Skip to main content
SpringerLink
Log in

Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining

  • Chapter
Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8401))

Abstract

Evolutionary Algorithms (EAs) are a fascinating branch of computational intelligence with much potential for use in many application areas. The fundamental principle of EAs is to use ideas inspired by the biological mechanisms observed in nature, such as selection and genetic changes, to find the best solution for a given optimization problem. Generally, EAs use iterative processes, by growing a population of solutions selected in a guided random search and using parallel processing, in order to achieve a desired result. Such population based approaches, for example particle swarm and ant colony optimization (inspired from biology), are among the most popular metaheuristic methods being used in machine learning, along with others such as the simulated annealing (inspired from thermodynamics). In this paper, we provide a short survey on the state-of-the-art of EAs, beginning with some background on the theory of evolution and contrasting the original ideas of Darwin and Lamarck; we then continue with a discussion on the analogy between biological and computational sciences, and briefly describe some fundamentals of EAs, including the Genetic Algorithms, Genetic Programming, Evolution Strategies, Swarm Intelligence Algorithms (i.e., Particle Swarm Optimization, Ant Colony Optimization, Bacteria Foraging Algorithms, Bees Algorithm, Invasive Weed Optimization), Memetic Search, Differential Evolution Search, Artificial Immune Systems, Gravitational Search Algorithm, Intelligent Water Drops Algorithm. We conclude with a short description of the usefulness of EAs for Knowledge Discovery and Data Mining tasks and present some open problems and challenges to further stimulate research.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Box, G.E.: Evolutionary operation: A method for increasing industrial productivity. Applied Statistics 6(2), 81–101 (1957)

    Article  Google Scholar 

  2. Holland, J.H.: Outline for a Logical Theory of Adaptive Systems. J. ACM 9(3), 297–314 (1962)

    Article  MATH  Google Scholar 

  3. Hendy, M.D., Penny, D.: Branch an Bound Algorithms to determine minimal Evolutionary Trees. Mathematical Biosciences 59(2), 277–290 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  4. Letunic, I., Bork, P.: Interactive Tree Of Life (iTOL): An online tool for phylogenetic tree display and annotation. Bioinformatics 23(1), 127–128 (2007)

    Article  Google Scholar 

  5. Darwin, C.: On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life. John Murray, London (1859)

    Google Scholar 

  6. Hoffmeister, F.: Scalable Parallelism by Evolutionary Algorithms. Lecture Notes in Economics and Mathematical Systems 367, 177–198 (1991)

    Article  MATH  Google Scholar 

  7. Cheng, A., Lim, C.C.: Optimizing System-On-Chip verifications with multi-objective genetic evolutionary algorithms. Journal of Industrial and Management Optimization 10(2), 383–396 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  8. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)

    Article  Google Scholar 

  9. Waddington, C.H.: Canalization of development and the inheritance of acquired characters. Nature 150(3811), 563–565 (1942)

    Article  Google Scholar 

  10. Trygve, T.: Handbook of Epigenetics. Academic Press, San Diego (2011)

    Google Scholar 

  11. Fitch, W.M., Margoliash, E.: Construction of phylogenetic trees. Science 155(760), 279–284 (1967)

    Article  Google Scholar 

  12. Saitou, N., Nei, M.: The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4(4), 406–425 (1987)

    Google Scholar 

  13. Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., Held, P.: Computational Intelligence: A Methodological Introduction. Springer, Heidelberg (2013)

    Book  MATH  Google Scholar 

  14. Lollini, P.-L., Motta, S., Pappalardo, F.: Discovery of cancer vaccination protocols with a genetic algorithm driving an agent based simulator. BMC Bioinformatics 7(1), 352 (2006)

    Article  Google Scholar 

  15. Ritchie, M.D., Motsinger, A.A., Bush, W.S., Coffey, C.S., Moore, J.H.: Genetic programming neural networks: A powerful bioinformatics tool for human genetics. Applied Soft Computing 7(1), 471–479 (2007)

    Article  Google Scholar 

  16. Winstein, K., Balakrishnan, H.: TCP ex Machina: Computer-Generated Congestion Control. In: ACM SIGCOMM, pp. 123–134. ACM (2013)

    Google Scholar 

  17. Gen, M., Cheng, R.: Genetic algorithms and engineering optimization. John Wiley & Sons (2000)

    Google Scholar 

  18. Rafael, B., Oertl, S., Affenzeller, M., Wagner, S.: Music segmentation with genetic algorithms. In: 20th International Workshop on Database and Expert Systems Application, DEXA 2009, pp. 256–260. IEEE (2009)

