Compact Optimization

  • Ferrante Neri
  • Giovanni Iacca
  • Ernesto Mininno
Part of the Intelligent Systems Reference Library book series (ISRL, volume 38)


Compact algorithms are optimization algorithms belonging to the class of Estimation of Distribution Algorithms (EDAs). Compact algorithms employ the search logic of population-based algorithms but do not store and process an entire population and all the individuals therein, but on the contrary make use of a probabilistic representation of the population in order to perform the optimization process. This probabilistic representation simulates the population behaviour as it extensively explores the decision space at the beginning of the optimization process and progressively focuses the search on the most promising genotypes and narrows the search radius. In this way, a much smaller amount of parameters must be stored in the memory. Thus, a run of these algorithms requires much more limited memory devices compared to their corresponding standard population-based algorithms. This class of algorithms is especially useful for those applications characterized by a limited hardware, e.g. mobile systems, industrial robots, etc. This chapter illustrates the history of compact optimization by giving a description of the main paradigms proposed in literature and a novel interpretation of the subject as well as a design procedure. An application to space robotics is given in order to show the applicability of compact algorithms.


Differential Evolution Probability Vector Memetic Algorithm Space Robot Evolution Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Ahn, C.W., Ramakrishna, R.S.: Elitism based compact genetic algorithms. IEEE Transactions on Evolutionary Computation 7(4), 367–385 (2003)CrossRefGoogle Scholar
  2. 2.
    Aporntewan, C., Chongstitvatana, P.: A hardware implementation of the compact genetic algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 624–629 (2001)Google Scholar
  3. 3.
    Baraglia, R., Hidalgo, J.I., Perego, R.: A hybrid heuristic for the traveling salesman problem. IEEE Transactions on Evolutionary Computation 5(6), 613–622 (2001)CrossRefGoogle Scholar
  4. 4.
    Brest, J., Greiner, S., Bošković, B., Mernik, M., Žumer, V.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 10(6), 646–657 (2006)CrossRefGoogle Scholar
  5. 5.
    Caponio, A., Cascella, G.L., Neri, F., Salvatore, N., Sumner, M.: A fast adaptive memetic algorithm for on-line and off-line control design of PMSM drives. IEEE Transactions on System Man and Cybernetics-part B 37(1), 28–41 (2007)CrossRefGoogle Scholar
  6. 6.
    Caponio, A., Neri, F., Tirronen, V.: Super-fit control adaptation in memetic differential evolution frameworks. Soft Computing-A Fusion of Foundations, Methodologies and Applications 13(8), 811–831 (2009)Google Scholar
  7. 7.
    Cody, W.J.: Rational Chebyshev Approximations for the Error Function 23(107), 631–637 (1969)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Cupertino, F., Mininno, E., Naso, D.: Elitist compact genetic algorithms for induction motor self-tuning control. In: Proceedings of the IEEE Congress on Evolutionary Computation (2006)Google Scholar
  9. 9.
    Cupertino, F., Mininno, E., Naso, D.: Compact genetic algorithms for the optimization of induction motor cascaded control. In: Proceedings of the IEEE International Conference on Electric Machines and Drives, vol. 1, pp. 82–87 (2007)Google Scholar
  10. 10.
    Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation (2011) (to appear)Google Scholar
  11. 11.
    Dasgupta, S., Das, S., Biswas, A., Abraham, A.: On stability and convergence of the population-dynamics in differential evolution. AI Communications - The European Journal on Artificial Intelligence 22(1), 1–20 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computation. Springer, Berlin (2003)Google Scholar
  13. 13.
    Fan, H.Y., Lampinen, J.: A trigonometric mutation operation to differential evolution. Journal of Global Optimization 27(1), 105–129 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Fossati, L., Lanzi, P.L., Sastry, K., Goldberg, D.E.: A simple real-coded extended compact genetic algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 342–348 (2007)Google Scholar
  15. 15.
    Gallagher, J.C., Vigraham, S.: A modified compact genetic algorithm for the intrinsic evolution of continuous time recurrent neural networks. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 163–170 (2002)Google Scholar
  16. 16.
    Gallagher, J.C., Vigraham, S., Kramer, G.: A family of compact genetic algorithms for intrinsic evolvable hardware. IEEE Transactions Evolutionary Computation 8(2), 111–126 (2004)CrossRefGoogle Scholar
  17. 17.
    Gautschi, W.: Error function and fresnel integrals. In: Abramowitz, M., Stegun, I.A. (eds.) Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, ch. 7, pp. 297–309 (1972)Google Scholar
  18. 18.
