Skip to main content
Log in

Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms

  • Published:
Journal of Heuristics Aims and scope Submit manuscript

Abstract

This paper investigates how the policy used to select migrants and the individuals they replace affects the selection pressure in parallel evolutionary algorithms (EAs) with multiple populations. The four possible combinations of random and fitness-based emigration and replacement of existing individuals are considered. The investigation follows two approaches. The first is to calculate the takeover time under the four migration policies. This approach makes several simplifying assumptions, but the qualitative conclusions that are derived from the calculations are confirmed by the second approach. The second approach consists on quantifying the increase in the selection intensity. The selection intensity is a domain-independent adimensional quantity that can be used to compare the selection pressure of common selection methods with the pressure caused by migration. The results may help to avoid excessively high (or low) selection pressures that may cause the search to fail, and offer a plausible explanation to the frequent claims of superlinear speedups in parallel EAs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bäck, T. (1994). “Selective Pressure in Evolutionary Algorithms: A Characterization of Selection Mechanisms.” Proceedings of the First IEEE Conference on Evolutionary Computation, Vol. 1. Piscataway, NJ: IEEE Press, pp. 57–62.

    Google Scholar 

  • Bäck, T. (1995). “Generalized Convergence Models for Tournament-and (µ,λ)-Selection.” In (Eschelman, 1995), pp. 2–8.

  • Baker, J.E. (1985). “Adaptive Selection Methods for Genetic Algorithms.” In J.J. Grefenstette (ed.), Proceedings of an International Conference on Genetic Algorithms and Their Applications. Hillsdale, NJ: Lawrence Erlbaum, pp. 101–111.

  • Baker, J.E. (1987). “Reducing Bias and Ineffiency in the Selection Algorithm.” In (Grefenstette, 1987), pp. 14–21.

  • Baluja, S. (1994). “Population-Based Incremental Learning:AMethod for Integrating Genetic Search Based Function Optimization and Competitive Learning.” Tech. Rep. No. CMU-CS-94-163, Carnegie Mellon University, Pittsburgh, PA.

    Google Scholar 

  • Belding, T.C. (1995). “The Distributed Genetic Algorithm Revisited.” In (Eschelman, 1995), pp. 114–121.

  • Blickle, T. and L. Thiele. (1996). “A Comparison of Selection Schemes used in Evolutionary Algorithms.” Evolutionary Computation 4(4), 361–394.

    Google Scholar 

  • Booker, L.B. (1982). “Intelligent Behavior as an Adaptation to the Task Environment.” Unpublished Doctoral Dissertation, The University of Michigan. (University Microfilm No. 8214966).

  • Brindle, A. (1981). “Genetic Algorithms for Function Optimization.” Unpublished Doctoral Dissertation, University of Alberta, Edmonton, Canada.

    Google Scholar 

  • Burrows, P. (1972). “Expected Selection Differentials for Directional Selection.” Biometrics 23, 1091–1100.

    Google Scholar 

  • Cantú-Paz, E. (1999). “Migration Policies and Takeover Times in Parallel Genetic Algorithms.” In W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela, and R.E. Smith (eds.), GECCO-99: Proceedings of the 1999 Genetic and Evolutionary Computation Conference. San Francisco, CA: Morgan Kaufmann, p. 775.

    Google Scholar 

  • Cantú-Paz, E. and D.E. Goldberg. (2000). “Parallel Genetic Algorithms: Theory and Practice.” Computer Methods in Applied Mechanics and Engineering 186, 221–238.

    Google Scholar 

  • Eschelman, L. (ed.). (1995). Proceedings of the Sixth International Conference on Genetic Algorithms. San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  • Goldberg, D.E. and K. Deb. (1991). “A Comparative Analysis of Selection Schemes used in Genetic Algorithms.” Foundations of Genetic Algorithms. San Mateo, CA: Morgan Kaufmann 1, 69–93. (Also TCGA Report 90007).

    Google Scholar 

  • Goldberg, D.E., K. Deb, and D. Thierens. (1993). “Toward a Better Understanding of Mixing in Genetic Algorithms.” Journal of the Society of Instrument and Control Engineers 32(1), 10–16.

    Google Scholar 

  • Grefenstette, J.J. (1981). “Parallel Adaptive Algorithms for Function Optimization.” Tech. Rep. No. CS-81-19, Vanderbilt University, Computer Science Department, Nashville, TN.

    Google Scholar 

  • Grefenstette, J.J. (ed.). (1987). Proceeding of the Second International Conference on Genetic Algorithms. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Grosso, P.B. (1985). “Computer Simulations of Genetic Adaptation: Parallel Subcomponent Interaction in a Multilocus Model.” Unpublished Doctoral Dissertation, The University of Michigan. (University Microfilms No. 8520908).