    Google Scholar 

  19. Soupios, P., Akca, I., Mpogiatzis, P., Basokur, A., Papazachos, C.: Application of Genetic Algorithms in Seismic Tomography. In: EGU General Assembly Conference Abstracts, p. 1555 (2010)

    Google Scholar 

  20. Waddington, C.H.: Epigenetics and Evolution. Symposia of the Society for Experimental Biology 7, 186–199 (1953)

    Google Scholar 

  21. Jablonka, E.: Epigenetic inheritance and evolution: The Lamarckian dimension. Oxford University Press, Oxford (1999)

    Google Scholar 

  22. Tapscott, D.: Grown Up Digital: How the Net Generation is Changing Your World HC. Mcgraw-Hill (2008)

    Google Scholar 

  23. Bird, A.: Perceptions of epigenetics. Nature 447(7143), 396–398 (2007)

    Article  Google Scholar 

  24. Handel, A., Ramagopalan, S.: Is Lamarckian evolution relevant to medicine? BMC Medical Genetics 11(1), 73 (2010)

    Article  Google Scholar 

  25. Kiberstis, P.A.: All Eyes on Epigenetics. Science 335(6069), 637 (2012)

    Article  Google Scholar 

  26. Emmert-Streib, F., de Matos Simoes, R., Glazko, G., McDade, S., Haibe-Kains, B., Holzinger, A., Dehmer, M., Campbell, F.: Functional and genetic analysis of the colon cancer network. BMC Bioinformatics 15(suppl. 6), S6 (2014)

    Google Scholar 

  27. Freitas, A.A.: A survey of evolutionary algorithms for data mining and knowledge discovery. In: Advances in Evolutionary Computing, pp. 819–845. Springer (2003)

    Google Scholar 

  28. Coello Coello, C.A., Lechuga, M.S.: MOPSO: A proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), pp. 1051–1056. IEEE (2002)

    Google Scholar 

  29. Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Ruß, G., Steinbrecher, M.: Computational Intelligence: Eine methodische Einführung in Künstliche Neuronale Netze, Evolutionäre Algorithmen, Fuzzy-Systeme und Bayes-Netze Vieweg+Teubner, Wiesbaden (2011)

    Google Scholar 

  30. Jansen, T.: Analyzing evolutionary algorithms: The computer science perspective. Springer Publishing Company (2013) (incorporated)

    Google Scholar 

  31. Yu, X., Gen, M.: Introduction to evolutionary algorithms. Springer, Heidelberg (2010)

    Google Scholar 

  32. Eiben, A.E., Smith, J.E.: Introduction to evolutionary computing. Springer, Berlin (2010)

    Google Scholar 

  33. De Jong, K.A.: Evolutionary computation: A unified approach. MIT press, Cambridge (2006)

    Google Scholar 

  34. Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)

    Article  Google Scholar 

  35. Mitchell, T.M.: Machine learning, p. 267. McGraw-Hill, Boston (1997)

    MATH  Google Scholar 

  36. Forrest, S.: Genetic algorithms: Principles of natural selection applied to computation. Science 261(5123), 872–878 (1993)

    Article  Google Scholar 

  37. Grefenstette, J.J.: Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems Man and Cybernetics 16(1), 122–128 (1986)

    Article  Google Scholar 

  38. Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Reading, MA (1989)

    Google Scholar 

  39. Mitchell, M.: An Introduction to Genetic Algorithms (Complex Adaptive Systems). MIT Press, Cambridge (1998)

    Google Scholar 

  40. Coley, D.A.: An introduction to Genetic Algorithms for Scientists and Engineers. World Scientific Publishing, Singapore (1999)

    Book  Google Scholar 

  41. Koza, J.R.: Genetic programming as a means for programming computers by natural selection. Statistics and Computing 4(2), 87–112 (1994)

    Article  Google Scholar 

  42. Brameier, M., Banzhaf, W.: A comparison of linear genetic programming and neural networks in medical data mining. IEEE Transactions on Evolutionary Computation 5(1), 17–26 (2001)

    Article  MATH  Google Scholar 

  43. Bäck, T., Hoffmeister, F., Schwefel, H.P.: A survey of evolution strategies. In: Proceedings of the 4th International Conference on Genetic Algorithms, pp. 2-9 (1991)

