Skip to main content
SpringerLink
Log in

Self-healing strategies for memetic algorithms in unstable and ephemeral computational environments

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

Optimization algorithms deployed on unstable computational environments must be resilient to the volatility of computing nodes. Different fault-tolerance mechanisms have been proposed for this purpose. We focus on the use of island-based multimemetic algorithms, namely memetic algorithms which explicitly represent and evolve memes alongside solutions, endowed with self-scaling capabilities. These strategies dynamically resize populations in order to react to system fluctuations. In this context, we study the joint use of different self-healing strategies, aimed to compensating the harm that the loss of computing nodes produces. Firstly, we consider the use of probabilistic models in order to self-sample the current population when it has to be resized, thus minimizing distortions in the convergence of the population and the progress of the search. Then, we complement the previous approach with the use of rewiring strategies intended to keep a rich connectivity in the system along time. We perform an extensive empirical assessment of those strategies on three different problems, considering a simulated computational environment featuring diverse degrees of instability. It is shown that these self-healing strategies provide a performance improvement and interact synergistically with each other, in particular in scenarios with large volatility.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Alba E (2002) Parallel evolutionary algorithms can achieve super-linear performance. Inf Process Lett 82(1):7–13

    Article  MathSciNet  MATH  Google Scholar 

  • Alba E (2005) Parallel metaheuristics: a new class of algorithms. Wiley, New Jersey

    Book  MATH  Google Scholar 

  • Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47–97

    Article  MathSciNet  MATH  Google Scholar 

  • Babaoglu O, Jelasity M, Montresor A, Fetzer C, Leonardi S, van Moorsel A, van Steen M (eds) (2005) Self-star properties in complex information systems, vol 3460, Lecture notes in computer science. Springer, Berlin

  • Baluja S, Davies S (1997) Using optimal dependency-trees for combinatorial optimization: learning the structure of the search space. In: 14th international conference on machine learning. Morgan Kaufmann Publishers, Burlington, pp 30–38

  • Barabási AL (2016) Network science. Cambridge University Press, Cambridge. http://barabasi.com/networksciencebook/ under CC-BY-NC-SA 2.0. Accessed 26 Nov 2015

  • Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    Article  MathSciNet  MATH  Google Scholar 

  • Berns A, Ghosh S (2009) Dissecting self-\(\star\) properties. In: Third IEEE international conference on self-adaptive and self-organizing systems—SASO, San Francisco, 2009. IEEE Press, pp 10–19

  • Bonet JSD, Isbell CL Jr, Viola P (1996) Mimic: finding optima by estimating probability densities. In: Mozer M, Jordan M, Petsche T (eds) Advances in neural information processing systems, vol 9. MIT Press, Cambridge, pp 424–430

  • Cotta C, Fernández-Leiva AJ, Fernández de Vega F, Chávez F, Merelo JJ, Castillo PA, Bello G, Camacho D (2015) Ephemeral computing and bioinspired optimization, challenges and opportunities. In: 7th international joint conference on evolutionary computation theory and applications. Lisboa, Portugal, pp 319–324

  • Deb K, Goldberg DE (1993) Analyzing deception in trap functions. In: Whitley LD (ed) Second workshop on foundations of genetic algorithms., Morgan Kaufmann Publishers, Vail, pp 93–108

  • Gagné C, Parizeau M, Dubreuil M (2013) Distributed beagle: an environment for parallel and distributed evolutionary computations. In: 17th annual international symposium on high performance computing systems and applications—HPCS 2003, Sherbrooke, Quebec, pp 201–208

  • García Arenas M, Collet P, Eiben AE, Jelasity M, Merelo Guervós JJ, Paechter B, Preuß M, Schoenauer M (2002) A framework for distributed evolutionary algorithms. In: Merelo Guervós JJ et al (eds) Parallel problem solving from nature—PPSN VII, vol 2439. Lecture notes in computer science. Springer, Berlin, pp 665–675

  • Goldberg DE, Deb K, Horn J (1992) Massive multimodality, deception and genetic algorithms. In: Männer R, Manderick B (eds) Parallel problem solving from nature—PPSN II. Elsevier Science Inc, New York, pp 37–48