    Harik, G.: Linkage learning via probabilistic modeling in the ECGA. Tech. Rep. 99010, University of Illinois at Urbana-Champaign, Urbana, IL (1999)Google Scholar
  19. 19.
    Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE Transactions on Evolutionary Computation 3(4), 287–297 (1999)CrossRefGoogle Scholar
  20. 20.
    Harik, G.R., Lobo, F.G., Sastry, K.: Linkage learning via probabilistic modeling in the extended compact genetic algorithm (ECGA). In: Pelikan, M., Sastry, K., Cantú-Paz, E. (eds.) Scalable Optimization via Probabilistic Modeling. SCI, vol. 33, pp. 39–61. Springer (2006)Google Scholar
  21. 21.
    Hart, W.E., Krasnogor, N., Smith, J.E.: Memetic evolutionary algorithms. In: Hart, W.E., Krasnogor, N., Smith, J.E. (eds.) Recent Advances in Memetic Algorithms, pp. 3–27. Springer, Berlin (2004)Google Scholar
  22. 22.
    Huang, P., Chen, K., Xu, S.: Optimal path planning for minimizing disturbance of space robot. In: Proceedings of the IEEE International Conference on on Control, Automation, Robotics, and Vision (2006)Google Scholar
  23. 23.
    Iacca, G., Mallipeddi, R., Mininno, E., Neri, F., Suganthan, P.N.: Global supervision for compact differential evolution. In: Proceedings IEEE Symposium on Differential Evolution, pp. 25–32 (2011a)Google Scholar
  24. 24.
    Iacca, G., Mallipeddi, R., Mininno, E., Neri, F., Suganthan, P.N.: Super-fit and population size reduction mechanisms in compact differential evolution. In: Proceedings of IEEE Symposium on Memetic Computing, pp. 21–28 (2011b)Google Scholar
  25. 25.
    Iacca, G., Mininno, E., Neri, F.: Composed compact differential evolution. Evolutionary Intelligence 4(1), 17–29 (2011c)CrossRefGoogle Scholar
  26. 26.
    Iacca, G., Neri, F., Mininno, E.: Opposition-Based Learning in Compact Differential Evolution. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcázar, A.I., Merelo, J.J., Neri, F., Preuss, M., Richter, H., Togelius, J., Yannakakis, G.N. (eds.) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 264–273. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  27. 27.
    Ishibuchi, H., Yoshida, T., Murata, T.: Balance between genetic search and local search in memetic algorithms for multiobjective permutation flow shop scheduling. IEEE Transactions on Evolutionary Computation 7, 204–223 (2003)CrossRefGoogle Scholar
  28. 28.
    Ishibuchi, H., Hitotsuyanagi, Y., Nojima, Y.: An empirical study on the specification of the local search application probability in multiobjective memetic algorithms. In: Proc. of the IEEE Congress on Evolutionary Computation, pp. 2788–2795 (2007)Google Scholar
  29. 29.
    Jewajinda, Y., Chongstitvatana, P.: Cellular compact genetic algorithm for evolvable hardware. In: Proceedings of the International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, vol. 1, pp. 1–4 (2008)Google Scholar
  30. 30.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  31. 31.
    Krasnogor, N.: Toward robust memetic algorithms. In: Hart, W.E., Krasnogor, N., Smith, J.E. (eds.) Recent Advances in Memetic Algorithms. STUDFUZZ, pp. 185–207. Springer, Berlin (2004)Google Scholar
  32. 32.
    Lanzi, P., Nichetti, L., Sastry, K., Goldberg, D.E.: Real-coded extended compact genetic algorithm based on mixtures of models. In: Linkage in Evolutionary Computation. SCI, vol. 157, pp. 335–358. Springer (2008)Google Scholar
  33. 33.
    Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer (2001)Google Scholar
  34. 34.
    Mallipeddi, R., Iacca, G., Suganthan, P.N., Neri, F., Mininno, E.: Ensemble strategies in compact differential evolution. In: Proceedings of the IEEE Congress on Evolutionary Computation (2011)Google Scholar
  35. 35.
    Mininno, E., Cupertino, F., Naso, D.: Real-valued compact genetic algorithms for embedded microcontroller optimization. IEEE Transactions on Evolutionary Computation 12(2), 203–219 (2008)CrossRefGoogle Scholar
  36. 36.
    Mininno, E., Neri, F., Cupertino, F., Naso, D.: Compact differential evolution. IEEE Transactions on Evolutionary Computation 15(1), 32–54 (2011)CrossRefGoogle Scholar
  37. 37.
    Neri, F., Mininno, E.: Memetic compact differential evolution for cartesian robot control. IEEE Computational Intelligence Magazine 5(2), 54–65 (2010)CrossRefGoogle Scholar
  38. 38.
