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Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead

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Abstract

Compact algorithms are Estimation of Distribution Algorithms which mimic the behavior of population-based algorithms by means of a probabilistic representation of the population of candidate solutions. These algorithms have a similar behaviour with respect to population-based algorithms but require a much smaller memory. This feature is crucially important in some engineering applications, especially in robotics. A high performance compact algorithm is the compact Differential Evolution (cDE) algorithm. This paper proposes a novel implementation of cDE, namely compact Differential Evolution light (cDElight), to address not only the memory saving necessities but also real-time requirements. cDElight employs two novel algorithmic modifications for employing a smaller computational overhead without a performance loss, with respect to cDE. Numerical results, carried out on a broad set of test problems, show that cDElight, despite its minimal hardware requirements, does not deteriorate the performance of cDE and thus is competitive with other memory saving and population-based algorithms. An application in the field of mobile robotics highlights the usability and advantages of the proposed approach.

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References

  1. Norman PG (1987) The new AP101S general-purpose computer (GPC) for the space shuttle. IEEE Proceedings 75(3):308–319

    Article  Google Scholar 

  2. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation 13(2):398–417

    Article  Google Scholar 

  3. Zhang J, Sanderson AC (2009) JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation 13(5):945–958

    Article  Google Scholar 

  4. Handoko SD, Kwoh CK, Ong YS (2010) Feasibility structure modeling: An effective chaperon for constrained memetic algorithms. IEEE Transactions on Evolutionary Computation 14(5):740–758

    Article  Google Scholar 

  5. Prügel-Bennet A (2010) Benefits of a population: Five mechanisms that advantage population-based algorithms. IEEE Transactions on Evolutionary Computation 14(4):500–517

    Article  Google Scholar 

  6. Larrañaga P, Lozano J A. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic, 2002.

  7. Harik GR, Lobo FG, Goldberg DE (1999) The compact genetic algorithm. IEEE Transactions on Evolutionary Computation 3(4):287–297

    Article  Google Scholar 

  8. Rastegar R, Hariri A (2006) A step forward in studying the compact genetic algorithm. Evolutionary Computation 14(3):277–289

    Article  Google Scholar 

  9. Hidalgo J I, Prieto M, Lanchares J et al. Hybrid parallelization of a compact genetic algorithm. In Proc. the 11th Euromicro Conference on Parallel, Distributed and Network-Based Processing, Feb. 2003, pp.449–445.

  10. Lobo F G, Lima C F, Mártires H. An architecture for massive parallelization of the compact genetic algorithm. In Lecture Notes in Computer Science 3103, Deb K, Poli R, Banzhaf W, et al. (eds.), Springer, 2004, pp.412–413.

  11. Harik G. Linkage learning via probabilistic modeling in the ECGA. Tech. Rep. 99010, University of Illinois at Urbana-Champaign, Urbana, IL, 1999.

  12. Harik G R, Lobo F G, Sastry K. Linkage learning via probabilistic modeling in the extended compact genetic algorithm (ECGA). In Proc. Scalable Optimization via Probabilistic Modeling, 33, Pelikan M, Sastry K, Cantú-Paz E (eds.), Springer, 2006, pp.39-61.

  13. Sastry K, Goldberg D E. On extended compact genetic algorithm. Tech. Rep. 2000026, University of Illinois at Urbana-Champaign, Urbana, IL, 2000.

  14. Sastry K, Xiao G. Cluster optimization using extended compact genetic algorithm. Tech. Rep. 2001016, University of Illinois at Urbana-Champaign, Urbana, IL, 2001.

  15. Ahn CW, An J, Yoo JC (2012) Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs. Information Sciences 192(1):109–119

    Article  Google Scholar 

  16. Sastry K, Goldberg DE, Johnson DD (2007) Scalability of a hybrid extended compact genetic algorithm for ground state optimization of clusters. Materials and Manufacturing Processes 22(5):570–576

    Article  Google Scholar 

  17. Aporntewan C, Chongstitvatana P. A hardware implementation of the compact genetic algorithm. In Proc. the IEEE Congress on Evolutionary Computation, May 2001, pp.624–629.

  18. Gallagher JC, Vigraham S, Kramer G (2004) A family of compact genetic algorithms for intrinsic evolvable hardware. IEEE Transactions Evolutionary Computation 8(2):111–126

    Article  Google Scholar 

  19. Jewajinda Y, Chongstitvatana P. Cellular compact genetic algorithm for evolvable hardware. In Proc. the 5th Int. Conf. Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, May 2008, pp.1–4.

  20. Gallagher J C, Vigraham S. A modified compact genetic algorithm for the intrinsic evolution of continuous time recurrent neural networks. In Proc. the Genetic and Evolutionary Computation Conference, July 2002, pp.163–170.

  21. Ahn CW, Ramakrishna RS (2003) Elitism-based compact genetic algorithms. IEEE Transactions on Evolutionary Computation 7(4):367–385

    Article  Google Scholar 

  22. Rudolph G. A partial order approach to noisy fitness functions. In Proc. the IEEE Congress on Evolutionary Computation, May 2001, pp.318–325.

