Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

8. Topics in Evolutinary Algorithms

verfasst von : Ke-Lin Du, M. N. S. Swamy

Erschienen in: Search and Optimization by Metaheuristics

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This chapter continues to introduce topics on EAs. Convergence of EAs is first analyzed by using scheme theorem, building-block hypothesis, and then by using finite and infinite population models. Various parallel implementations of EAs are then described in detail. Some other associated topics including coevolution and fitness approximation are finally introduced.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Estimation of Distribution Algorithms
Nächstes Kapitel Particle Swarm Optimization
Literatur
1.
Akbarzadeh-T M-R, Davarynejad M, Pariz N. Adaptive fuzzy fitness granulation for evolutionary optimization. Int J Approx Reason. 2008;49:523–38.CrossRef
2.
3.
Alba E, Dorronsoro B. The exploration/exploitation tradeoff in dynamic cellular evolutionary algorithms. IEEE Trans Evol Comput. 2005;9(2):126–42.CrossRef
4.
Alba E, Tomassini M. Parallelism and evolutionary algorithms. IEEE Trans Evol Comput. 2002;6(5):443–62.CrossRef
5.
Al-Madi NA. De Jong’s sphere model test for a human community based genetic algorithm model (HCBGA). Int J Adv Compu Sci Appl. 2014;5(1):166–172.
6.
Al-Madi NA, Khader AT. A social based model for genetic algorithms. In: Proceedings of the 3rd international conference on information technology (ICIT), Amman, Jordan, May 2007. p. 23–27
7.
Al-Naqi A, Erdogan AT, Arslan T. Adaptive three-dimensional cellular genetic algorithm for balancing exploration and exploitation processes. Soft Comput. 2013;17:1145–57.CrossRef
8.
Arora R, Tulshyan R, Deb K. Parallelization of binary and realcoded genetic algorithms on GPU using CUDA. In: Proceedings of IEEE world congress on computational intelligence, Barcelona, Spain, July 2010. p. 3680–3687.
9.
Arsuaga-Rios M, Vega-Rodriguez MA. Multiobjective energy optimization in grid systems from a brain storming strategy. Soft Comput. 2015;19:3159–72.
10.
Bai H, Ouyang D, Li X, He L, Yu H. MAX-MIN ant system on GPU with CUDA. In: Proceedings of the IEEE 4th international conference on innovative computing, information and control (ICICIC), Kaohsiung, Taiwan, Dec 2009. p. 801–204.
11.
Barabasi AL, Freeh VW, Jeong H, Brockman JB. Parasitic computing. Nature. 2001;412(6850):894–7.CrossRef
12.
Barbosa HJC. A genetic algorithm for min-max problems. In: Proceedings of the 1st international conference on evolutionary computation and applications, Moscow, Russia, 1996. p. 99–109.
13.
Beyer H-G. An alternative explanation for the manner in which genetic algorithms operate. Biosystems. 1997;41(1):1–15.CrossRef
14.
Biles J. Genjam: a genetic algorithm for generating jazz solos. In: Proceedings of international computer music conference, Arhus, Denmark, 1994. p. 131–137.
15.
Bongard J, Zykov V, Lipson H. Resilient machines through continuous self-modeling. Science. 2006;314(5802):1118–21.CrossRef
16.
Bozejko W, Smutnicki C, Uchronski M. Parallel calculating of the goal function in metaheuristics using GPU. In: Proceedings of the 9th international conference on computational science, Baton Rouge, LA, USA, May 2009, vol. 5544 of Lecture Notes in Computer Science. Berlin: Springer; 2009. p. 1014–2023.
17.
Brownlee AEI, McCall JAW, Zhang Q. Fitness modeling with Markov networks. IEEE Trans Evol Comput. 2013;17(6):862–79.CrossRef
18.
Calazan RM, Nedjah N, De Macedo Mourelle L. Parallel GPU-based implementation of high dimension particle swarm optimizations. In: Proceedings of the IEEE 4th Latin American symposium on circuits and systems (LASCAS), Cusco, Peru, Feb 2013. p. 1–4.
19.
Caldwell C, Johnston VS. Tracking a criminal suspect through “face-space” with a genetic algorithm. In: Proceedings of the 4th international conference on genetic algorithms, San Diego, CA, USA, July 1991. San Diego, CA: Morgan Kaufmann; 1991. p. 416–421
20.
