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2018 | OriginalPaper | Chapter

14. Evolutionary Algorithms

Authors : David Corne, Michael A. Lones

Published in: Handbook of Heuristics

Publisher: Springer International Publishing

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Abstract

Evolutionary algorithms (EAs) are population-based metaheuristics, originally inspired by aspects of natural evolution. Modern varieties incorporate a broad mixture of search mechanisms, and tend to blend inspiration from nature with pragmatic engineering concerns; however, all EAs essentially operate by maintaining a population of potential solutions and in some way artificially ‘evolving’ that population over time. Particularly well-known categories of EAs include genetic algorithms (GAs), Genetic Programming (GP), and Evolution Strategies (ES). EAs have proven very successful in practical applications, particularly those requiring solutions to combinatorial problems. EAs are highly flexible and can be configured to address any optimization task, without the requirements for reformulation and/or simplification that would be needed for other techniques. However, this flexibility goes hand in hand with a cost: the tailoring of an EA’s configuration and parameters, so as to provide robust performance for a given class of tasks, is often a complex and time-consuming process. This tailoring process is one of the many ongoing research areas associated with EAs.

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Lones MA (2014) Metaheuristics in nature-inspired algorithms. In: Proceedings of genetic and evolutionary computation conference (GECCO 2014), workshop on metaheuristic design patterns (MetaDeeP). ACM, pp 1419–1422

Fogel DB (1998) Evolutionary computation: the fossil record. Wiley-IEEE Press, Piscataway

Hauschild M, Pelikan M (2011) An introduction and survey of estimation of distribution algorithms. Swarm Evol Comput 1(3):111–128. https://doi.org/10.1016/j.swevo.2011.08.003. Available: http://www.sciencedirect.com/science/article/pii/S2210650211000435

Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31. https://doi.org/10.1109/TEVC.2010.2059031. Available: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5601760&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs∼all.jsp%3Farnumber%3D5601760

Storn R, Price K (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. https://doi.org/1008202821328. Available: http://link.springer.com/article/10.1023%2FA%3A1008202821328#page-1.

Zaharie D (2009) Influence of crossover on the behavior of differential evolution algorithms. Appl Soft Comput 9(3):1126–1138. https://doi.org/10.1016/j.asoc.2009.02.012. Available: http://www.sciencedirect.com/science/article/pii/S1568494609000325

García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J Heuristics 15(6):617–644. https://doi.org/10.1007/s10732-008-9080-4. Available: http://link.springer.com/article/10.1007/s10732-008-9080-4

Hansen N, Auger A, Finck S, Ros R (2010) Real-parameter black-box optimization benchmarking 2010: experimental setup. INRIA research report No. 7215. INRIA

Liang J, Qu B, Suganthan P, Hernández-Díaz A (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Technical report 201212. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, pp 3–18

10.

Tang K, Li X, Suganthan PN, Yang Z, Weise T (2009) Benchmark functions for the CEC’2010 special session and competition on large-scale global optimization. Technical report. Nature Inspired Computation and Applications Laboratory, University of Science and Technology of China

11.

Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://doi.org/10.1109/4235.585893. Available: http://ieeexplore.ieee.org/xpls/abs~all.jsp?arnumber=585893

12.

Igel C, Toussaint M (2003) On classes of functions for which no free lunch results hold. Inf Process Lett 86(6):317–321

13.

Piotrowski AP (2015) Regarding the rankings of optimization heuristics based on artificially-constructed benchmark functions. Inf Sci 297:191–201. Available: http://www.sciencedirect.com/science/article/pii/S0020025514010937

14.

Lones MA, Tyrrell AM (2007) Regulatory motif discovery using a population clustering evolutionary algorithm. IEEE/ACM Trans Comput Biol Bioinform 4(3):403–414. https://doi.org/10.1109/tcbb.2007.1044. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4288066

15.

Koza J (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

16.

Miller JF (2011) Cartesian genetic programming. https://doi.org/10.1007/978-3-642-17310-3_2

17.

Veenhuis CB (2009) Tree based differential evolution. Lect Notes Comput Sci 5481:208–219

18.