  23. Mininno E, Cupertino F, Naso D (2008) Real-valued compact genetic algorithms for embedded microcontroller optimization. IEEE Transactions on Evolutionary Computation 12(2):203–219

    Article  Google Scholar 

  24. Cupertino F, Mininno E, Naso D. Elitist compact genetic algorithms for induction motor self-tuning control. In Proc. the IEEE Congress on Evolutionary Computation, July 2006, pp.3057–3063.

  25. Cupertino F, Mininno E, Naso D. Compact genetic algorithms for the optimization of induction motor cascaded control. In Proc. the IEEE International Conference on Electric Machines and Drives, May 2007, pp.82–87.

  26. Mininno E, Neri F, Cupertino F, Naso D (2011) Compact differential evolution. IEEE Transactions on Evolutionary Computation 15(1):32–54

    Article  Google Scholar 

  27. Neri F, Tirronen V (2010) Recent advances in differential evolution: A review and experimental analysis. Artificial Intelligence Review 33(1–2):61–106

    Article  Google Scholar 

  28. Neri F, Mininno E (2010) Memetic compact differential evolution for cartesian robot control. IEEE Computational Intelligence Magazine 5(2):54–65

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  30. Iacca G, Mallipeddi R, Mininno E, Neri F, Suganthan P N. Super-fit and population size reduction in compact differential evolution. In Proc. IEEE Symposium on Memetic Computing, April 2011, pp.1–8.

  31. Iacca G, Neri F, Mininno E. Opposition-based learning in compact differential evolution. In Lecture Notes in Computer Science 6624, Di Chio C, Cagnoni S, Cotta C, et al. (eds.), Springer, 2011, pp.264–273.

  32. Caponio A, Neri F, Tirronen V (2009) Super-fit control adaptation in memetic differential evolution frameworks. Soft Computing-A Fusion of Foundations, Methodologies and Applications 13(8):811–831

    Google Scholar 

  33. Rahnamayan S, Tizhoosh HR, Salama MM (2008) Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation 12(1):64–79

    Article  Google Scholar 

  34. Iacca G, Mininno E, Neri F (2011) Composed compact differential evolution. Evolutionary Intelligence 4(1):17–29

    Article  Google Scholar 

  35. Iacca G, Mallipeddi R, Mininno E et al. Global supervision for compact differential evolution. In Proc. IEEE Symp. Differential Evolution, April 2011, pp.25–32.

  36. Mallipeddi R, Iacca G, Suganthan P N, Neri F, Mininno E. Ensemble strategies in compact differential evolution. In Proc. the IEEE Congress on Evolutionary Computation, June 2011, pp.1972–1977.

  37. Das S, Suganthan PN (2011) Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 15(1):4–31

    Article  Google Scholar 

  38. Price K V, Storn R, Lampinen J. Differential Evolution: A Practical Approach to Global Optimization, Springer, 2005.

  39. Gautschi W. Error function and fresnel integrals. In Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, Abramowitz M, Stegun I A (eds.), 1972, pp.297–309.

  40. Cody WJ (1969) Rational chebyshev approximations for the error function. Mathematics of Computation 23(107):631–637

    Article  MathSciNet  MATH  Google Scholar 

  41. Chen H, Zhu Y, Hu K. Adaptive bacterial foraging optimization. Abstract and Applied Analysis, 2011, Article ID 108269.

  42. Das S, Abraham A, Chakraborty UK et al (2009) Differential evolution with a neighborhood-based mutation operator. IEEE Trans Evolutionary Computation 13(3):526–553

    Article  Google Scholar 

  43. Auger A, Hansen N. A restart CMA evolution strategy with increasing population size. In Proc. the IEEE Congress on Evolutionary Computation, Sept. 2005, pp.1769–1776.

  44. Zhou J, Ji Z, Shen L. Simplified intelligence single particle optimization based neural network for digit recognition. In Proc. the Chinese Conference on Pattern Recognition, Oct. 2008.

  45. Zhao X (2011) Simulated annealing algorithm with adaptive neighborhood. Applied Soft Computing 11(2):1827–1836

    Article  Google Scholar 

  46. Hansen N, Auger A, Finck S, Ros R. Real-parameter black-box optimization benchmarking 2010: Experimental setup. Tech. Rep. RR-7215, INRIA, 2010.

  47. Tang K, Yao X, Suganthan P N, MacNish C, Chen Y P, Chen C M, Yang Z. Benchmark functions for the CEC’2008 special session and competition on large scale global optimization. Tech. Rep., Nature Inspired Computation and Applications Laboratory, USTC, China, 2007.

  48. Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bulletin 1(6):80–83

    Article  Google Scholar 

  49. Holm S (1979) A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6(2):65–70

    MathSciNet  MATH  Google Scholar 

  50. 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 Computing 13(10):959–977

    Article  Google Scholar 

  51. Bräuni T. Embedded Robotics: Mobile Robot Design and Applications with Embedded Systems (3rd edition), Springer, 2008.

  52. Mindstorms education, NXT User Guide, 2006, http://education.lego.com/downloads/?q=f02FB6AC1-07BO-4E1A-862D-7AE2DBC88F9Eg , Aug. 2011.

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Correspondence to Giovanni Iacca.

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Iacca, G., Caraffini, F. & Neri, F. Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead. J. Comput. Sci. Technol. 27, 1056–1076 (2012). https://doi.org/10.1007/s11390-012-1284-2

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