    Google Scholar 

  44. Ohkura, K., Matsumura, Y., Ueda, K.: Robust evolution strategies. Applied Intelligence 15(3), 153–169 (2001)

    Article  MATH  Google Scholar 

  45. Huhse, J., Zell, A.: Evolution Strategy with Neighborhood Attraction–A Robust Evolution Strategy. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2001, pp. 1026–1033 (2001)

    Google Scholar 

  46. Auger, A., Hansen, N.: Theory of Evolution Strategies: A new perspective. In: Auger, A., Doerr, B. (eds.) Theory of Randomized Search Heuristics: Foundations and Recent Developments, pp. 289–325. World Scientific Publishing, Singapore (2011)

    Chapter  Google Scholar 

  47. Beyer, H.-G., Schwefel, H.-P.: Evolution strategies–A comprehensive introduction. Natural Computing 1(1), 3–52 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  48. Beyer, H.-G.: The theory of evolution strategies. Springer, Heidelberg (2001)

    Google Scholar 

  49. Martens, D., Baesens, B., Fawcett, T.: Editorial survey: Swarm intelligence for data mining. Mach. Learn. 82(1), 1–42 (2011)

    Article  MathSciNet  Google Scholar 

  50. Franks, N.R., Pratt, S.C., Mallon, E.B., Britton, N.F., Sumpter, D.J.T.: Information flow, opinion polling and collective intelligence in house-hunting social insects. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 357(1427), 1567–1583 (2002)

    Article  Google Scholar 

  51. Kennedy, J.F., Eberhart, R.C.: Swarm intelligence. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  52. van’t Woud, J., Sandberg, J., Wielinga, B.J.: The Mars crowdsourcing experiment: Is crowdsourcing in the form of a serious game applicable for annotation in a semantically-rich research domain? In: 2011 16th International Conference on Computer Games (CGAMES), pp. 201–208. IEEE (2011)

    Google Scholar 

  53. Woolley, A.W., Chabris, C.F., Pentland, A., Hashmi, N., Malone, T.W.: Evidence for a Collective Intelligence Factor in the Performance of Human Groups. Science 330(6004), 686–688 (2010)

    Article  Google Scholar 

  54. Bonabeau, E.: Decisions 2.0: The power of collective intelligence. MIT Sloan Management Review 50(2), 45–52 (2009)

    Google Scholar 

  55. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: IEEE Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995 (1995)

    Google Scholar 

  56. Eberhart, R., Simpson, P., Dobbins, R.: Computational intelligence PC tools. Academic Press Professional, Inc. (1996)

    Google Scholar 

  57. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, pp. 4104–4108. IEEE (1997)

    Google Scholar 

  58. Eberhart, R.C., Shi, Y.H.: Particle swarm optimization: Developments, applications and resources. IEEE, New York (2001)

    Google Scholar 

  59. Sim, K.M., Sun, W.H.: Ant colony optimization for routing and load-balancing: Survey and new directions. IEEE Trans. Syst. Man Cybern. Paart A-Syst. Hum. 33(5), 560–572 (2003)

    Article  Google Scholar 

  60. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of the First European Conference on Artificial Life, vol. 142, pp. 134–142 (1991)

    Google Scholar 

  61. Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)

    Article  Google Scholar 

  62. Dorigo, M., Blum, C.: Ant colony optimization theory: A survey. Theoretical Computer Science 344(2-3), 243–278 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  63. Colorni, A., Dorigo, M., Maniezzo, V.: An Investigation of some Properties of an “Ant Algorithm”. In: Parallel Problem Solving from Nature Conference (PPSN), pp. 509–520. Elsevier (1992)

    Google Scholar 

  64. Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization - Artificial ants as a computational intelligence technique. IEEE Computational Intelligence Magazine 1(4), 28–39 (2006)

    Article  Google Scholar 

  65. Chen, Y.J., Wong, M.L., Li, H.B.: Applying Ant Colony Optimization to configuring stacking ensembles for data mining. Expert Systems with Applications 41(6), 2688–2702 (2014)

    Article  Google Scholar 

  66. Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation 6(4), 321–332 (2002)

    Article  MATH  Google Scholar 

  67. de Boer, P.A.J.: Advances in understanding E. coli cell fission. Current Opinion in Microbiology 13(6), 730–737 (2010)

    Article  Google Scholar 

  68. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems 22(3), 52–67 (2002)