Candan C, Dreo J, Saveant P, Vidal V. Parallel divide-and-evolve: experiments with Open-MP on a multicore machine. In: Proceedings of GECCO, Dublin, Ireland, July 2011. p. 1571–1578.
21.
22.
Cheang SM, Leung KS, Lee KH. Genetic parallel programming: design and implementation. Evol Comput. 2006;14(2):129–56.CrossRef
23.
Collet P, Lutton E, Schoenauer M, Louchet J. Take it EASEA. In: Proceedings of the 6th international conference on parallel problem solving from nature (PPSN VI), Paris, France, Sept 2000, vol. 1917 of Lecture Notes in Computer Science. London: Springer; 2000. p. 891–901
24.
Collins RJ, Jefferson DR. Selection in massively parallel genetic algorithms. In: Belew RK, Booker LB, editors. Proceedings of the 4th international conference on genetic algorithms, San Diego, CA, USA, July 1991. San Diego, CA: Morgan Kaufmann; 1991. p. 249–256.
25.
Corno F, Reorda M, Squillero G. The selfish gene algorithm: a new evolutionary optimization strategy. In: Proceedings of the 13th annual ACM symposium on applied computing (SAC), Atlanta, Georgia, USA, 1998. p. 349–355.
26.
Cramer AM, Sudhoff SD, Zivi EL. Evolutionary algorithms for minimax problems in robust design. IEEE Trans Evol Comput. 2009;13(2):444–53.CrossRef
27.
Dawkins R. The selfish gene. Oxford: Oxford University Press; 1989.
28.
De Jong K. An analysis of the behavior of a class of genetic adaptive systems. PhD Thesis, University of Michigan, Ann Arbor, 1975.
29.
de Veronese PL, Krohling RA. Differential evolution algorithm on the GPU with C-CUDA. In: Proceedings of IEEE world congress on computational intelligence, Barcelona, Spain, July 2010. p. 1878–1884.
30.
Dean J, Ghemawat S. MapReduce: simplified data processing on large clusters. In: Proceedings of the 6th symposium on operating system design and implementation (OSDI), San Francisco, CA, 2004. p. 137–147.
31.
32.
33.
Eiben AE, Aarts EHL, Van Hee KM. Global convergence of genetic algorithms: a Markov chain analysis. In: Proceedings of the 1st workshop on parallel problem solving from nature (PPSN I), Dortmund, Germany, Oct 1990. Berlin: Springer; 1991. p. 3–12.
34.
Emmerich MTM, Giannakoglou KC, Naujoks B. Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels. IEEE Trans Evol Comput. 2006;10(4):421–39.CrossRef
35.
Ewald G, Kurek W, Brdys MA. Grid implementation of a parallel multiobjective genetic algorithm for optimized allocation of chlorination stations indrinking water distribution systems: Chojnice case study. IEEE Trans Syst Man Cybern Part C. 2008;38(4):497–509.
36.
Fok K-L, Wong T-T, Wong M-L. Evolutionary computing on consumer graphics hardware. IEEE Intell Syst. 2007;22:69–78.CrossRef
37.
Folino G, Pizzuti C, Spezzano G. A scalable cellular implementation of parallel genetic programming. IEEE Trans Evol Comput. 2003;7(1):37–53.CrossRefMATH
38.
Ge H, Sun L, Yang X, Yoshida S, Liang Y. Cooperative differential evolution with fast variable interdependence learning and cross-cluster mutation. Appl Soft Comput. 2015;36:300–14.CrossRef
39.
Goh C-K, Tan KC. A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Trans Evol Comput. 2009;13(1):103–27.CrossRef
40.
Goldberg DE. Genetic algorithms in search, optimization, and machine learning. Reading, MA, USA: Addison-Wesley; 1989.MATH
41.
Goldberg DE, Deb K, Korb B. Messy genetic algorithms: motivation, analysis, and first results. Complex Syst. 1989;3:493–530.MathSciNetMATH
42.
Gong Y-J, Chen W-N, Zhan Z-H, Zhang J, Li Y, Zhang Q, Li J-J. Distributed evolutionary algorithms and their models: a survey of the state-of-the-art. Appl Soft Comput. 2015;34:286–300.CrossRef
43.
