home Page (2012) Kimeme. http://cyberdynesoft.it/

  • De Moura Oliveira P (2005) Modern heuristics review for pid control systems optimization: A teaching experiment. In: Proceedings of the 5th international conference on control and automation, ICCA’05, pp 828–833

  • Eiben AE, Smit SK (2011) Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol Comput 1(1):19–31

    Article  Google Scholar 

  • Fan XF, Zhu Z, Ong YS, Lu YM, Shen ZX, Kuo J-L (2007) A direct first principle study on the structure and electronic properties of bexzn1-xo. Appl Phys Lett 91:121

    Google Scholar 

  • Fleming P, Purshouse R (2002) Evolutionary algorithms in control systems engineering: a survey. Control Eng Pract 10(11):1223–1241

    Article  Google Scholar 

  • Garcia S, Fernandez A, Luengo J, Herrera F (2008) A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput 13(10):959–977

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Co., Reading

    MATH  Google Scholar 

  • Handoko SD, Kwoh CK, Ong YS (2010) Feasibility structure modeling: an effective chaperon for constrained memetic algorithms. IEEE Trans Evol Comput 14(5):740–758

    Article  Google Scholar 

  • Hansen N, Müller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11(1):1–18

    Article  Google Scholar 

  • Hansen N, Auger A, Finck S, Ros R et al (2010) Real-parameter black-box optimization benchmarking 2010: noiseless functions definitions. Technical Report, RR-6829, INRIA,

  • Hart WE, Krasnogor N, Smith JE (2004) Memetic evolutionary algorithms. In: Hart WE, Krasnogor N, Smith JE (eds)Recent advances in memetic algorithms. Springer, Berlin, pp 3–27

  • Hasan SMK, Sarker R, Essam D, Cornforth D (2009) Memetic algorithms for solving job-shop scheduling problems. Memetic Comput J 1(1):69–83

    Article  Google Scholar 

  • Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70

    MathSciNet  MATH  Google Scholar 

  • Iacca G, Neri F, Mininno E, Ong YS, Lim MH (2012) Ockham’s razor in memetic computing: three stage optimal memetic exploration. Inf Sci 188:17–43

    Article  MathSciNet  Google Scholar 

  • Iacca G, Caraffini F, Neri F (2012) Compact differential evolution light. J Comput Sci Technol 27(5):1056–1076

    Article  MathSciNet  Google Scholar 

  • Islam S, Das S, Ghosh S, Roy S, Suganthan P (2012) An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans Syst Man Cybern Part B Cybern 42:482–500

    Article  Google Scholar 

  • Ji M, Klinowski J (2006) Taboo evolutionary programming: a new method of global optimization. Proc R Soc Lond Ser A Math Phys Eng Sci 462(2076):3613–3627

    Article  MathSciNet  MATH  Google Scholar 

  • Joshi R, Sanderson AC (1999) Minimal representation multisensor fusion using differential evolution. IEEE Trans Syst Man Cybern Part A 29(1):63–76

    Article  Google Scholar 

  • Lee C-Y, Yao X (2004) Evolutionary programming using mutations based on the levy probability distribution. IEEE Trans Evol Comput 8(1):1–13

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  • Lim KK, Ong Y-S, Lim MH, Chen X, Agarwal A (2008) Hybrid ant colony algorithms for path planning in sparse graphs. Soft Comput 12(10):981–994

    Article  Google Scholar 

  • Lozano M, Herrera F, Molina D (2011) Scalability of evolutionary algorithms and other metaheuristics for large scale continuous optimization problems. Soft Comput 15(11)

  • Mallipeddi R, Mallipeddi S, Suganthan PN (2010) Ensemble strategies with adaptive evolutionary programming. Inf Sci 180(9):1571–1581

    Article  Google Scholar 

  • Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696

    Article  Google Scholar 

  • Meuth R, Lim MH, Ong YS, Wunsch-II DC (2009) A proposition on memes and meta-memes in computing for higher-order learning. Memetic Comput J 1(2):85–100

    Article  Google Scholar 

  • Mininno E, Neri F, Cupertino F, Naso D (2011) Compact differential evolution. IEEE Trans Evol Comput 15(1):32–54

