Skip to main content

Memetic Algorithms

  • Reference work entry
Handbook of Natural Computing

Abstract

Memetic algorithms (MA) has become one of the key methodologies behind solvers that are capable of tackling very large, real-world, optimization problems. They are being actively investigated in research institutions as well as broadly applied in industry. This chapter provides a pragmatic guide on the key design issues underpinning memetic algorithms (MA) engineering. It begins with a brief contextual introduction to memetic algorithms and then moves on to define a pattern language for MAs. For each pattern, an associated design issue is tackled and illustrated with examples from the literature. The last section of this chapter “fast forwards” to the future and mentions what, in our mind, are the key challenges that scientists and practitioners will need to face if memetic algorithms are to remain a relevant technology in the next 20 years.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 999.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 1,199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. IEEE Trans Evolut Comput 6:443–462

    Article  Google Scholar 

  • Alexander C, Ishikawa S, Silverstein M, Jacobson M, Fiksdahl-King I, Angel S (1977) A pattern language - towns, buildings, construction. Oxford University Press, New York

    Google Scholar 

  • Armour P (2007) The conservation of uncertainty, exploring different units for measuring software. Commun ACM 50:25–28

    Article  Google Scholar 

  • Bacardit J, Krasnogor N (2009) Performance and efficiency of memetic Pittsburgh learning classifier systems. Evolut Comput 17(3)

    Google Scholar 

  • Bader-El-Den M, Poli R (2008) Evolving heuristics with genetic programming. In: GECCO ’08: Proceedings of the 10th annual conference on genetic and evolutionary computation, ACM, New York, pp 601–602, doi: http://doi.acm.org/10.1145/1389095.1389212

  • Bader-El-Din MB, Poli R (2007) Generating SAT local-search heuristics using a GP hyper-heuristic framework. In: LNCS 4926. Proceedings of the 8th international conference on artifcial evolution, Honolulu, pp 37–49

    Google Scholar 

  • Berrut JP, Trefethen L (2004) Barycentric Lagrange interpolation. SIAM Rev 46(3):501–517

    Article  MathSciNet  MATH  Google Scholar 

  • Bhattacharya M (2007) Surrogate based EA for expensive optimization problems. In: Proceedings for the IEEE congress on evolutionary computation (CEC), Singapore, pp 3847–3854

    Google Scholar 

  • Blackmore S (1999) The meme machine. Oxford University Press, Oxford

    Google Scholar 

  • Branke J (1998) Creating robust solutions by means of an evolutionary algorithm. In: Parallel problem solving from nature PPSN V, Amsterdam, pp 119–128

    Google Scholar 

  • Branke J (2001) Reducing the sampling variance when searching for robust solutions. In: Spector L, et al. (eds) Proceedings of the genetic and evolutionary computation conference, Kluwer, San Francisco, CA, pp 235–242

    Google Scholar 

  • Branke J (2002) Evolutionary Optimization in Dynamic Environments. Kluwer, Boston, MA

    Book  MATH  Google Scholar 

  • Bull L (1999) On model-based evolutionary computation. J Soft Comput Fus Found Methodol Appl 3:76–82

    Google Scholar 

  • Bull L, Holland O, Blackmore S (2000) On meme–gene coevolution. Artif Life 6:227–235

    Article  Google Scholar 

  • Burke E, Landa-Silva J (2004) The design of memetic algorithms for scheduling and timetabling problems. In: Hart W, Krasnogor N, Smith J (eds) Recent advances in memetic algorithms. Springer, pp 289–312

    Google Scholar 

  • Burke E, Newall J, Weare R (1996) A memetic algorithm for university exam timetabling. In: Burke E, Ross P (eds) The practice and theory of automated timetabling, Lecture notes in computer science, vol 1153. Springer, Berlin, pp 241–250

    Google Scholar 

  • Burke E, Newall J, Weare R (1998) Initialization strategies and diversity in evolutionary timetabling. Evolut Comput 6:81–103

    Article  Google Scholar 

  • Burke E, Bykov Y, Newall J, Petrovic S (2004) A time-predefined local search approach to exam timetabling problems. IIE Trans 36:509–528