Grefenstette JJ. Deception considered harmful. In: Whitley LD, editor. Foundations of genetic algorithms, vol. 2. Morgan Kaufmann: San Mateo, CA; 1993. p. 75–91.
44.
Hastings EJ, Guha RK, Stanley KO. Interactive evolution of particle systems for computer graphics and animation. IEEE Trans Evol Comput. 2009;13(2):418–32.CrossRef
45.
Herrmann JW. A genetic algorithm for minimax optimization problems. In: Proceedings of the congress on evolutionary computation (CEC), Washington DC, July 1999, vol. 2. p. 1099–1103.
46.
47.
He J, Yao X. From an individual to a population: an analysis of the first hitting time of population-based evolutionary algorithms. IEEE Trans Evol Comput. 2002;6(5):495–511.CrossRef
48.
He J, Yao X. Analysis of scalable parallel evolutionary algorithms. In: Proceedings of the IEEE congress on evolutionary computation (CEC), Vancouver, BC, Canada, July 2006. p. 120–127.
49.
He J, Yu X. Conditions for the convergence of evolutionary algorithms. J Syst Arch. 2001;47(7):601–12.CrossRef
50.
Holland J. Adaptation in natural and artificial systems. Ann Arbor, Michigan: University of Michigan Press; 1975.
51.
Holland JH. Building blocks, cohort genetic algorithms and hyperplane-defined functions. Evol Comput. 2000;8(4):373–91.CrossRef
52.
Horn J. Finite Markov chain analysis of genetic algorithms with niching. In: Proceedings of the 5th international conference on genetic algorithms, Urbana, IL, July 1993. San Francisco, CA: Morgan Kaufmann Publishers; 1993. p. 110–117
53.
Jansen T, De Jong KA, Wegener I. On the choice of the offspring population size in evolutionary algorithms. Evol Comput. 2005;13(4):413–40.CrossRef
54.
Jansen T, Wegener I. The analysis of evolutionary algorithms—a proof that crossover really can help. Algorithmica. 2002;33:47–66.MathSciNetCrossRefMATH
55.
Jin H, Frumkin M, Yan J.The OpenMP implementation of NAS parallel benchmarks and its performance. MRJ Technology Solutions, NASA Contract NAS2-14303, Moffett Field, CA, Oct 1999.
56.
Jin Y, Sendhoff B. Reducing fitness evaluations using clustering techniques and neural network ensembles. In: Proceedings of genetic and evolutionary computation, Seattle, WA, USA, July 2004. p. 688–699.
57.
Jones DR, Schonlau M, Welch WJ. Efficient global optimization of expensive black-box functions. J Global Optim. 1998;13(4):455–92.MathSciNetCrossRefMATH
58.
Kim H-S, Cho S-B. An efficient genetic algorithms with less fitness evaluation by clustering. In: Proceedings of IEEE congress on evolutionary computation (CEC), Seoul, Korea, May 2001. p. 887–894.
59.
Koza JR. Genetic programming: on the programming of computers by means of natural selection. Cambridge, MA: MIT Press; 1992.MATH
60.
Krawiec K, Bhanu B.Coevolution and linear genetic programming for visual learning. In: Proceedings of genetic and evolutionary computation conference (GECCO), Chicago, Illinois, USA, vol. 2723 of Lecture Notes of Computer Science. Berlin: Springer; 2003. p. 332–343
61.
Krohling RA, Coelho LS. Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems. IEEE Trans Syst Man Cybern Part B. 2006;36(6):1407–16.CrossRef
62.
Lassig J, Sudholt D. Design and analysis of migration in parallel evolutionary algorithms. Soft Comput. 2013;17:1121–44.CrossRefMATH
63.
Lastra M, Molina D, Benitez JM. A high performance memetic algorithm for extremely high-dimensional problems. Inf Sci. 2015;293:35–58.CrossRef
64.
Lehman J, Stanley KO. Abandoning objectives: evolution through the search for novelty alone. Evol Comput. 2011;19(2):189–223.CrossRef
65.
Lehre PK, Yao X. On the impact of mutation-selection balance on the runtime of evolutionary algorithms. IEEE Trans Evol Comput. 2012;16(2):225–41.CrossRef
66.
Leung Y, Gao Y, Xu Z-B. Degree of population diversity: a perspective on premature convergence in genetic algorithms and its Markov chain analysis. IEEE Tran Neural Netw. 1997;8(5):1165–76.CrossRef
67.
