Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Basic Concepts of Cellular Automata Read chapter Read first chapter

2012 | OriginalPaper | Chapter

Selected Aspects of Natural Computing

Authors : David W. Corne, Kalyanmoy Deb, Joshua Knowles, Xin Yao

Published in: Handbook of Natural Computing

Publisher: Springer Berlin Heidelberg

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this chapter we will discuss a selection of application areas in which natural computation shows its value in real-world enterprises. For the purposes of demonstrating the significant impact and potential of natural computation in practice, there is certainly no shortage of documented examples that could be selected. We present just ten applications, ranging from specific problems to specific domains, and ranging from cases familiar to the authors to highlights known well in the general natural computation community. Each displays the proven promise or great potential of nature-inspired computation in high-profile and important real-world applications, and we hope that these applications inspire both students and practitioners.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter Natural Computing in Finance – A Review
next chapter Artificial Life
Literature
Allen F, Karjalainen R (1999) Using genetic algorithms to find technical trading rules. J Financial Econ 51:245–271CrossRef
Angeline PJ (1996) Genetic programming’s continued evolution. In: Angeline PJ, Kinnear K (eds) Advances in genetic programming, vol 2. MIT Press, Cambridge, pp 89–110
Arcuri A, Yao X (2007) A memetic algorithm for test data generation of object-oriented software. In: IEEE congress on evolutionary computation (CEC), Singapore, pp 2048–2055
Arcuri A, Yao X (2008) Search based software testing of object-oriented containers. Inf Sci 178:3075–3095CrossRef
Ayob M, Kendall G (2003) A Monte Carlo hyper-heuristic to optimise component placement sequencing for multi head placement machine. In: Proceedings of the international conference on intelligent technologies, Chiang Mai, Thailand, pp 132–141
Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic programming – An introduction: On the automatic evolution of computer programs and its applications. Morgan Kaufmann, San Francisco, CAMATH
Baresel A, Sthamer H, Schmidt M (2002) Fitness function design to improve evolutionary structural testing. In: Genetic and evolutionary computation conference (GECCO). Morgan Kaufmann, San Francisco, New York, CA, pp 1329–1336
Becker LA, Seshadri M (2003a) Comprehensibility and overfitting avoidance in genetic programming for technical trading rules. Computer Science Technical Report WPI-CS-TR-03–09. Worcester Polytechnic Institute, Worcester, Massachusetts, USA
Becker LA, Seshadri M (2003b) Cooperative coevolution of technical trading rules. Computer Science Technical Report WPI-CS-TR-03-15. Worcester Polytechnic Institute, Worcester, Massachusetts, USA
Becker LA, Seshadri M (2003c) GP-evolved technical trading rules can outperform buy and hold. In: Proceedings of sixth international conference on computational intelligence and natural computing, North Carolina, USA, 26–30 September 2003
Beizer B (1990) Software testing techniques. Van Nostrand Rheinhold, New York
Bilgin B, Ozcan E, Korkmaz EE (2006) An experimental study on hyper-heuristics and final exam scheduling. In: Proceedings of the 2006 international conference on the practice and theory of automated timetabling, Brno, Czech Republic, pp 123–140
Box GEP (1957) Evolutionary operation: A method for increasing industrial productivity. Appl Stat 6:81–101CrossRef
Box G, Hunter W, Hunter J (2005) Statistics for experimenters: design, innovation, and discovery, 2nd edn. Wiley, New YorkMATH
Boyapati C, Khurshid S, Marinov D (2002) Korat: Automated testing based on java predicates. In: Proceedings of the international symposium on software testing and analysis (ISSTA). ACM, New York