    Article  Google Scholar 

  • Burke E, Gustafson S, Kendall G, Krasnogor N (2002) Advanced population diversity measures in genetic programming. In: Guervos JM, Adamidis P, Beyer H, Fernandez-Villacanas J, Schwefel H (eds) 7th International conference parallel problem solving from nature, PPSN, Springer, Granada, Spain, Lecture notes in computer science, vol 2439. Springer, New York, pp 341–350

    Google Scholar 

  • Burke E, Gustafson S, Kendall G, Krasnogor N (2003) Is increased diversity beneficial in genetic programming: an analysis of the effects on fitness. In: IEEE congress on evolutionary computation, CEC, IEEE, Canberra, pp 1398–1405

    Google Scholar 

  • Burke E, Hyde M, Kendall G (2006) Evolving bin packing heuristics with genetic programming. In: Runarsson T, Beyer HG, Burke E, Merelo-Guervos J, Whitley D, Yao X (eds) Proceedings of the 9th International conference on parallel problem solving from nature (PPSN 2006), LNCS 4193. Springer, pp 860–869

    Google Scholar 

  • Burke E, Hyde M, Kendall G, Woodward J (2007a) Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one. In: Proceedings of the genetic and evolutionary computation conference (GECCO 2007), ACM, London, pp 1559–1565

    Google Scholar 

  • Burke E, Hyde M, Kendall G, Woodward J (2007b) Scalability of evolved on line bin packing heuristics. In: Proceedings of the congress on evolutionary computation (CEC 2007). Singapore, pp 2530–2537

    Google Scholar 

  • Burke E, McCollum B, Meisels A, Petrovic S, Qu R (2007c) A graph-based hyper-heuristic for timetabling problems. Eur J Oper Res 176:177–192

    Article  MathSciNet  MATH  Google Scholar 

  • Caponio A, Cascella G, 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 37:28–41

    Article  Google Scholar 

  • Carr R, Hart W, Krasnogor N, Burke E, Hirst J, Smith J (2002) Alignment of protein structures with a memetic evolutionary algorithm. In: Langdon W, Cantu-Paz E, Mathias K, Roy R, Davis D, Poli R, Balakrishnan K, Honavar V, Rudolph G, Wegener J, Bull L, Potter M, Schultz A, Miller J, Burke E, Jonoska N (eds) GECCO-2002: Proceedings of the genetic and evolutionary computation conference, Morgan Kaufmann, San Mateo, CA

    Google Scholar 

  • Cavalli-Sforza L, Feldman M (1981) Cultural transmission and evolution: a quantitative approach. Princeton University Press, Princeton, NJ

    Google Scholar 

  • Cheng R, Gen M (1997) Parallel machine scheduling problems using memetic algorithms. Comput Ind Eng 33(3–4):761–764

    Article  Google Scholar 

  • Cloak F (1975) Is a cultural ethology possible. Hum Ecol 3:161–182

    Article  Google Scholar 

  • Cooper J (2000) Java design patterns: a tutorial. Addison-Wesley, Boston, MA

    Google Scholar 

  • Cordon O, Herrera F, Stutzle T (2002) A review on the ant colony optimization metaheuristic: basis, models and new trends. Mathware Soft Comput 9:141–175

    MathSciNet  MATH  Google Scholar 

  • Cutello V, Krasnogor N, Nicosia G, Pavone M (2007) Immune algorithm versus differential evolution: a comparative case study using high dimensional function optimization. In: International conference on adaptive and natural computing algorithms, ICANNGA 2007. LNCS, Springer, Berlin, pp 93–101

    Chapter  Google Scholar 

  • Dawkins R (1976) The selfish gene. Oxford University Press, New York

    Google Scholar 

  • Dawkins R (1982) The extended phenotype. Freeman, Oxford

    Google Scholar 

  • Dorigo M, Gambardela L (1997) Ant colony system: a cooperative learning approach to the travelling salesman problem. IEEE Trans Evolut Comput 1(1): 53–66