Liu J, Zhong W, Jiao L. A multiagent evolutionary algorithm for constraint satisfaction problems. IEEE Trans Syst Man Cybern Part B. 2006;36(1):54–73.CrossRef
68.
Liu J, Zhong W, Jiao L. A multiagent evolutionary algorithm for combinatorial optimization problems. IEEE Trans Syst Man Cybern Part B. 2010;40(1):229–40.CrossRef
69.
Mallipeddi R, Lee M. An evolving surrogate model-based differential evolution algorithm. Appl Soft Comput. 2015;34:770–87.CrossRef
70.
Manderick B, Spiessens P. Fine-grained parallel genetic algorithms. In: Schaffer JD, editor. Proceedings of the 3rd international conference on genetic algorithms, Fairfax, Virginia, USA, June 1989. San Mateo, CA: Morgan Kaufmann; 1989. p. 428–433.
71.
Merelo-Guervos JJ. Fluid evolutionary algorithms. In: Proceedings of IEEE congress on evolutionary computation, Barcelona, Spain, July 2010. p. 1–8.
72.
Meri K, Arenas MG, Mora AM, Merelo JJ, Castillo PA, Garcia-Sanchez P, Laredo JLJ. Cloud-based evolutionary algorithms: an algorithmic study. Natural Comput. 2013;12(2):135–47.MathSciNetCrossRef
73.
Meyer-Spradow J, Loviscach J. Evolutionary design of BRDFs. In: Chover M, Hagen H, Tost D, editors. Eurographics 2003 short paper proceedings. Spain: Granada; 2003. p. 301–6.
74.
Muhlenbein H. Parallel genetic algorithms, population genetics and combinatorial optimization. In: Schaffer JD, editor. Proceedings of the 3rd international conference on genetic algorithms, Fairfax, Virginia, USA, June 1989. San Mateo, CA: Morgan Kaufman; 1989. p. 416–421.
75.
Muhlenbein H, Schomisch M, Born J. The parallel genetic algorithm as a function optimizer. In: Proceedings of the 4th international conference on genetic algorithms, San Diego, CA, July 1991. p. 271–278.
76.
Munawar A, Wahib M, Munawar A, Wahib M. Theoretical and empirical analysis of a GPU based parallel Bayesian optimization algorithm. In: Proceedings of IEEE international conference on parallel and distributed computing, applications and technologies, Higashi Hiroshima, Japan, Dec 2009. p. 457–462.
77.
Nara K, Takeyama T, Kim H. A new evolutionary algorithm based on sheep flocks heredity model and its application to scheduling problem. In: Proceedings of IEEE international conference on systems, man, and cybernetics, Tokyo, Japan, Oct 1999, vol. 6. p. 503–508.
78.
Niwa T, Iba H. Distributed genetic programming: empirical study and analysis. In: Proceedings of the 1st annual conference on genetic programming, Stanford University, CA, USA, July 1996. p. 339–344.
79.
80.
Omidvar MN, Li X, Mei Y, Yao X. Cooperative co-evolution with differential grouping for large scale optimization. IEEE Trans Evol Comput. 2014;18(3):378–93.CrossRef
81.
Ong YS, Nair PB, Kean AJ. Evolutionary optimization of computationally expensive problems via surrogate modeling. AIAA J. 2003;41(4):687–96.CrossRef
82.
O’Reilly UM, Oppacher F. The troubling aspects of a building-block hypothesis for genetic programming. In: Whitley LD, Vose MD, editors. Foundations of genetic algorithm 3. San Francisco, CA: Morgan Kaufmann; 1995. p. 73–88
83.
Panait L. Theoretical convergence guarantees for cooperative coevolutionary algorithms. Evol Comput. 2010;18(4):581–615.CrossRef
84.
Poli R. Parallel distributed genetic programming. In: Come D, Dorigo M, Glover F, editors. New ideas in optimization. New York: McGraw-Hill; 1999.
85.
Poli R. Exact schema theory for GP and variable-length GAs with one-point crossover. Genetic Progr Evol Mach. 2001;2:123–63.CrossRefMATH
86.
Poli R, Langdon WB. Schema theory for genetic programming with one-point crossover and point mutation. Evol Comput. 2001;6(3):231–52.CrossRef
87.