  • Hancock, P.J.B. (1997). “Selection: A Comparison of Selection Mechanisms.” In T. Bäck, D.B. Fogel, and Z. Michalewicz (eds.), Handbook of Evolutionary Computation. Bristol and New York: Institute of Physics Publishing and Oxford University Press, pp. C2.8:1–C2.8:11.

    Google Scholar 

  • Harik, G.R., F.G. Lobo, and D.E. Goldberg. (1998). “The Compact Genetic Algorithm.” In I. of Electrical IEEE Press, Proceedings of 1998 IEEE International Conference on Evolutionary Computation. Piscataway, NJ, pp. 523–528.

  • Harter, H.L. (1970). Order Statistics and Their Use in Testing and Estimation.Washington, D.C.: U.S. Government Printing Office.

    Google Scholar 

  • Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.

    Google Scholar 

  • Lin, S.-C., E.D. Goodman, and W.F. Punch III. (1997). “Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problems.” In P.J. Angeline, R.G. Reynolds, J.R. McDonnell, and R. Eberhart (eds.), Evolutionary Programming VI. Berlin: Springer-Verlag, pp. 383–393.

    Google Scholar 

  • Miller, B.L. and D.E. Goldberg. (1995). “Genetic Algorithms, Tournament Selection, and the Effects of Noise.” Complex Systems 9(3), 193–212.

    Google Scholar 

  • Miller, B.L. and D.E. Goldberg. (1996). “Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise.” Evolutionary Computation 4(2), 113–131.

    Google Scholar 

  • Mühlenbein, H. (1991). “Evolution in Time and Space—The Parallel Genetic Algorithm.” In G.J.E. Rawlins (ed.), Foundations of Genetic Algorithms. San Mateo, CA: Morgan Kaufmann, pp. 316–337.

    Google Scholar 

  • Mühlenbein, H. and G. Paaβ. (1996). “From Recombination of Genes to the Estimation of Distributions I. Binary Parameters.” In (Voigt et al., 1996), pp. 178–187.

  • Mühlenbein, H. and D. Schlierkamp-Voosen. (1993). “Predictive Models for the Breeder Genetic Algorithm: I. Continuous Parameter Optimization.” Evolutionary Computation 1(1), 25–49.

    Google Scholar 

  • Punch, W.F. (1998). “How Effective are Multiple Programs in Genetic Programming.” In J.R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D.B. Fogel, M.H. Garzon, D.E. Goldberg, H. Iba, and R.L. Riolo (eds.), Genetic Programming 98. San Francisco: Morgan Kaufmann, pp. 308–313.

    Google Scholar 

  • Sarma, J. and K. De Jong. (1996). “An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms.” In (Voigt et al., 1996), pp. 236–244.

  • Sarma, J. and K. De Jong. (1997). “An Analysis of Local Selection Algorithms in a Spatially Structured Evolutionary Algorithm.” In T. Bäck (ed.), Proceedings of the Seventh International Conference on Genetic Algorithms. San Francisco, Morgan Kaufmann, pp. 181–187.

    Google Scholar 

  • Schaffer, J.D. (ed.). (1989). Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  • Schwefel, H. (1981). Numerical Optimization of Computer Models. Chichester: John Wiley and Sons.

    Google Scholar 

  • Syswerda, G. (1989). “Uniform Crossover in Genetic Algorithms.” In (Schaffer, 1989), pp. 2–9.

  • Tanese, R. (1987). “Parallel Genetic Algorithm for a Hypercube.” In (Grefenstette, 1987), pp. 177–183.

  • Tanese, R. (1989). “Distributed Genetic Algorithms.” In (Schaffer, 1989), pp. 434–439.

  • Thierens, D. and D.E. Goldberg. (1993). “Mixing in Genetic Algorithms.” In S. Forrest (ed.), Proceedings of the Fifth International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann, pp. 38–45.

    Google Scholar 

  • Thierens, D. and D.E. Goldberg. (1994). “Convergence Models of Genetic Algorithm Selection Schemes.” In Y. Davidor, H.-P. Schwefel, and R. Männer (eds.), Parallel Problem Solving from Nature, PPSN III. Berlin: Springer-Verlag, pp. 119–129.

    Google Scholar 

  • Voigt, H.-M., W. Ebeling, I. Rechenberg, and H.-P. Schwefel (eds.). (1996). Parallel Problem Solving from Nature, PPSN IV. Berlin: Springer-Verlag.

    Google Scholar 

  • Whitley, D. (1993). “An Executable Model of a Simple Genetic Algorithm.” In L.D. Whitley (ed.), Foundations of Genetic Algorithms 2. San Mateo, CA: Morgan Kaufmann, pp. 45–62.

    Google Scholar 

  • Whitley, D., S. Rana, and R.B. Heckendorn. (1999). “Exploiting Separability in Search: The Island Model Genetic Algorithm.” Journal of Computing and Information Technology 7(1), 33–47.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cantú-Paz, E. Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms. Journal of Heuristics 7, 311–334 (2001). https://doi.org/10.1023/A:1011375326814

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1011375326814

Navigation