    Neri, F., Tirronen, V.: Recent advances in differential evolution: A review and experimental analysis. Artificial Intelligence Review 33(1–2), 61–106 (2010)CrossRefGoogle Scholar
  39. 39.
    Neri, F., Toivanen, J., Cascella, G.L., Ong, Y.S.: An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(2), 264–278 (2007)CrossRefGoogle Scholar
  40. 40.
    Neri, F., del Toro Garcia, X., Cascella, G.L., Salvatore, N.: Surrogate assisted local search on PMSM drive design. COMPEL: International Journal for Computation and Mathematics in Electrical and Electronic Engineering 27(3), 573–592 (2008)zbMATHCrossRefGoogle Scholar
  41. 41.
    Neri, F., Mininno, E., Kärkkäinen, T.: Noise Analysis Compact Genetic Algorithm. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010. LNCS, vol. 6024, pp. 602–611. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  42. 42.
    Neri, F., Iacca, G., Mininno, E.: Disturbed exploitation compact differential evolution for limited memory optimization problems. Information Sciences 181(12), 2469–2487 (2011)MathSciNetCrossRefGoogle Scholar
  43. 43.
    Norman, P.G.: The new AP101S general-purpose computer (gpc) for the space shuttle. IEEE Proceedings 75, 308–319 (1987)CrossRefGoogle Scholar
  44. 44.
    Ong, Y.S., Lim, M.H., Chen, X.: Memetic computation-past, present and future. IEEE Computational Intelligence Magazine 5(2), 24–31 (2010)CrossRefGoogle Scholar
  45. 45.
    Parsopoulos, K.E.: Cooperative micro-differential evolution for high-dimensional problems. In: Proceedings of the Conference on Genetic and Evolutionary Computation, pp. 531–538 (2009)Google Scholar
  46. 46.
    Price, K.V., Storn, R., Lampinen, J.: Differential Evolution: A Practical Approach to Global Optimization. Springer (2005)Google Scholar
  47. 47.
    Prügel-Bennett, A.: Benefits of a population: Five mechanisms that advantage population-based algorithms. IEEE Transactions on Evolutionary Computation 14(4), 500–517 (2010)CrossRefGoogle Scholar
  48. 48.
    Rastegar, R., Hariri, A.: A step forward in studying the compact genetic algorithm. Evolutionary Computation 14(3), 277–289 (2006)CrossRefGoogle Scholar
  49. 49.
    Ren, K., Fu, J.Z., Chen, Z.C.: A new linear interpolation method with lookahead for high speed machining. In: Technology and Innovation Conference, pp. 1056–1059 (2006)Google Scholar
  50. 50.
    Rudolph, G.: Self-adaptive mutations lead to premature convergence. IEEE Transactions on Evolutionary Computation 5(4), 410–414 (2001)CrossRefGoogle Scholar
  51. 51.
    Sastry, K., Goldberg, D.E.: On extended compact gentic algorithm. Tech. Rep. 2000026, University of Illinois at Urbana-Champaign, Urbana, IL (2000)Google Scholar
  52. 52.
    Sastry, K., Xiao, G.: Cluster optimization using extended compact genetic algorithm. Tech. Rep. 2001016, University of Illinois at Urbana-Champaign, Urbana, IL (2001)Google Scholar
  53. 53.
    Sastry, K., Goldberg, D.E., Johnson, D.D.: Scalability of a hybrid extended compact genetic algorithm for ground state optimization of clusters. Materials and Manufacturing Processes 22(5), 570–576 (2007)CrossRefGoogle Scholar
  54. 54.
    Tan, K., Chiam, S., Mamun, A., Goh, C.: Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization. European Journal of Operational Research 197, 701–713 (2009)zbMATHCrossRefGoogle Scholar
  55. 55.
    Tasoulis, D.K., Pavlidis, N.G., Plagianakos, V.P., Vrahatis, M.N.: Parallel differential evolution. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2023–2029 (2004)Google Scholar
  56. 56.
    Weber, M., Tirronen, V., Neri, F.: Scale factor inheritance mechanism in distributed differential evolution. Soft Computing - A Fusion of Foundations, Methodologies and Applications 14(11), 1187–1207 (2010)Google Scholar
  57. 57.
    Xu, Y.: The measure of dynamic coupling of space robot system. In: Proceedings of the IEEE Conference on Robotics and Automation, pp. 615–620 (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ferrante Neri
    • 1
  • Giovanni Iacca
    • 1
  • Ernesto Mininno
    • 1
  1. 1.Department of Mathematical Information TechnologyUniversity of JyväskyläAgoraFinland

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