Poli R, McPhee NF. General schema theory for genetic programming with subtree-swapping crossover: part i. Evol Comput. 2003;11(1):53–66.CrossRef
88.
Poli R, McPhee NF. General schema theory for genetic programming with subtree-swapping crossover: part ii. Evol Comput. 2003;11(2):169–206.CrossRef
89.
Potter MA, de Jong KA. A cooperative coevolutionary approach to function optimization. In: Proceedings of the 3rd conference on parallel problem solving from nature (PPSN III), Jerusalem, Isreal, Oct 1994. Berlin: Springer; 1994. p. 249–257.
90.
Potter MA, De Jong KA. Cooperative coevolution: an architecture for evolving coadapted subcomponenets. Evol Comput. 2000;8(1):1–29.CrossRef
91.
Qi X, Palmieri F. Theoretical analysis of evolutionary algorithms with an infinite population size in continuous space, part 1: basic properties of selection and mutation. IEEE Trans Neural Netw. 2004;5(1):102–19.
92.
Ratle A. Accelerating the convergence of evolutionary algorithms by fitness landscape approximation. In: Parallel problem solving from nature (PPSN V), 1998. p. 87–96.
93.
Regis RG, Shoemaker CA. Local function approximation in evolutionary algorithms for the optimization of costly functions. IEEE Trans Evol Comput. 2004;8(5):490–505.CrossRef
94.
Reza A, Vahid Z, Koorush Z. MLGA: a multilevel cooperative genetic algorithm. In: Proceedings of the IEEE 5th international conference on bio-inspired computing: theories and applications (BIC-TA), Changsha, China, Sept 2010. p. 271–277.
95.
Rosin C, Belew R. New methods for competitive coevolution. Evol Comput. 1997;15(1):1–29.CrossRef
96.
Rudolph G. Convergence analysis of canonical genetic algorithm. IEEE Trans Neural Netw. 1994;5(1):96–101.CrossRef
97.
Rudolph G. Finite Markov chain results in evolutionary computation: a tour d’horizon. Fundamenta Informaticae. 1998;35:67–89.MathSciNetMATH
98.
Rudolph G. Self-adaptive mutations may lead to premature convergence. IEEE Transa Evol Comput. 2001;5:410–4.CrossRef
99.
Salami M, Hendtlass T. A fast evaluation strategy for evolutionary algorithms. Appl Soft Comput. 2003;2(3):156–73.CrossRef
100.
Sastry K, Goldberg DE, Pelikan M. Don’t evaluate, inherit. In: Proceedings of genetic evolutionary computation conference (GECCO), San Francisco, CA, USA, July 2001. p. 551–558.
101.
Schmidt MD, Lipson H. Coevolution of fitness predictors. IEEE Trans Evol Comput. 2008;12(6):736–49.CrossRef
102.
Schutte JF, Reinbolt JA, Fregly BJ, Haftka RT, George AD. Parallel global optimization with the particle swarm algorithm. Int J Numer Methods Eng. 2004;61(13):2296–315.CrossRefMATH
103.
Shi Y, Krohling RA. Co-evolutionary particle swarm optimization to solve min-max problems. In: Proceedings of the congress on evolutionary computation (CEC), Honolulu, HI, May 2002, vol. 2. p. 1682–1687.
104.
Smith RE, Dike BA, Stegmann SA. Fitness inheritance in genetic algorithms. In: Proceedings of ACM symposium on applied computing, Nashville, Tennessee, USA, 1995. p. 345–350.
105.
Smith J, Vavak F. Replacement strategies in steady state genetic algorithms: static environments. In: Banzhaf W, Reeves C, editors. Foundations of genetic algorithms, vol. 5. CA: Morgan Kaufmann; 1999. p. 219–233.
106.
Stephens CR, Poli R. Coarse-grained dynamics for generalized recombination. IEEE Trans Evol Comput. 2007;11(4):541–57.CrossRef
107.
Stephens CR, Waelbroeck H. Schemata evolution and building blocks. Evol Comput. 1999;7:109–29.CrossRef
108.
Sudholt D. A new method for lower bounds on the running time of evolutionary algorithms. IEEE Trans Evol Comput. 2013;17(3):418–35.CrossRef
109.
Sudholt D. How crossover speeds up building-block assembly in genetic algorithms. Evol Comput 2016.
110.