    Article  MathSciNet  Google Scholar 

  69. Kim, D.H., Abraham, A., Cho, J.H.: A hybrid genetic algorithm and bacterial foraging approach for global optimization. Inf. Sci. 177(18), 3918–3937 (2007)

    Article  Google Scholar 

  70. Tripathy, M., Mishra, S., Lai, L.L., Zhang, Q.P.: Transmission loss reduction based on FACTS and bacteria foraging algorithm. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 222–231. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  71. Pirolli, P., Card, S.: Information foraging. Psychological Review 106(4), 643–675 (1999)

    Article  Google Scholar 

  72. Pirolli, P.: Rational analyses of information foraging on the Web. Cognitive Science 29(3), 343–373 (2005)

    Article  Google Scholar 

  73. Liu, J.M., Zhang, S.W., Yang, J.: Characterizing Web usage regularities with information foraging agents. IEEE Transactions on Knowledge and Data Engineering 16(5), 566–584 (2004)

    Article  Google Scholar 

  74. Goodwin, J.C., Cohen, T., Rindflesch, T.: Discovery by scent: Discovery browsing system based on the Information Foraging Theory. In: Gao, J., Dubitzky, W., Wu, C., Liebman, M., Alhaij, R., Ungar, L., Christianson, A., Hu, X. (eds.) 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops. IEEE, New York (2012)

    Google Scholar 

  75. Pham, D., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The bees algorithm-a novel tool for complex optimisation problems. In: Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), pp. 454–459 (2006)

    Google Scholar 

  76. Pham, D.T., Castellani, M.: The Bees Algorithm: Modelling foraging behaviour to solve continuous optimization problems. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science 223(12), 2919–2938 (2009)

    Article  Google Scholar 

  77. Ozbakir, L., Baykasoglu, A., Tapkan, P.: Bees algorithm for generalized assignment problem. Applied Mathematics and Computation 215(11), 3782–3795 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  78. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization 39(3), 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  79. Tsai, H.C.: Integrating the artificial bee colony and bees algorithm to face constrained optimization problems. Inf. Sci. 258, 80–93 (2014)

    Article  MathSciNet  Google Scholar 

  80. Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics 1(4), 355–366 (2006)

    Article  Google Scholar 

  81. Giri, R., Chowdhury, A., Ghosh, A., Das, S., Abraham, A., Snasel, V.: A Modified Invasive Weed Optimization Algorithm for training of feed- forward Neural Networks. In: 2010 IEEE International Conference on Systems, Man and Cybernetics (SMC 2010), pp. 3166–3173 (2010)

    Google Scholar 

  82. Huang, H., Ding, S., Zhu, H., Xu, X.: Invasive Weed Optimization Algorithm for Optimizating the Parameters of Mixed Kernel Twin Support Vector Machines. Journal of Computers 8(8) (2013)

    Google Scholar 

  83. Dawkins, R.: The selfish gene. Oxford University Press, Oxford (1976)

    Google Scholar 

  84. Corno, F., Reorda, M.S., Squillero, G.: The selfish gene algorithm: a new evolutionary optimization strategy. In: Proceedings of the 1998 ACM Symposium on Applied Computing, pp. 349–355. ACM (1998)

    Google Scholar 

  85. Ishibuchi, H., Yoshida, T., Murata, T.: Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation 7(2), 204–223 (2003)

    Article  Google Scholar 

  86. Simpson, G.G.: The Baldwin Effect. Evolution 7(2), 110–117 (1953)

    Article  Google Scholar 

  87. Kannan, S.S., Ramaraj, N.: A novel hybrid feature selection via Symmetrical Uncertainty ranking based local memetic search algorithm. Knowledge-Based Systems 23(6), 580–585 (2010)

    Article  Google Scholar 

  88. Molina, D., Lozano, M., Sanchez, A.M., Herrera, F.: Memetic algorithms based on local search chains for large scale continuous optimisation problems: MA-SSW-Chains. Soft Computing 15(11), 2201–2220 (2011)

    Article  Google Scholar 

  89. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey Wolf Optimizer. Advances in Engineering Software 69, 46–61 (2014)

    Article  Google Scholar 

  90. Yin, J., Meng, H.: Artificial bee colony algorithm with chaotic differential evolution search. Computer Engineering and Applications 47(29), 27–30 (2011)

    Google Scholar 

  91. Jia, D.L., Zheng, G.X., Khan, M.K.: An effective memetic differential evolution algorithm based on chaotic local search. Inf. Sci. 181(15), 3175–3187 (2011)