    Article  Google Scholar 

  • Dowsland K, Soubeiga E, Burke EK (2007) A simulated annealing hyper-heuristic for determining shipper sizes. Eur J Oper Res 179:759–774

    Article  MATH  Google Scholar 

  • Dueck G (1993) New optimisation heuristics. the Great Deluge algorithm and record-to-record travel. J Comput Phys 104:86–92

    Article  MATH  Google Scholar 

  • Duque T, Goldberg D, Sastry K (2008) Improving the efficiency of the extended compact genetic algorithm. In: GECCO ’08: Proceedings of the 10th annual conference on genetic and evolutionary computation, ACM, New York, pp 467–468. doi:http://doi.acm.org/10.1145/1389095.1389181

  • Durham W (1991) Coevolution: genes, culture and human diversity. Stanford University Press, Stanford, CA

    Google Scholar 

  • Fleurent C, Ferland J (1997) Genetic and hybrid algorithms for graph coloring. Ann Oper Res 63:437–461

    Article  Google Scholar 

  • Fukunaga A (2008) Automated discovery of local search heuristics for satisfiability testing. Evolut Comput 16(1):31–61, doi: 10.1162/evco.2008.16.1.31, URL http://www.mitpressjournals.org/doi/abs/10.1162/evco.2008.16.%1.31, pMID: 18386995, http://www.mitpressjournals.org/doi/pdf/10.1162/evco.2008.16.1.31

    Google Scholar 

  • Gabora L (1993) Meme and variations: a computational model of cultural evolution. In: L Nadel, Stein D (eds) 1993 Lectures in complex systems. Addison-Wesley, Boston, MA, pp 471–494

    Google Scholar 

  • Gallardo J, Cotta C, Fernandez A (2007) On the hybridization of memetic algorithms with branch-and-bound techniques. Syst Man Cybern Part B IEEE Trans 37(1):77–83. doi: 10.1109/TSMCB.2006.883266

    Article  Google Scholar 

  • Gamma E, Helm R, Johnson R, Vlissides J (1995) Design patterns, elements of reusable object-oriented software. Addison-Wesley, Reading, MA

    Google Scholar 

  • Geiger CD, Uzsoy R, Aytug H (2006) Rapid modeling and discovery of priority dispatching rules: an autonomous learning approach. J Scheduling 9(1):7–34

    Article  MATH  Google Scholar 

  • Glover F, Punnen A (1997) The traveling salesman problem: new solvable cases and linkages with the development of approximation algorithms. J Oper Res Soc 48:502–510

    MATH  Google Scholar 

  • Gutin G, Yeo A (2006) Domination analysis of combinatorial optimization algorithms and problems. In: Graph theory, combinatorics and algorithms, operations research/computer science interfaces, vol 34. Springer, New York, pp 145–171

    Google Scholar 

  • Gutin G, Karapetyan D, Krasnogor N (2007) Memetic algorithm for the generalized asymmetric traveling salesman problem. In: Pavone M, Nicosia G, Pelta D, Krasnogor N (eds) Proceedings of the 2007 workshop on nature inspired cooperative strategies for optimisation. Studies in computational intelligence. Springer, Berlin

    Google Scholar 

  • Hansen P, Mladenovic N (1998) Variable neighborhood search for the p-median. Location Sci 5(4):207–226

    Article  Google Scholar 

  • Hansen P, Mladenovic N (2001) Variable neighborhood search: principles and applications. Eur J Oper Res (130):449–467

    Article  MathSciNet  MATH  Google Scholar 

  • Hart W (2003) Locally-adaptive and memetic evolutionary pattern search algorithms. Evolut Comput 11:29–52

    Article  Google Scholar 

  • Hart W (2005) Rethinking the design of real-coded evolutionary algorithms: making discrete choices in continuous search domains. J Soft Comput Fus Found Methodol Appl 9:225–235

    MATH  Google Scholar 

  • Hart W, Krasnogor N, Smith J (2004) Recent advances in memetic algorithms, studies in fuzziness and soft computing, vol 166, Springer, Berlin/Heidelberg/New York, chap Memetic Evolutionary Algorithms, pp 3–27