    Article  Google Scholar 

  • Molina D, Lozano M, Garcia-Martinez C, Herrera F (2010) Memetic algorithms for continuous optimization based on local search chains. Evol Comput 18(1):27–63

    Article  Google Scholar 

  • Molina D, Lozano M, Herrera F (2010) MA-SW-Chains: memetic algorithm based on local search chains for large scale continuous global optimization. In: IEEE congress on, evolutionary computation pp 1–8

  • Montes de Oca MA, Stutzle T, Birattari M, Dorigo M (2009) Frankenstein’s PSO: a composite particle swarm optimization algorithm. IEEE Trans Evol Comput 13(5):1120–1132

    Article  Google Scholar 

  • Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Technical Report 826

  • Moscato P, Norman M (1989) A competitive and cooperative approach to complex combinatorial search. Technical report 790

  • Neri F, Mininno E (2010) Memetic compact differential evolution for cartesian robot control. IEEE Comput Intell Mag 5(2):54–65

    Article  Google Scholar 

  • Neri F, Tirronen V (2010) Recent advances in differential evolution: a review and experimental analysis. Artif Intell Rev 33(1–2):61–106

    Article  Google Scholar 

  • Neri F, Cotta C (2012) Memetic algorithms and memetic computing optimization: a literature review. Swarm Evol Comput 2:1–14

    Article  Google Scholar 

  • Neri F, Toivanen J, Mäkinen RAE (2007a) An adaptive evolutionary algorithm with intelligent mutation local searchers for designing multidrug therapies for HIV. Appl Intell 27:219–235

    Article  Google Scholar 

  • Neri F, Toivanen JI, Cascella GL, Ong YS (2007b) An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE/ACM Trans Comput Biol Bioinform 4(2):264–278

    Article  Google Scholar 

  • Neri F, Iacca G, Mininno E (2011) Disturbed exploitation compact differential evolution for limited memory optimization problems. Inf Sci 181(12):2469–2487

    Article  MathSciNet  Google Scholar 

  • Neri F, Cotta C, Moscato P (2011) Handbook of memetic algorithms, vol 379 of Studies in Computational Intelligence. Springer, Berlin

  • Neri F, Weber M, Caraffini F, Poikolainen I (2012) Meta-lamarckian learning in three stage optimal memetic exploration. In: Proceedings of the UK workshop on computational iintelligence

  • Nguyen QC, Ong YS, Lim MH (2009a) A probabilistic memetic framework. IEEE Trans Evol Comput 13(3):604–623

    Article  Google Scholar 

  • Nguyen QH, Ong YS, Hiot LM, Krasnogor N (2009b) Adaptive cellular memetic algorithms no access. Evol Comput 17(2):231–256

    Article  Google Scholar 

  • Ong YS, Keane AJ (2004) Meta-Lamarkian learning in memetic algorithms. IEEE Trans Evol Comput 8(2):99–110

    Article  Google Scholar 

  • Ong Y-S, Lim M-H, Chen X (2010) Memetic computation-past, present and future. IEEE Comput. Intell Mag 5(2):24–31

    Article  Google Scholar 

  • Passow BN, Gongora MA, Coupland S, Hopgood AA (2008) Real-time evolution of an embedded controller for an autonomous helicopter. In: Proceedings of the IEEE international congress on evolutionary computation (CEC’08), (Hong Kong), pp 2538–2545

  • Peng F, Tang K, Chen G, Yao X (2010) Population-based algorithm portfolios for numerical optimization. IEEE Trans Evol Comput 14(5):782–800

    Article  Google Scholar 

  • Poikolainen I, Caraffini F, Neri F, Weber M (2012) Handling non-separability in three stage memetic exploration. In: Proceedings of the fifth international conference on bioinspired optimization methods and their applications, pp 195–205

  • Poikolainen I, Neri F, Mininno E, Iacca G, Weber M (2012) Shrinking optimal three stage memetic exploration. In: Proceedings of the fifth international conference on bioinspired optimization methods and their applications, pp 61–74

  • Price KV, Storn R, Lampinen J (2005) Differential Evolution: a practical approach to global optimization. Springer, Berlin

  • Rogalsky T, Derksen RW (2000) Hybridization of differential evolution for aerodynamic design. In: Proceedings of the 8th annual conference of the computational fluid dynamics society of Canada, pp 729–736