  • Hidalgo JI, Lanchares J, de Fernández Vega F (2007) Is the island model fault tolerant? In: Thierens D et al (eds) Genetic and evolutionary computation—GECCO 2007. ACM Press, New York, pp 2737–2744

  • Jelasity M, van Steen M (2002) Large-scale newscast computing on the internet. Technical report IR-503, Vrije Universiteit Amsterdam, Department of Computer Science, Amsterdam

  • Krasnogor N, Blackburne B, Burke E, Hirst J (2002) Multimeme algorithms for protein structure prediction. In: Merelo Guervós JJ et al (eds) Parallel problem solving from nature—PPSN VII, vol 2439. Lecture notes in computer science. Springer, Berlin, pp 769–778

  • Krasnogor N, Gustafson S (2004) A study on the use of “self-generation” in memetic algorithms. Nat Comput 3(1):53–76

    Article  MathSciNet  MATH  Google Scholar 

  • Laredo JLJ, Bouvry P, González DL, Fernández de Vega F, Arenas MG, Merelo JJ, Fernandes CM (2014) Designing robust volunteer-based evolutionary algorithms. Genet Program Evol Mach 15(3):221–244

    Article  Google Scholar 

  • Laredo JLJ, Castillo PA, Mora AM, Merelo JJ, Fernandes C (2008) Resilience to churn of a peer-to-peer evolutionary algorithm. Int J High Perform Syst Archit 1(4):260–268

    Article  Google Scholar 

  • Lee ET, Wang JW (eds) (2003) Statistical methods for survival data analysis. Wiley, Hoboken

    MATH  Google Scholar 

  • Liu C, White RW, Dumais S (2010) Understanding web browsing behaviors through weibull analysis of dwell time. In: 33rd international ACM SIGIR conference on research and development in information retrieval—SIGIR 2010, pp 379–386. ACM Press, New York

  • Lombraña González D, Fernández de Vega F, Casanova H (2010a) Characterizing fault tolerance in genetic programming. Future Gener Comput Syst 26(6):847–856

    Article  Google Scholar 

  • Lombraña González D, Jiménez Laredo JL, Fernández de Vega F, Merelo Guervós JJ (2010b) Characterizing fault-tolerance of genetic algorithms in desktop grid systems. In: Cowling P, Merz P (eds) Evolutionary computation in combinatorial optimization, vol 6022. Lecture notes in computer science. Springer, Berlin, pp 131–142

  • Lombraña González D, Jiménez Laredo JL, Fernández de Vega F, Merelo Guervós JJ (2012) Characterizing fault-tolerance in evolutionary algorithms. In: Fernández de Vega F et al (eds) Parallel architectures and bioinspired algorithms, vol 415. Studies in computational intelligence. Springer, Berlin, pp 77–99

  • Lozano JA, Larrañaga P, Inza I, Bengoetxea E (eds) (2006) Towards a new evolutionary computation, vol 192., Studies in fuzziness and soft computingSpringer, Berlin

    Google Scholar 

  • Melab N, Cahon S, Talbi EG (2006) Grid computing for parallel bioinspired algorithms. J Parallel Distrib Comput 66(8):1052–1061

    Article  MATH  Google Scholar 

  • Milojičić DS, Kalogeraki V, Lukose R, Nagaraja K, Pruyne J, Richard B, Rollins S, Xu Z (2002) Peer-to-peer computing. Technical report HPL-2002-57, Hewlett-Packard Labs

  • Mühlenbein H, Paaß G (1996) From recombination of genes to the estimation of distributions I. Binary parameters. In: Voigt HM, Ebeling W, Rechenberg I, Schwefel HP (eds) Parallel Problem Solving from Nature - PPSN IV, vol 1141., Lecture Notes in Computer ScienceSpringer-Verlag, Berlin Heidelberg, pp 178–187

  • Neri F, Cotta C, Moscato P (eds) (2012) Handbook of memetic algorithms, vol 379., Studies in computational intelligenceSpringer, Berlin