    Book  Google Scholar 

  • Hart WE (1994) Adaptive global optimization with local search. Ph.D. thesis, University of California, San Diego, CA

    Google Scholar 

  • He L, Mort N (2000) Hybrid genetic algorithms for telecommunications network back-up routing. BT Technol J 18(4):42–50

    Article  Google Scholar 

  • Hinton G, Nowlan S (1987) How learning can guide evolution. Complex Syst 1:495–502

    MATH  Google Scholar 

  • Holland JH (1976) Adaptation in natural and artificial systems. The University of Michigan Press, New York

    Google Scholar 

  • Hoshino S (1971) On Davies, Swann and Campey minimisation process. Comput J 14:426

    Article  MATH  Google Scholar 

  • Houck C, Joines J, Kay M, Wilson J (1997) Empirical investigation of the benefits of partial lamarckianism. Evolut Comput 5(1):31–60

    Article  Google Scholar 

  • Ishibuchi H, Kaige S (2004) Implementation of simple multiobjective memetic algorithms and its application to knapsack problems. Int J Hybrid Intell Syst 1(1–2):22–35

    Google Scholar 

  • Jakob W (2006) Towards an adaptive multimeme algorithm for parameter optimisation suiting the engineers needs. In: Runarsson TP, et al. (eds) Proceedings of the IX parallel problem solving from nature conference (PPSN IX). Lecture notes in computer science 4193. Springer, Berlin, pp 132–141

    Google Scholar 

  • Jaszkiewicz A (2002) Genetic local search for multi-objective combinatorial optimization. Eur J Oper Res 137

    Google Scholar 

  • Jin Y (2005) A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput Fus Found Methodol Appl 9:3–12

    Google Scholar 

  • Johnson D, Papadimitriou C, Yannakakis M (1988) How easy is local search. J Comput Syst Sci 37:79–100

    Article  MathSciNet  MATH  Google Scholar 

  • Jongen H, Meer K, Triesch E (2004) Optimization theory. Springer, New York

    MATH  Google Scholar 

  • Kaelbling L, Littman M, Moore A (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237–285

    Google Scholar 

  • Kallel L, Naudts B, Reeves C (2001) Properties of fitness functions and search landscapes. In: Kallel L, Naudts B, Rogers A (eds) Theoretical aspects of evolutionary computing. Springer, Berlin, pp 175–206

    Google Scholar 

  • Kirkpatrick S, Gelatt C, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680

    Article  MathSciNet  MATH  Google Scholar 

  • Kononova A, Hughes K, Pourkashanian M, Ingham D (2007) Fitness diversity based adaptive memetic algorithm for solving inverse problems of chemical kinetics. In: IEEE Congress on Evolutionary Computation (CEC), IEEE. Singapore, pp 2366–2373

    Chapter  Google Scholar 

  • Krasnogor N (1999) Coevolution of genes and memes in memetic algorithms. In: Wu A (ed) Proceedings of the 1999 genetic and evolutionary computation conference, Graduate students workshop program, San Francisco, CA, http://www.cs.nott.ac.uk/ nxk/PAPERS/memetic.pdf, (poster)

  • Krasnogor N (2002) Studies on the theory and design space of memetic algorithms. Ph.D. thesis, University of the West of England, Bristol, http://www.cs.nott.ac.uk/ nxk/PAPERS/thesis.pdf

  • Krasnogor N (2004a) Recent advances in memetic algorithms, Studies in fuzziness and soft computing, vol 166, Springer, Berlin, Heidelberg New York, chap Towards robust memetic algorithms, pp 185–207

    Book  Google Scholar 

  • Krasnogor N (2004b) Self-generating metaheuristics in bioinformatics: the protein structure comparison case. Genet Programming Evol Mach 5(2):181–201

    Article  Google Scholar 

  • Krasnogor N, Gustafson S (2002) Toward truly “memetic” memetic algorithms: discussion and proof of concepts. In: Corne D, Fogel G, Hart W, Knowles J, Krasnogor N, Roy R, Smith JE, Tiwari A (eds) Advances in nature-inspired computation: the PPSN VII workshops, PEDAL (Parallel, Emergent and Distributed Architectures Lab). University of Reading, UK ISBN 0-9543481-0-9