Kim K, Shan Y, Nguyen X, McKay RI (2014) Probabilistic model building in genetic programming: a critical review. Genet Program Evolvable Mach 15(2):115–167. https://doi.org/10.1007/s10710-013-9205-x. Available: http://link.springer.com/article/10.1007/s10710-013-9205-x

19.

Poli R, Langdon W, McPhee NF (2008) A field guide to genetic programming. Published via http://lulu.com

20.

Luke S (2013) Essentials of metaheuristics. Published via http://lulu.com

21.

Stanley KO, Miikkulainen R (2003) A taxonomy for artificial embryogeny. Artif Life 9(2):93–130. https://doi.org/10.1162/106454603322221487. Available: http://www.mitpressjournals.org/doi/abs/10.1162/106454603322221487 (pages 94 and 95)

22.

Floreano D, Dürr P, Mattiussi C (2008) Neuroevolution: from architectures to learning. Evol Intell 1(1):47–62. https://doi.org/10.1007/s12065-007-0002-4. Available: http://link.springer.com/article/10.1007/s12065-007-0002-4

23.

Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech concurrent computation program, C3P report 826

24.

Neri F, Cotta C (2012) Memetic algorithms and memetic computing optimization: a literature review. Swarm Evol Comput 2:1–14. https://doi.org/10.1016/j.swevo.2011.11.003. Available: http://www.sciencedirect.com/science/article/pii/S2210650211000691

25.

Hao J (2012) Memetic algorithms in discrete optimization. In: Neri F, Cotta C, Moscato P (eds) Handbook of memetic algorithms. Springer, Berlin/Heidelberg. https://doi.org/10.1007/978-3-642-23247-3_6

26.

Ross P (2005) Hyper-heuristics. In: Search methodologies. Springer, Berlin, pp 529–556

27.

Singh G, Deb K (2006) Comparison of multi-modal optimization algorithms based on evolutionary algorithms. ACM, New York. https://doi.org/10.1145/1143997.1144200

28.

Mengshoel OJ, Goldberg DE (2008) The crowding approach to niching in genetic algorithms. Evol Comput 16(3):315–354. https://doi.org/10.1162/evco.2008.16.3.315. Available: http://www.mitpressjournals.org/doi/abs/10.1162/evco.2008.16.3.315

29.

Sareni B, Krahenbuhl L (1998) Fitness sharing and niching methods revisited. IEEE Trans Evol Comput 2(3):97–106. https://doi.org/10.1109/4235.735432. Available: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=735432&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel4%2F4235%2F15834%2F00735432.pdf%3Farnumber%3D735432

30.

Lim T (2014) Structured population genetic algorithms: a literature survey. Artif Intell Rev 41(3):385–399. https://doi.org/10.1007/s10462-012-9314-6. Available: http://link.springer.com/article/10.1007%2Fs10462-012-9314-6

31.

Shir OM, Back T (2005) Dynamic niching in evolution strategies with covariance matrix adaptation. https://doi.org/10.1109/CEC.2005.1555018

32.

Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRef

33.

Knowles J, Corne D (1999) The pareto archived evolution strategy: a new baseline algorithm for paretomultiobjective optimisation. In: Proceedings of the 1999 congress on evolutionary computation (CEC’99), vol 1. IEEE

34.

Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731CrossRef

35.

Corne DW, Deb K, Fleming PJ, Knowles JD (2003) The good of the many outweighs the good of the one: evolutionary multi-objective optimization. IEEE Connect Newslett 1(1):9–13

36.

Zhou A, Qu B, Li H, Zhao S, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol Comput 1(1):32–49. https://doi.org/10.1016/j.swevo.2011.03.001. Available: http://www.sciencedirect.com/science/article/pii/S2210650211000058

37.

Goldberg D, Smith R (1987) Nonstationary function optimization using genetic algorithm with dominance and diploidy. In: Proceedings of the second international conference on genetic algorithms and their application (ICGA). Laurence Erlbaum Associates, pp 59–68

38.

Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol Comput 6:1–24. https://doi.org/10.1016/j.swevo.2012.05.001. Available: http://www.sciencedirect.com/science/article/pii/S2210650212000363

39.

Popovici E, Bucci A, Wiegand RP, De Jong ED (2012) Coevolutionary principles. In: Rozenberg G, Bäck T, Kok JN (eds) Handbook of natural computing. Springer, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_31

40.

Hillis WD (1990) Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D Nonlinear Phenom 42(1–3):228–234. https://doi.org/10.1016/0167-2789(90)90076-2. Available: http://www.sciencedirect.com/science/article/pii/0167278990900762

41.

Potter MA, Jong KA (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1):1–29. https://doi.org/10.1162/106365600568086. Available: http://www.mitpressjournals.org/doi/abs/10.1162/106365600568086

42.

Yang Z, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985–2999. https://doi.org/10.1016/j.ins.2008.02.017. Available: http://www.sciencedirect.com/science/article/pii/S002002550800073X

43.

Urbanowicz RJ, Moore JH (2009) Learning classifier systems: a complete introduction, review, and roadmap. J Artif Evol Appl 2009:1–25CrossRef

44.

Ochoa G, Harvey I, Buxton H (1999) On recombination and optimal mutation rates. In: Proceedings of genetic and evolutionary computation conference, vol 1, pp 488–495. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.2369

45.

Eiben AE, Smit SK (2011) Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol Comput 1(1):19–31. https://doi.org/10.1016/j.swevo.2011.02.001. Available: http://www.sciencedirect.com/science/article/pii/S2210650211000022

46.

Fogel LJ (1962) Autonomous automata. Ind Res 4(2):14–19

47.

Ochoa G, Blum C, Chicano F (2015) Evolutionary computation in combinatorial optimization. Springer International Publishing: Imprint: Springer, Cham

48.

Bajpai RP (ed) (2014) Innovative design, analysis and development practices in aerospace and automotive engineering: I-Dad 2014, 22–24 Feb 2014. Springer Science & Business, Singapore

49.

Gaurav A, Kumar V, Nigam D (2012) New applications of soft computing in bioinformatics: a review. J Pure Appl Sci Tech 11(1):12–22

50.

Gupta SK, Ramteke M (2014) Applications of genetic algorithms in chemical engineering II: case studies. In: Applications of metaheuristics in process engineering. Springer, Cham, pp 61–87

51.

Bentley P, Corne D (2002) Creative evolutionary systems. Morgan Kaufmann, San Francisco

52.

Chen SH (ed) (2012) Genetic algorithms and genetic programming in computational finance. Springer Science & Business Media, New York

53.

Gen M, Cheng R (1996) Genetic algorithms and manufacturing systems design, 1st edn. Wiley, New YorkCrossRef

54.

Adeli H, Sarma KC (2006) Cost optimization of structures: fuzzy logic, genetic algorithms, and parallel computing. Wiley, ChichesterCrossRef

55.

Lones MA, Tyrrell AM (2007) A co-evolutionary framework for regulatory motif discovery. https://doi.org/10.1109/CEC.2007.4424978

56.

Lones M, Alty JE, Lacy SE, Jamieson DR, Possin KL, Schuff N, Smith SL (2013) Evolving classifiers to inform clinical assessment of parkinson’s disease. In: 2013 IEEE symposium on computational intelligence in healthcare and e-health (CICARE), pp. 76–82. IEEE

57.

Lones M, Turner AP, Caves LS, Stepney S, Smith SL, Tyrrell AM (2014) Artificial biochemical networks: evolving dynamical systems to control dynamical systems. IEEE Trans Evol Comput 18(2):145–166CrossRef

58.

Lones MA, Smith SL, Tyrrell AM, Alty JE, Jamieson DS (2013) Characterising neurological time series data using biologically motivated networks of coupled discrete maps. BioSystems 112(2):94–101CrossRef

Title: Evolutionary Algorithms
Authors: David Corne
Michael A. Lones
Publisher: Springer International Publishing
Book: Handbook of Heuristics
Print ISBN: 978-3-319-07123-7

Electronic ISBN: 978-3-319-07124-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-07124-4_27

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