Brabazon A, O'Neill M (2005) Biologically inspired algorithms for financial modelling. Natural computing series. Springer, New York
Branke J, Deb K (2005) Integrating user preferences into evolutionary multi-objective optimization. In: Knowledge incorporation in evolutionary computation. Springer, New York, pp 461–477
Brockhoff D, Zitzler E (2006) Are all objectives necessary? On dimensionality reduction in evolutionary multiobjective optimization. In: Parallel problem solving from nature – PPSN IX. Lecture notes in computer science, vol 4193. Springer, New York, pp 533–542CrossRef
Burke EK, MacCarthy BL, Petrovic S, Qu R (2002) Knowledge discovery in a hyperheuristic for course timetabling using case based reasoning. In: Proceedings of the fourth international conference on the practice and theory of automated timetabling (PATAT’02) Gent, Belgium. Springer, Berlin
Burke EK, Kendall G, Newall J, Hart E, Ross P, Schulenburg S (2003) Hyper-heuristics an emerging direction in modern search technology. In: Glover F, Kochenberger GA (eds) Handbook of metaheuristics. Springer, New York, pp 457–474
Buy U, Orso A, Pezze M (2000) Automated testing of classes. In: Proceedings of the international symposium on software testing and analysis (ISSTA), pp 39–48
Chapman L, Thornes JE, Bradley AV (2002) Sky-view factor approximation using GPS receivers. Int J Climatol 22(5):615–621CrossRef
Chellapilla K, Fogel DB (1999a) Evolution, neural networks, games, and intelligence. Proc IEEE 87(9):1471–1496CrossRef
Chellapilla K, Fogel DB (1999b) Evolving neural networks to play checkers without expert knowledge. IEEE Trans Neural Netw 10(6):1382–1391CrossRef
Chellapilla K, Fogel DB (2001) Evolving an expert checkers playing program without using human expertise. IEEE Trans Evol Comput 5(4):422–428CrossRef
Chen SH (2002) Genetic algorithms and genetic programming in computational finance. Kluwer, Boston, MACrossRef
Chen SH, Yeh CH (1996) Toward a computable approach to the efficient market hypothesis: an application of genetic programming. J Econ Dyn Cont 21:1043–1063MathSciNetCrossRef
Cheng SL, Khai YL (2002) GP-based optimisation of technical trading indicators and profitability in FX market. In: Proceeding of the ninth international conference on neural information processing (ICONIP’02), vol 3, Singapore, pp 1159–1163
Chernoff H (1972) Sequential analysis and optimal design. SIAM monograph. SIAM, Philadelphia, PACrossRef
Coello C (2000) An updated survey of GA-based multiobjective optimization techniques. ACM Comput Surv (CSUR) 32(2):109–143CrossRef
Coello C (2006) Twenty years of evolutionary multi-objective optimization: A historical view of the field. IEEE Comput Intell Mag 1(1):28–36
Corne D, Jerram N, Knowles J, Oates M (2001) PESA-II: Region-based selection in evolutionary multiobjective optimization. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt H-M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of GECCO-2001: Genetic and evolutionary computation conference. Morgan Kaufmann, San Mateo, San Francisco, CA, pp 283–290
Corne D, Deb K, Fleming P, Knowles J (2003a) The good of the many outweighs the good of the one: Evolutionary multiobjective optimization. IEEE Connections Newsletter 1(1):9–13. ISSN 1543-4281
Corne D, Oates M, Kell D (2003b) Fitness gains and mutation patterns: Deriving mutation rates by exploiting landscape data. In: De Jong K, Poli R, Rowe J (eds) Foundations of genetic algorithms. Morgan Kaufmann, San Francisco, CA, pp 347–364
Cornford D, Thornes JE (1996) A comparison between spatial winter indices and expenditure on winter road maintenance in Scotland. Int J Climatol 16:339–357CrossRef
Cowling P, Kendall G, Soubeiga E (2000) A hyperheuristic approach to scheduling a sales summit. In: Burke EK, Erben W (eds) Practice and theory of automated timetabling III: Third international conference, PATAT 2000, Konstanz, Germany, August 2000, selected papers. LNCS, vol 2079. Springer, pp 176–190
Cowling P, Kendall G, Soubeiga E (2002) Hyperheuristics: A robust optimisation method applied to nurse scheduling. Technical Report NOTTCS-TR-2002-6. School of Computer Science & IT, University of Nottingham, Nottingham, England
Cross SE, Walker E (1994) Dart: Applying knowledge-based planning and scheduling to crisis action planning. In: Zweben M, Fox MS (eds) Intelligent scheduling. Morgan Kaufmann, San Francisco, CA
Datta R, Deb K (2009) A classical-cum-evolutionary multi-objective optimization for optimal machining parameters. In: Proceedings of NABIC. IEEE CIS Press
Davies ZS, Gilbert RJ, Merry RJ, Kell DB, Theodorou MK, Griffith GW (2000) Efficient improvement of silage additives by using genetic algorithms. Appl Environ Microbiol April:1435–1443
Deb K (1997) Mechanical component design using genetic algorithms. In: Dasgupta D, Michalewicz Z (eds) Evolutionary algorithms in engineering applications. Springer, New York, pp 495–512
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Meth Appl Mech Eng 1862(4):311–338CrossRef
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New YorkMATH
Deb K, Kumar A (1995) Real-coded genetic algorithms with simulated binary crossover: Studies on multi-modal and multi-objective problems. Complex Syst 9(6):431–454
Deb K, Srinivasan A (2005) Innovization: Innovation of design principles through optimization. KanGAL Report No. 2005007
Deb K, Srinivasan A (2006) Innovization: Innovating design principles through optimization. In: Proceedings of GECCO. ACM, New York, pp 1629–1636
Deb K, Agrawal S, Pratap A, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRef
Doong R, Frankl PG (1994) The ASTOOT approach to testing object-oriented programs. ACM Trans Softw Eng Methodol 3:101–130CrossRef
Ellims M, Bridges J, Ince DC (2006) The economics of unit testing. Emp Softw Eng 11(1):5–31CrossRef
Evans JRG, Edirisinghe MJ, Eames PVCJ (2001) Combinatorial searches of inorganic materials using the inkjet printer: Science philosophy and technology. J Eur Ceramic Soc 21:2291–2299CrossRef
Fang H-L, Ross PM, Corne D (1994) A promising hybrid GA/heuristic approach for open-shop scheduling problems. In: Cohn A (ed) Proceedings of ECAI 94: 11th European conference on artificial intelligence. Wiley, Amsterdam, The Netherlands, pp 590–594
Farnsworth GV, Kelly JA, Othling AS, Pryor RJ (2004) Successful technical trading agents using genetic programming. SANDIA Report SAND2004–4774. SANDIA National Laboratories, California
Fisher R (1971) The design of experiments, 9th edn. Macmillan, New York
Fogel D (1998) Evolutionary computation. The fossil record. Selected readings on the history of evolutionary computation. IEEE Press, Piscataway, New Jersey, USA
Fogel DB (2002) Blondie24: Playing at the edge of AI. Morgan Kaufmann, San Francisco. CA, ISBN 1-55860-783-8
Fogel DB, Hays TJ, Hahn SL, Quon J (2004) A self-learning evolutionary chess program. Proc IEEE 92(12):1947–1954CrossRef
Fogel DB, Hays TJ, Hahn SL, Quon J (2006) The Blondie25 chess program competes against Fritz 8.0 and a human chess master. In: Louis S, Kendall G (eds) Proceedings of 2006 IEEE symposium on computational intelligence & games. IEEE, Reno, pp 230–235CrossRef
Fonseca C, Fleming P (1995) An overview of evolutionary algorithms in multiobjective optimization. Evol Comput 3(1):1–16CrossRef
Fonseca C, Fleming P (1998) Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation. IEEE Trans Syst, Man, Cybernetics – Part A 28(1):26–37CrossRef
Fukunaga A (2008) Automated discovery of local search heuristics for satisfiability testing. Evol Comput 16(1):31–61CrossRef
Fyfe C, Marney JP, Tarbert H (1999) Technical trading versus market efficiency: A genetic programming approach. Appl Finan Econ 9:183–191CrossRef
Gent IP, Walsh T (1993) Towards an understanding of hill-climbing procedures for SAT. In: Proceedings of AAAI’93. AAAI Press/MIT Press, Menlo Park, pp 28–33