    Google Scholar 

  • Krasnogor N, Gustafson S (2003) The local searcher as a supplier of building blocks in self-generating memetic algorithms. In: Hart JS WE, Krasnogor N (eds) Fourth international workshop on memetic algorithms (WOMA4), In GECCO 2003 workshop proceedings. Chicago, IL

    Google Scholar 

  • 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 

  • Krasnogor N, Pelta D (2002) Fuzzy memes in multimeme algorithms: a fuzzy-evolutionary hybrid. In: Verdegay J (ed) Fuzzy sets based heuristics for optimization. Springer, Berlin

    Google Scholar 

  • Krasnogor N, Smith J (2000) A memetic algorithm with self-adaptive local search: TSP as a case study. In: Whitley D, Goldberg D, Cantu-Paz E, Spector L, Parmee I, Beyer HG (eds) GECCO 2000: Proceedings of the 2000 genetic and evolutionary computation conference, Morgan Kaufmann, San Francisco, CA

    Google Scholar 

  • Krasnogor N, Smith J (2001) Emergence of profitable search strategies based on a simple inheritance mechanism. In: Spector L, Goodman E, Wu A, Langdon W, Voigt H, Gen M, Sen S, Dorigo M, Pezeshj S, Garzon M, Burke E (eds) GECCO 2001: Proceedings of the 2001 genetic and evolutionary computation conference, Morgan Kaufmann, San Francisco, CA

    Google Scholar 

  • Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms: Model, taxonomy and design issues. IEEE Trans Evolut Algorithms 9(5):474–488

    Article  Google Scholar 

  • Krasnogor N, Smith J (2008) Memetic algorithms: the polynomial local search complexity theory perspective. J Math Model Algorithms 7:3–24

    Article  MathSciNet  MATH  Google Scholar 

  • Krasnogor N, Blackburne B, Hirst J, Burke E (2002) Multimeme algorithms for protein structure prediction. In: Guervos JM, Adamidis P, Beyer H, Fernandez-Villacanas J, Schwefel H (eds) 7th International conference parallel problem solving from nature, PPSN, Springer, Berlin/Heidelberg, Granada, Spain, Lecture notes in computer science, vol 2439. Springer, pp 769–778

    Google Scholar 

  • Krasnogor N, Gustafson S, Pelta D, Verdegay J (eds) (2008) Systems self-assembly: multidisciplinary snapshots, Studies in multidisciplinarity, vol 5. Elsevier, Spain

    Google Scholar 

  • Kretwski M (2008) A memetic algorithm for global induction of decision trees. In: Proceedings of SOFSEM: theory and practice of computer science. Lecture notes in computer science, Springer, New York, pp 531–540

    Google Scholar 

  • Kuhn T (1962) The structure of scientific revolution. University of Chicago Press, Chicago, IL

    Google Scholar 

  • Landa-Silva D, Le KN (2008) A simple evolutionary algorithm with self-adaptation for multi-objective optimisation. Springer, Berlin, pp 133–155

    Google Scholar 

  • Landa Silva J, Burke EK (2004) Using diversity to guide the search in multi-objective optimization. World Scientific, Singapore, pp 727–751

    Google Scholar 

  • Lee Z, Lee C (2005) A hybrid search algorithm with heuristics for resource allocation problem. Inf Sci 173:155–167

    Article  Google Scholar 

  • Li H, Landa-Silva D (2008) Evolutionary multi-objective simulated annealing with adaptive and competitive search direction. In: Proceedings of the 2008 IEEE congress on evolutionary computation (CEC 2008). IEEE Press, Piscataway, NJ, pp 3310–3317

    Google Scholar 

  • Liu BF, Chen HM, Chen JH, Hwang SF, Ho SY (2005) Meswarm: memetic particle swarm optimization. In: GECCO ’05: Proceedings of the 2005 conference on Genetic and evolutionary computation, ACM, New York, pp 267–268. doi: http://doi.acm.org/10.1145/1068009.1068049