  • Rosenbrock HH (1960) An automatic Method for finding the greatest or least value of a function. Comput J 3(3):175–184

    Article  MathSciNet  Google Scholar 

  • Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y-P, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical Report, 2005005. Nanyang Technological University and KanGAL, Singapore and IIT Kanpur, India

  • Tan KC, Cheong CY, Goh CK (2007) Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation. Eur J Oper Res 177(2):813–839

    Google Scholar 

  • Tang K, Yao X, Suganthan PN, MacNish C, Chen YP, Chen CM, Yang Z (2007) Benchmark functions for the CEC 2008 special session and competition on large scale global optimization. Technical report, Nature Inspired Computation and Applications Laboratory, USTC, China

  • Tang K, Li X, Suganthan PN, Yang Z, Weise T (2010) Benchmark functions for the CEC’2010 special session and competition on large-scale global optimization. Technical report. University of Science and Technology of China (USTC), School of Computer Science and Technology, Nature Inspired Computation and Applications Laboratory (NICAL), Hefei, Anhui, China

  • Tseng L-Y, Chen C (2008) Multiple trajectory search for large scale global optimization. In: Proceedings of the IEEE congress on, evolutionary computation, pp 3052–3059

  • Vrugt JA, Robinson BA, Hyman JM (2009) Self-adaptive multimethod search for global optimization in real-parameter spaces. IEEE Trans Evol Comput 13(2):243–259

    Article  Google Scholar 

  • Wescott T (2000) Pid without a phd. Embed Syst Program 13(11):86–108

    Google Scholar 

  • Wilcoxon F (1945) Individual comparisons by ranking methods. Biometr Bull 1(6):80–83

    Google Scholar 

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

    Article  Google Scholar 

  • Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  • Zaharie D (2009) Influence of crossover on the behavior of differential evolution algorithms. Appl Soft Comput 9(3):1126–1138

    Article  Google Scholar 

  • Zamuda A, Brest J (2012) Population reduction differential evolution with multiple mutation strategies in real world industry challenges. In: ICAISC (SIDE-EC), pp 154–161

  • Zamuda A, Brest J, Boşković B, Zumer V (2011) Differential evolution for parameterized procedural woody plant models reconstruction. Appl Soft Comput 11(8):4904–4912

    Google Scholar 

  • Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13:945–958

    Google Scholar 

  • Zhou J, Ji Z, Shen L, (2008) Simplified intelligence single particle optimization based neural network for digit recognition. In: Proceedings of the Chinese conference on, pattern recognition, pp 1–5 (1031–1847)

Download references

Acknowledgments

The numerical experiments have been carried out on the computer network of the De Montfort University by means of the software for distributed optimization Kimeme (Cyber Dyne Srl Home Page 2012) and the Memenet project. We thank Dr. Lorenzo Picinali, Dr. Nathan Jeffery, and Mr. David Tunnicliffe for the technical support of the computer network.

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Authors and Affiliations

  1. Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester, LE1 9BH, UK

    Fabio Caraffini & Ferrante Neri

  2. Department of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35 (Agora), 40014 , Jyväskylä, Finland

    Fabio Caraffini & Ferrante Neri

  3. DMU’s Interdisciplinary Group in Intelligent Transport Systems (DIGITS), School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester,  LE1 9BH, UK

    Benjamin N. Passow

  4. INCAS 3-Innovation Centre for Advanced Sensors and Sensor Systems, P.O. Box 797, 9400 AT , Assen, The Netherlands

    Giovanni Iacca

Authors
  1. Fabio Caraffini
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  2. Ferrante Neri
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  3. Benjamin N. Passow
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  4. Giovanni Iacca
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Corresponding author

Correspondence to Ferrante Neri.

Additional information

Communicated by G. Acampora.

This research is supported by the Academy of Finland, Akatemiatutkija 130600, “Algorithmic design issues in Memetic Computing”. INCAS\(^{3}\) is co-funded by the Province of Drenthe, the Municipality of Assen, the European Fund for Regional Development and the Ministry of Economic Affairs, Peaks in the Delta.

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Caraffini, F., Neri, F., Passow, B.N. et al. Re-sampled inheritance search: high performance despite the simplicity. Soft Comput 17, 2235–2256 (2013). https://doi.org/10.1007/s00500-013-1106-7

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