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Boston, MAMATH
Goldberg DE (2002) The design of innovation: Lessons from and for competent genetic algorithms. Kluwer, Boston, MAMATH
Gratch J, Chein S, de Jong G (1993) Learning search control knowledge for Deep Space network scheduling. In: Proceedings of the tenth international conference on machine learning, Amherst, MA, pp 135–142
Grefenstette JJ (1988) Credit assignment in rule discovery systems based on genetic algorithms. Mach Learn 3:225–246
Handa H, Chapman L, Yao X (2005) Dynamic salting route optimisation using evolutionary computation. In: Proceedings of the 2005 congress on evolutionary computation, Edinburgh, Scotland, vol 1, pp 158–165CrossRef
Handa H, Chapman L, Yao X (2006) Robust route optimization for gritting/salting trucks: A CERCIA experience. IEEE Comput Intell Mag February:6–9
Harman M, Hu L, Hierons R, Baresel A, Sthamer H (2002) Improving evolutionary testing by flag removal. In: Genetic and evolutionary computation conference (GECCO). Morgan Kaufmann, San Francisco, New York, CA, pp 1351–1358
Hart E, Ross PM (1998) A heuristic combination method for solving job-shop scheduling problems. In: Eiben AE, Back T, Schoenauer M, Schwefel H-P (eds) Parallel problem solving from nature V. LNCS, vol 1498. Springer, Berlin, pp 845–854
Hart E, Ross PM, Nelson J (1998) Solving a real-world problem using an evolving heuristically driven schedule builder. Evol Comput 6(1):61–80CrossRef
Holland JH (1992) Adaptation in natural and artificial systems. MIT Press, Cambridge, MA, USA
Holland JH, Holyoak KJ, Nisbett RE, Thagard PR (1986) Induction: Processes of inference, learning, and discovery. MIT Press, Cambridge
Hornby GS, Globus A, Linden DS, Lohn JD (2006) Automated antenna design with evolutionary algorithms. In: AIAA Space, San Jose, CA
Kannan BK, Kramer SN (1994) An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. ASME J Mech Des 116(2):405–411CrossRef
Kerlogue F, Zanetini F (2004) Batik: Design, style and history. Thames and Hudson, London
Knight CG, Platt M, Rowe W, Wedge DC, Khan F, Day PJ, McShea A, Knowles J, Kell DB (2008) Array-based evolution of DNA aptamers allows modelling of an explicit sequence-fitness landscape. Nucleic Acids Research November:e6
Knowles J (2006) ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans Evol Comput 10(1):50–66CrossRef
Knowles JD (2009) Closed-loop evolutionary multiobjective optimization. IEEE Comput Intell Mag August:77–91
Korel B (1990) Automated software test data generation. IEEE Trans Softw Eng 16:870–879CrossRef
Koza JR (1992) Genetic programming: On the programming of computers by means of natural selection. MIT Press, CambridgeMATH
Li Y, Hu CJ, Yao X (2009) Innovative Batik design with an interactive evolutionary art system. J Comput Sci Technol 24(6):1035–1047CrossRef
Liu X, Wang B, Liu H (2005) Evolutionary search in the context of object oriented programs. In: MIC2005: The sixth metaheuristics international conference, Vienna, Austria
Lo AW, Mamaysky H, Wang J (2000) Foundations of technical analysis: Computational algorithms, statistical-inference, and empirical implementation. J Finance 55:1705–1770CrossRef
Lohpetch D, Corne D (2009) Discovering effective technical trading rules with genetic programming: Towards robustly outperforming buy-and-hold. In: World congress on nature and biologically inspired computing (NABIC). IEEE Press
Lohpetch D, Corne D (2010) Outperforming buy-and-hold with evolved technical trading rules: Daily, weekly and monthly trading. In: EvoApplications. Proceedings of EvoStar 2010. LNCS, vol 6025. Springer pp
Lutton E (2006) Evolution of fractal shapes for artists and designers. Int J Artif Intell Tools 15(4):651–672CrossRef
Marinov D, Khurshid S (2001) TestEra: A novel framework for testing java programs. In: IEEE international conference on automated software engineering (ASE), San Diego, California, USA. Kluwer, The Netherlands.