  • Liu D, Tan KC, Goh CK, Ho WK (2007) A multiobjective memetic algorithm based on particle swarm optimization. Syst Man Cybern Part B IEEE Trans 37(1):42–50. doi: 10.1109/TSMCB.2006.883270

    Article  Google Scholar 

  • Llora X, Sastry K, Yu T, Goldberg D (2007) Do not match, inherit: fitness surrogates for genetics-based machine learning techniques. In: Proceedings of the 9th annual conference on genetic and evolutionary computation. ACM, San Mateo, CA, pp 1798–1805

    Google Scholar 

  • Lozano M, Herrera F, Krasnogor N, Molina D (2004) Real-coded memetic algorithms with crossover hill-climbing. Evolut Comput 12(3):273–302

    Article  Google Scholar 

  • Mayley G (1996) Landscapes, learning costs and genetic assimilation. Evolut Comput 4(3):213–234

    Article  Google Scholar 

  • McCulloch W, Pitts W (1943) A logical calculus of the ideas immanent in nervous system. Bull Math Biophys 5:115–133

    Article  MathSciNet  MATH  Google Scholar 

  • Merz P (2003) The compact memetic algorithm. In: Proceedings of the IV International workshop on memetic algorithms (WOMA IV). GECCO 2003, Chicago, IL. http://w210.ub.uni-tuebingen.de/portal/woma4/

  • Merz P, Freisleben B (1999) Fitness landscapes and memetic algorithm design. In: New ideas in optimization, McGraw-Hill, Maidenhead, pp 245–260

    Google Scholar 

  • Mezmaz M, Melab N, Talbi EG (2007) Combining metaheuristics and exact methods for solving exactly multi-objective problems on the grid. J Math Model Algorithms 6:393–409

    Article  MathSciNet  MATH  Google Scholar 

  • Michiels W, Aarts E, Korst J (2007) Theoretical aspects of local search. Monographs in theoretical computer science. Springer, New York

    MATH  Google Scholar 

  • Molina D, Lozano M, Garcia-Martines C, Herrera F (2008) Memetic algorithm for intense local search methods using local search chains. In: Hybrid metaheuristics: 5th international workshop. Lecture notes in computer science. Springer, Berlin/Heidelberg/New York, pp 58–71

    Google Scholar 

  • Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a lamarkian genetic algorithm and an empirical binding free energy function. J Comp Chem 14:1639–1662

    Article  Google Scholar 

  • Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Tech. Rep. Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, CA

    Google Scholar 

  • Nebro A, Alba E, Luna F (2007) Multi-objective optimization using grid computing. Soft Comput 11:531–540

    Article  Google Scholar 

  • Nelder J, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313. doi: 10.1093/comjnl/7.4.308

    MATH  Google Scholar 

  • Neri F, Jari T, Cascella G, Ong Y (2007a) An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE/ACM Trans Comput Biol Bioinformatics 4(2):264–278

    Article  Google Scholar 

  • Neri F, Tirronen V, Karkkainen T, Rossi T (2007b) Fitness diversity based adaptation in multimeme algorithms: a comparative study. In: Proceedings of the IEEE congress on evolutionary computation. IEEE, Singapore, pp 2374–2381

    Google Scholar 

  • Nguyen QH, Ong YS, Lim MH, Krasnogor N (2007) A comprehensive study on the design issues of memetic algorithm. In: Proceedings of the 2007 IEEE congress on evolutionary computation. IEEE, Singapore, pp 2390–2397

    Google Scholar 

  • Niesse J, Mayne H (Sep. 15, 1996) Global geometry optimization of atomic clusters using a modified genetic algorithm in space-fixed coordinates. J Chem Phys 105(11):4700–4706

    Article  Google Scholar 

  • O'Neill M, Ryan C (2003) Grammatical evolution: evolutionary automatic programming in an arbitrary language. Genetic Programming, vol 4. Springer, Essex

    MATH  Google Scholar 

  • Ong Y, Keane A (2004) Meta-lamarckian learning in memetic algorithms. IEEE Trans Evolut Comput 8:99–110

    Article  Google Scholar 

  • Ong Y, Lim M, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern Part B 36:141–152

    Google Scholar 

  • Ong Y, Lum K, Nair P (2008) Hybrid evolutionary algorithm with hermite radial basis function interpolants for computationally expensive adjoint solvers. Comput Opt Appl 39:97–119

    Article  MathSciNet  MATH  Google Scholar 

  • Paenke I, Jin J (2006) Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation. IEEE Trans Evolut Comput 10:405–420

    Article  Google Scholar 

  • Pappa G, Freitas A (2007) Discovering new rule induction algorithms with grammar-based genetic programming. In: Maimon O, Rokach L (eds) Soft computing for knowledge discovery and data mining. Springer, New York, pp 133–152

    Google Scholar 

  • Petalas Y, Parsopoulos K, Vrahatis M (2007) Memetic particle swarm optimisation. Ann Oper Res 156:99–127

    Article  MathSciNet  MATH  Google Scholar 

  • Pirkwieser S, Raidl GR, Puchinger J (2008) A Lagrangian decomposition/evolutionary algorithm hybrid for the knapsack constrained maximum spanning tree problem. In: Cotta C, van Hemert J (eds) Recent advances in evolutionary computation for combinatorial optimization. Springer, Valencia, pp 69–85

    Chapter  Google Scholar 

  • Quang Q, Ong Y, Lim M, Krasnogor N (2009) Adaptive cellular memetic algorithm. Evolut Comput 17(2):231–256

    Article  Google Scholar 

  • Raidl GR, Puchinger J (2008) Combining (integer) linear programming techniques and metaheuristics for combinatorial optimization. In: Blum C, et al. (eds) Hybrid Metaheuristics - an emergent approach for combinatorial optimization. Springer, Berlin/Heidelberg/New York, pp 31–62

    Google Scholar 

  • Reeves C (1996) Hybrid genetic algorithms for bin-packing and related problems. Ann Oper Res 63:371–396

    Article  MATH  Google Scholar 

  • Richerson P, Boyd R (1978) A dual inheritance model of the human evolutionary process: I. Basic postulates and a simple model. J Soc Biol Struct I:127–154

    Article  Google Scholar 

  • Romero-Campero F, Cao H, Camara M, Krasnogor N (2008) Structure and parameter estimation for cell systems biology models. In: Keijzer, M et al. (eds) Proceedings of the genetic and evolutionary computation conference (GECCO-2008), ACM, Seattle, WA, pp 331–338

    Chapter  Google Scholar 

  • Sacks J, Welch W, Mitchell T, Wynn H (1989) Design and analysis of computer experiments. Stat Sci 4:409–435

    Article  MathSciNet  MATH  Google Scholar 

  • Schwefel H (1993) Evolution and optimum seeking: the sixth generation. Wiley, New York, NY

    Google Scholar 

  • Siepmann P, Martin C, Vancea I, Moriarty P, Krasnogor N (2007) A genetic algorithm approach to probing the evolution of self-organised nanostructured systems. Nano Lett 7(7):1985–1990

    Article  Google Scholar 

  • Smith J (2001) Modelling GAs with self adaptive mutation rates. In: GECCO-2001: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, San Francisco, CA

    Google Scholar 

  • Smith J (2002a) Co-evolution of memetic algorithms: Initial results. In: Merelo, Adamitis, Beyer, Fernandez-Villacans, Schwefel (eds) Parallel problem solving from nature – PPSN VII, LNCS 2439. Springer, Spain, pp 537–548

    Chapter  Google Scholar 

  • Smith J (2002b) Co-evolution of memetic algorithms for protein structure prediction. In: Hart K, Smith J (eds) Proceedings of the third international workshop on memetic algorithms, New York

    Google Scholar 

  • Smith J (2003) Co-evolving memetic algorithms: A learning approach to robust scalable optimisation. In: Proceedings of the 2003 congress on evolutionary computation. Canberra, pp 498–505

    Google Scholar 

  • Smith JE (2007) Credit assignment in adaptive memetic algorithms. In: GECCO ’07: Proceedings of the 9th annual conference on genetic and evolutionary computation, ACM, New York, pp 1412–1419. doi: http://doi.acm.org/10.1145/1276958.1277219