Szumlanski SR, Wu AS, Hughes CE. Conflict resolution and a framework for collaborative interactive evolution. In: Proceedings of the 21st national conference on artificial intelligence (AAAI), Boston, Massachusetts, USA, July 2006. p. 512–517.
111.
Takagi H. Interactive evolutionary computation: fusion of the capacities of EC optimization and human evaluation. Proc IEEE. 2001;89(9):1275–96.CrossRef
112.
Tasoulis DK, Pavlidis NG, Plagianakos VP, Vrahatis MN. Parallel differential evolution. In: Proceedings of the IEEE congress on evolutionary computation, Portland, OR, USA, June 2004. p. 2023–2029.
113.
Thomsen R, Rickers P, Krink T. A religion-based spatial model for evolutionary algorithms. In: Proceedings of the 6th international conference on parallel problem solving from nature (PPSN VI), Paris, France, September 2000, vol. 1917 of Lecture Notes in Computer Science. London: Springer; 2000. p. 817–826.
114.
van den Bergh F, Engelbrecht A. A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput. 2004;8(3):225–39.CrossRef
115.
116.
Weber M, Neri F, Tirronen V. Distributed differential evolution with explorative-exploitative population families. Genetic Progr Evol Mach. 2009;10:343–471.CrossRef
117.
Whitley D, Starkweather T. GENITOR II: a distributed genetic algorithm. J Exp Theor Artif Intell. 1990;2(3):189–214.CrossRef
118.
Whitley D, Yoo NW. Modeling simple genetic algorithms for permutation problems. In: Whitley D, Vose M, editors. Foundations of genetic algorithms, vol. 3. San Mateo, CA: Morgan Kaufmann; 1995. p. 163–184.
119.
Wickramasinghe W, van Steen M, Eiben A. Peer-to-peer evolutionary algorithms with adaptive autonomous selection. In: Proceedings of the 9th annual conference on genetic and evolutionary computation (GECCO), London, U.K., July 2007. p. 1460–1467.
120.
Wong M-L, Cui G. Data mining using parallel multiobjective evolutionary algorithms on graphics hardware. In: Sobrevilla P, editors. Proceedings of IEEE world congress on computational intelligence, Barcelona, Spain, July 2010. p. 3815–3822.
121.
Wong M-L, Wong T-T, Fok K-L. Parallel evolutionary algorithms on graphics processing unit. In: Proceedings of the IEEE congress on evolutionary computation, Edinburgh, UK, Sept 2005. p. 2286–2293.
122.
Xu L, Zhang F. Parallel particle swarm optimization for attribute reduction. In: Proceedings of the 8th ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing, Qingdao, China, July 2007, vol. 1. p. 770–775.
123.
124.
Yao X, Liu Y. A new evolutionary system for evolving artificial neural networks. IEEE Trans Neural Netw. 1997;8(3):694–713.MathSciNetCrossRef
125.
Yuen SY, Cheung BKS. Bounds for probability of success of classical genetic algorithm based on Hamming distance. IEEE Trans Evol Comput. 2006;10(1):1–18.CrossRef
126.
Yu Y, Zhou Z-H. A new approach to estimating the expected first hitting time of evolutionary algorithms. Artif Intell. 2008;172(15):1809–32.MathSciNetCrossRefMATH
127.
Zhang C, Chen J, Xin B. Distributed memetic differential evolution with the synergy of Lamarckian and Baldwinian learning. Appl Soft Comput. 2013;13(5):2947–59.CrossRef
128.
Zhong W, Liu J, Xue M, Jiao L. A multiagent genetic Algorithm for global numerical optimization. IEEE Trans Syst Man Cybern Part B. 2004;34(2):1128–41.CrossRef
129.
Zhou Z, Ong YS, Nair PB, Keane AJ, Lum KY. Combining global and local surrogate models to accelerate evolutionary optimization. IEEE Trans Syst Man Cybern Part C. 2007;37(1):66–76.
Metadaten
Titel
Topics in Evolutinary Algorithms
verfasst von
Ke-Lin Du
M. N. S. Swamy
Copyright-Jahr
2016
Buch
Search and Optimization by Metaheuristics

Print ISBN: 978-3-319-41191-0

Electronic ISBN: 978-3-319-41192-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-41192-7_8

Premium Partner

    Bildnachweise