Marney JP, Miller D, Fyfe C, Tarbert H (2000) Technical analysis versus market efficiency: A genetic programming approach. Computing in Economics and Finance, Society for Computational Economics, Barcelona, Spain (paper #169)
Marney JP, Fyfe C, Tarbert H, Miller D (2001) Risk adjusted returns to technical trading rules: A genetic programming approach. Computing in Economics and Finance, Society for Computational Economics, Yale University, USA
Marney JP, Tarbert H, Fyfe C (2005) Risk adjusted returns from technical trading: A genetic programming approach. Appl Financial Econ 15: 1073–1077CrossRef
McAllester D, Selman B, Kautz H (1997) Evidence for invariants in local search. In: Proceedings of the 14th national conference on artificial intelligence. AAAI Press/MIT Press, Menlo Park, Providence, Rhode Island, USA, pp 321–326
McMinn P (2004) Search-based software test data generation: A survey. Softw Test Verif Reliab 14(2):105–156CrossRef
McMinn P, Holcombe M (2003) The state problem for evolutionary testing. In: Genetic and evolutionary computation conference (GECCO), Chicago, Illinois, USA, pp 2488–2500
McMinn P, Holcombe M (2004) Hybridizing evolutionary testing with the chaining approach. In: Genetic and evolutionary computation conference (GECCO), Seattle, Washington, USA, pp 1363–1374
McMinn P, Holcombe M (2005) Evolutionary testing of state-based programs. In: Genetic and evolutionary computation conference (GECCO), Washington, DC, USA, pp 1013–1020
Messac A, Mattson CA (2004) Normal constraint method with guarantee of even representation of complete Pareto frontier. AIAA J 42(10):2101–2111CrossRef
Miettinen K (1999) Nonlinear multiobjective optimization. Springer, New YorkMATH
Minton S (1988) Learning search control knowledge: An explanation-based approach. Kluwer Academic Publishers Norwell, MA, USA
Murphy JJ (1999) Technical analysis of the financial markets. New York Institute of Finance, New York
Myers G (1979) The art of software testing. Wiley, New York
Myers R, Montgomery D (1995) Response surface methodology: Process and product optimization using designed experiments. Wiley, New YorkMATH
Nagata Y, Kobayashi S (1997) Edge assembly crossover: A high-power genetic algorithm for the traveling salesman problem. In: Proceedings of the seventh international conference on genetic algorithms, East Lansing, Michigan, USA, pp 450–457
Neely C (2001) Risk-adjusted, ex ante, optimal technical trading rules in equity markets. Working Papers 99-015D, Revised August 2001, Federal Reserve Bank of St. Louis
Nolfi S, Floreano D (2004) Evolutionary robotics: The biology, intelligence, and technology of self-organizing machines. Bradford Book. MIT Press, Cambridge
O’Hagan S, Dunn WB, Brown M, Knowles JD, Kell DB (2005) Closed-loop, multiobjective optimization of analytical instrumentation: Gas chromatography/time-off light mass spectrometry of the metabolomes of human serum and of yeast fermentations. Anal Chem 77(1):290–303CrossRef
O’Hagan S, Dunn W, Knowles J, Broadhurst D, Williams R, Ashworth J, Cameron M, Kell D (2007) Closed-loop, multiobjective optimization of two-dimensional gas chromatography/mass spectrometry for serum metabolomics. Anal Chem 79(2):464–476CrossRef
Özcan E, Bilgin B, Korkmaz EE (2008) A comprehensive analysis of hyper-heuristics. Intell Data Anal 12(1):3–23
Potvin JY, Soriano P, Vallée M (June 2004) Generating trading rules on the stock markets with genetic programming. Comput Oper Res 31(7):1033–1047CrossRefMATH