  • Smith R, Smuda E (1995) Adaptively resizing populations: algorithms, analysis and first results. Complex Syst 1(9):47–72

    Google Scholar 

  • Sorensen K, Sevaux M (2006) MA:PM: memetic algorithms with population management. Comput Oper Res 33:1214–1225

    Article  Google Scholar 

  • Sudholt D (2007) Memetic algorithms with variable-depth search to overcome local optima. In: Proceedings of the 2007 conference on genetic and evolutionary computation (GECCO), ACM, New York, pp 787–794

    Google Scholar 

  • Tabacman M, Bacardit J, Loiseau I, Krasnogor N (2008) Learning classifier systems in optimisation problems: a case study on fractal travelling salesman problems. In: Proceedings of the international workshop on learning classifier systems, Lecture notes in computer science, Springer, New York, URL http://www.cs.nott.ac.uk/ nxk/PAPERS/maxi.pdf

  • Tang M, Yao X (2007) A memetic algorithm for VLSI floorplanning. Syst Man Cybern Part B IEEE Trans 37(1):62–69. doi: 10.1109/TSMCB.2006.883268

    Article  Google Scholar 

  • Tay JC, Ho NB (2008) Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Comput Ind Eng 54(3):453–473

    Article  Google Scholar 

  • Turney P (1996) How to shift bias: lessons from the Baldwin effect. Evolut Comput 4(3):271–295

    Article  Google Scholar 

  • Vavak F, Fogarty T (1996) Comparison of steady state and generational genetic algorithms for use in nonstationary environments. In: Proceedings of the 1996 IEEE conference on evolutionary computation, Japan, pp 192–195

    Google Scholar 

  • Wang H, Wang D, Yang S (2009) A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Comput 13(8–9)

    Google Scholar 

  • Whitley L, Gruau F (1993) Adding learning to the cellular development of neural networks: evolution and the Baldwin effect. Evolut Comput 1:213–233

    Article  Google Scholar 

  • Whitley L, Gordon S, Mathias K (1994) Lamarkian evolution, the Baldwin effect, and function optimisation. In: Davidor Y, Schwefel HP, Männer R (eds) PPSN, Lecture notes in computer science, vol 866. Springer, Berlin, pp 6–15

    Google Scholar 

  • Wolpert D, Macready W (1997) No free lunch theorems for optimisation. IEEE Trans Evolut Comput 1(1):67–82

    Article  Google Scholar 

  • Yanga J, Suna L, Leeb H, Qiand Y, Liang Y (2008) Clonal selection based memetic algorithm for job shop scheduling problems. J Bionic Eng 5:111–119

    Article  Google Scholar 

  • Yannakakis M (1997) Computational complexity. In: Aarts E, Lenstra J (eds) Local search in combinatorial optimization. Wiley, New York, pp 19–55

    Google Scholar 

  • Zhou Z (2004) Hierarchical surrogate-assisted evolutionary optimization framework. In: Congress on evolutionary computation, 2004. CEC 2004. Portland, pp 1586–1593

    Google Scholar 

  • Zhou Z, Ong Y, Lim M, Lee B (2007) Memetic algorithm using multi-surrogates for computationally expensive optimization problems. Soft Comput Fus Found Methodol Appl 11:957–971

    Google Scholar 

Download references

Acknowledgments

The author would like to acknowledge the many friends and colleagues with whom he has collaborated over the years. Their ideas, scientific rigor and enthusiasm for memetic algorithms has been a continuous source of inspiration and challenges. The author would also like to thank Jonathan Blakes and James Smaldon for their valuable comments during the preparation of this paper. The author wishes to acknowledge funding from the EPSRC for projects EP/D061571/1 and EP/C523385/1. Finally, the editors of this book are thanked for giving the author an opportunity to contribute.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Natalio Krasnogor .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this entry

Cite this entry

Krasnogor, N. (2012). Memetic Algorithms. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds) Handbook of Natural Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_29

Download citation

Publish with us

Policies and ethics