    Google Scholar 

  • Nogueras R, Cotta C (2014) An analysis of migration strategies in island-based multimemetic algorithms. In: Bartz-Beielstein T et al (eds) Parallel problem solving from nature—PPSN XIII, vol 8672. Lecture notes in computer science. Springer, Berlin, pp 731–740

  • Nogueras R, Cotta C (2014) On meme self-adaptation in spatially-structured multimemetic algorithms. In: I Dimov, S Fidanova, I Lirkov (eds) Numerical methods and applications. In: 8th international conference, vol 8962. Lecture notes in computer science. Springer, Berlin, pp 70–77

  • Nogueras R, Cotta C (2015a) Self-balancing multimemetic algorithms in dynamic scale-free networks. In: Mora AM, Squillero G (eds) Applications of evolutionary computing, vol 9028. Lecture notes in computer science. Springer, Berlin, pp 177–188

  • Nogueras R, Cotta C (2015b) Self-sampling strategies for multimemetic algorithms in unstable computational environments. In: Ferrández Vicente JM et al (eds) Bioinspired computation in artificial systems, vol 9108. Lecture notes in computer science. Springer, Berlin, pp 69–78

  • Nogueras R, Cotta C (2015c) Sensitivity analysis of checkpointing strategies for multimemetic algorithms on dynamic complex networks. In: 10th international conference on large scale scientific computations, vol 9374. Lecture notes in computer science. Springer, Berlin, pp 233–240

  • Nogueras R, Cotta C (2015d) Studying fault-tolerance in island-based evolutionary and multimemetic algorithms. J Grid Comput 13(3):351–374

    Article  Google Scholar 

  • Nogueras R, Cotta C (2016) Studying self-balancing strategies in island-based multimemetic algorithms. J Comput Appl Math 293:180–191

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  • Sarmenta LFG (1998) Bayanihan: web-based volunteer computing using java. In: Masunaga Y, Katayama T, Tsukamoto M (eds) Worldwide computing and its applications—WWCA 1998, vol 1368. Lecture notes in computer science. Springer, Berlin, pp 444–461

  • Smith JE (2008) Self-adaptation in evolutionary algorithms for combinatorial optimisation. In: Cotta C, Sevaux M, Sörensen K (eds) Adaptive and multilevel metaheuristics, vol 136., Studies in computational intelligenceSpringer, Berlin, pp 31–57

    Chapter  Google Scholar 

  • Stutzbach D, Rejaie R (2006) Understanding churn in peer-to-peer networks. In: 6th ACM SIGCOMM conference on internet measurement—IMC 2006. ACM Press, New York, pp 189–202

  • Tanese R (1989) Distributed genetic algorithms. In: 3rd international conference on genetic algorithms. Morgan Kaufmann Publishers, San Francisco, pp 434–439

  • Watson RA, Hornby GS, Pollack JB (1998) Modeling building-block interdependency. In: Eiben AE et al (eds) Parallel Problem solving from nature—PPSN V, vol 1498. Lecture notes in computer science. Springer, Berlin, pp 97–106

Download references

Author information

Authors and Affiliations

  1. Dept. Lenguajes y Ciencias de la Computación, Universidad de Málaga, ETSI Informática, Campus de Teatinos, 29071, Málaga, Spain

    Rafael Nogueras & Carlos Cotta

Authors
  1. Rafael Nogueras
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos Cotta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlos Cotta.

Additional information

This work is an extended version of Nogueras and Cotta (2015). We acknowledge support from Spanish Ministry of Economy and Competitiveness and European Regional Development Fund (FEDER) under project EphemeCH (TIN2014-56494-C4-1-P), from Junta de Andalucía under project DNEMESIS (P10-TIC-6083) and from Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nogueras, R., Cotta, C. Self-healing strategies for memetic algorithms in unstable and ephemeral computational environments. Nat Comput 16, 189–200 (2017). https://doi.org/10.1007/s11047-016-9560-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-016-9560-7

Keywords

Search

Navigation