Pring MJ (1980) Technical analysis explained. McGraw-Hill, New York
Rechenberg I (1965) Cybernetic solution path of an experimental problem. Royal Aircraft Establishment, Library Translation 1122, Farnborough, Hampshire, UK
Rechenberg I (2000) Case studies in evolutionary experimentation and computation. Comput Meth Appl Mech Eng 186(2–4):125–140CrossRefMATH
Reklaitis GV, Ravindran A, Ragsdell KM (1983) Engineering optimization methods and applications. Wiley, New York
Romero J, Machado P (2008) The art of artificial evolution: a handbook on evolutionary art and music. Springer, Heidelberg
Ross P, Hart E, Corne D (1997) Some observations about GA-based exam timetabling. In: Burke EK, Carter M (eds) Practice and theory of automated timetabling II: Second international conference, PATAT 1997, Toronto, Canada, August 1997, selected papers. LNCS, vol 1408. Springer, pp 115–129
Ross P, Schulenburg S, Marín-Blázquez JG, Hart E (2002) Hyper-heuristics: Learning to combine simple heuristics in bin-packing problems. In: Genetic and evolutionary computation conference (GECCO 2002), New York
Ross P, Marín-Blázquez JG, Schulenburg S, Hart E (2003) Learning a procedure that can solve hard bin-packing problems: A new GA-based approach to hyper-heuristics. In: Proceedings of the genetic and evolutionary computation conference (GECCO 2003). Lecture notes in computer science, vol 2723. Springer, Chicago, Illinois, USA, pp 1295–1306
Ruggiero MA (1997) Cybernetic trading strategies. Wiley, New York
Russell SJ, Norvig P (2003) Artificial intelligence: A modern approach, 2nd edn. Prentice Hall, Upper Saddle River, NJ, pp 163–171
Sagarna R, Arcuri A, Yao X (2007) Estimation of distribution algorithms for testing object oriented software. In: IEEE congress on evolutionary computation (CEC), Singapore, pp 438–444
Schaeffer J (1996) One jump ahead: Challenging human supremacy in checkers. Springer, New York, p 97, 447
Schaeffer J, Lake R, Lu P, Bryant M (1996) Chinook: The world man–machine checkers champion. AI Mag 17:21–29
Seesing A (2006) Evotest: Test case generation using genetic programming and software analysis. Master’s thesis, Delft University of Technology
Selman B, Levesque HJ, Mitchell DG (1992) A new method for solving hard satisfiability problems. In: Tenth AAAI, San Jose, pp 440–446
Selman B, Kautz HA, Cohen B (1994) Noise strategies for improving local search. In: Proceedings of the 12th national conference on artificial intelligence. AAAI Press/MIT Press, Menlo Park, Seattle, Washington, USA, pp 337–343
Sharpe WF (1966) Mutual fund performance. J Business 39(S1):119–138. doi:10.1086/294846CrossRef
Shaw RL (1998) Fighter combat: Tactics and maneuvering. United States Naval Institute Press, Annapolis, Maryland, USA
Sims K (1991) Artificial evolution for computer graphics. In: Proceedings of the 18th annual conference on computer graphics and interactive techniques (SIGGRAPH 1991). ACM, New York, Las Vegas, Nevada, USA, pp 319–328CrossRef
Smith RE, Dike BA (1995) Learning novel fighter combat maneuver rules via genetic algorithms. Int J Expert Syst 8(3):247–276
Smith RE, Dike BA, Mehra RK, Ravichandran B, El-Fallah A (2000) Classifier systems in combat: Two-sided learning of maneuvers for advanced fighter aircraft. Comput Meth Appl Mech Eng 186:431–437CrossRef
Smith RE, Dike BA, Ravichandran B, El-Fallah A, Mehra RK (2002) Discovering novel fighter combat maneuvers: Simulating test pilot creativity. In: Bentley P, Corne D (eds) Creative evolutionary systems. Morgan Kaufmann, San Francisco, CA, pp 467–486CrossRef
Tassey G (2002) The economic impacts of inadequate infrastructure for software testing. Final Report. National Institute of Standards and Technology
Terashima-Marín H, Ross PM, Valenzuela-Rendón M (1999) Evolution of constraint satisfaction strategies in examination timetabling. In: Banzhaf W et al. (eds) Proceedings of the GECCO-99 genetic and evolutionary computation conference, Orlando, Florida. Morgan Kaufmann, San Francisco, pp 635–642
Thompson A, Layzell P (1999) Analysis of unconventional evolved electronics. Commun ACM 42(4):71–79CrossRef
Tonella P (2004) Evolutionary testing of classes. In: Proceedings of the international symposium on software testing and analysis (ISSTA), pp 119–128
Trianni V, Nolfi S, Dorigo M (2006) Cooperative hole avoidance in a swarm-bot. Robot Autonomous Syst 54(2):97–103CrossRef
Tuerk C, Gold L (1990) Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science 249(4968):505CrossRef
Visser W, Pasareanu CS, Khurshid S (2004) Test input generation with java pathfinder. In: Proceedings of the international symposium on software testing and analysis (ISSTA), Boston, Massachusetts, USA
Visser W, Pasareanu CS, Pelánek R (2006) Test input generation for java containers using state matching. In: Proceedings of the international symposium on software testing and analysis (ISSTA), Portland, Maine, USA, pp 37–48
Wang Y, Tan T, Zhu Y (2000) Face verification based on singular value decomposition and radial basis function neural network. In: Proceedings of fourth Asian conference on computer vision, Taiwan, pp 432–436
Wang SF, Wang S, Takagi H (2006) User fatigue reduction by an absolute rating data-trained predictor in IEC. In: Proceedings of 2006 congress on evolutionary computation, pp 2195–2200
Wappler S, Wegener J (2006) Evolutionary unit testing of object-oriented software using strongly-typed genetic programming. In: Genetic and evolutionary computation conference (GECCO), Seattle, Washington, USA, pp 1925–1932
Wedge D, Rowe W, Kell D, Knowles J (2009) In silico modelling of directed evolution: Implications for experimental design and stepwise evolution. J Theor Biol 257:131–141CrossRef
Wilson S (1998) Generalisation in the XCS classifier system. In: Koza J (ed) Proceedings of the third genetic programming conference, Madison, Wisconsin. Morgan Kaufmann, San Francisco, CA, USA, pp 665–674
Xie T, Marinov D, Notkin D (2004) Rostra: A framework for detecting redundant object-oriented unit tests. In: IEEE international conference on automated software engineering (ASE), Linz, Austria. IEEE Computer Society, Washington, DC, pp 196–205
Xie T, Marinov D, Schulte W, Notkin D (2005) Symstra: A framework for generating object-oriented unit tests using symbolic execution. In: Proceedings of the 11th international conference on tools and algorithms for the construction and analysis of systems, Edinburgh, UK, pp 365–381CrossRef
Metadata
Title
Selected Aspects of Natural Computing
Authors
David W. Corne
Kalyanmoy Deb
Joshua Knowles
Xin Yao
Copyright Year
2012
Publisher
Springer Berlin Heidelberg
Book
Handbook of Natural Computing

Print ISBN: 978-3-540-92909-3

Electronic ISBN: 978-3-540-92910-9

Copyright Year: 2012

DOI
https://doi.org/10.1007/978-3-540-92910-9_52

Premium Partner

    Image Credits