    Article  Google Scholar 

  92. Farmer, J.D., Packard, N.H., Perelson, A.S.: The immune system, adaptation, and machine learning. Physica D: Nonlinear Phenomena 22(1), 187–204 (1986)

    Article  MathSciNet  Google Scholar 

  93. Parham, P.: The Immune System, 3rd edn. Garland Science, Taylor and Francis, New York (2009)

    Google Scholar 

  94. Dasgupta, D., Yu, S.H., Nino, F.: Recent Advances in Artificial Immune Systems: Models and Applications. Applied Soft Computing 11(2), 1574–1587 (2011)

    Article  Google Scholar 

  95. de Castro, L.N., Timmis, J.I.: Artificial immune systems as a novel soft computing paradigm. Soft Computing 7(8), 526–544 (2003)

    Article  Google Scholar 

  96. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: A Gravitational Search Algorithm. Inf. Sci. 179(13), 2232–2248 (2009)

    Article  MATH  Google Scholar 

  97. Dowlatshahi, M.B., Nezamabadi-Pour, H., Mashinchi, M.: A discrete gravitational search algorithm for solving combinatorial optimization problems. Inf. Sci. 258, 94–107 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  98. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: BGSA: Binary gravitational search algorithm. Natural Computing 9(3), 727–745 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  99. Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)

    Article  Google Scholar 

  100. Shah-Hosseini, H.: The intelligent water drops algorithm: A nature-inspired swarm-based optimization algorithm. International Journal of Bio-Inspired Computation 1(1-2), 71–79 (2009)

    Article  Google Scholar 

  101. Shah-Hosseini, H.: Problem solving by intelligent water drops. IEEE, New York (2007)

    Google Scholar 

  102. Agarwal, K., Goyal, M., Srivastava, P.R.: Code coverage using intelligent water drop (IWD). International Journal of Bio-Inspired Computation 4(6), 392–402 (2012)

    Article  Google Scholar 

  103. Maimon, O., Rokach, L. (eds.): Data Mining and Knowledge Discovery Handbook. Springer, New York (2010)

    MATH  Google Scholar 

  104. Holzinger, A., Blanchard, D., Bloice, M., Holzinger, K., Palade, V., Rabadan, R.: Darwin, Lamarck, or Baldwin: Applying Evolutionary Algorithms to Machine Learning Techniques. In: World Intelligence Congress (WIC). IEEE (2014) (in print)

    Google Scholar 

  105. Whitley, D.: An overview of evolutionary algorithms: Practical issues and common pitfalls. Information and Software Technology 43(14), 817–831 (2001)

    Article  Google Scholar 

  106. Pappa, G.L., Freitas, A.A.: Discovering new rule induction algorithms with grammar-based genetic programming. In: Soft Computing for Knowledge Discovery and Data Mining, pp. 133–152. Springer (2008)

    Google Scholar 

  107. Pappa, G.L., Freitas, A.A.: Evolving rule induction algorithms with multi-objective grammar-based genetic programming. Knowledge and Information Systems 19(3), 283–309 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Medical Informatics, Statistics and Documentation, Research Unit HCI, Medical University Graz, Graz, Austria

    Katharina Holzinger & Andreas Holzinger

  2. Faculty of Engineering and Computing, Coventry University, Coventry, UK

    Vasile Palade

  3. Department of Computer Science, Oxford University, Oxford, UK

    Vasile Palade

  4. Department of Systems Biology and Department of Biomedical Informatics, Columbia University, New York, US

    Raul Rabadan

Authors
  1. Katharina Holzinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vasile Palade
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Raul Rabadan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andreas Holzinger
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Research Unit Human-Computer Interaction, Austrian IBM Watson Think Gruop, Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerplatz 2/V, 8036, Graz, Austria

    Andreas Holzinger

  2. IBM Life Sciences Discovery Centre, TECHNA for the Advancement of Technology for Health, Princess Margaret Cancer Centre, University Health Network, TMDT Room 11-314, 101 College Street, M5G 1L7, Toronto, ON, Canada

    Igor Jurisica

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Holzinger, K., Palade, V., Rabadan, R., Holzinger, A. (2014). Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining. In: Holzinger, A., Jurisica, I. (eds) Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. Lecture Notes in Computer Science, vol 8401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43968-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-43968-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43967-8

  • Online ISBN: 978-3-662-43968-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation