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Genetic Programming: An Introduction On The Automatic Evolution Of Computer Programs And Its Applications

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Since the early 1990s, genetic programming (GP) a discipline whose goal is to enable the automatic generation of computer programs has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning Since the early 1990s, genetic programming (GP) a discipline whose goal is to enable the automatic generation of computer programs has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks. This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.


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Since the early 1990s, genetic programming (GP) a discipline whose goal is to enable the automatic generation of computer programs has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning Since the early 1990s, genetic programming (GP) a discipline whose goal is to enable the automatic generation of computer programs has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks. This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.

49 review for Genetic Programming: An Introduction On The Automatic Evolution Of Computer Programs And Its Applications

  1. 5 out of 5

    Clemens Lode

    A very technical book which covers state-of-the-art (well, 1997) scientific knowledge about genetic programming. It's a great compilation of studies with many diagrams but it's not for the faint-hearted. Genetic programming is a field of Artificial Intelligence where the programmer (you?) does not try to solve the problem. Instead a simulation is created with which the AI trains itself. While there is no guarantee for achieving the best solution, it tends to come very close while taking very A very technical book which covers state-of-the-art (well, 1997) scientific knowledge about genetic programming. It's a great compilation of studies with many diagrams but it's not for the faint-hearted. Genetic programming is a field of Artificial Intelligence where the programmer (you?) does not try to solve the problem. Instead a simulation is created with which the AI trains itself. While there is no guarantee for achieving the best solution, it tends to come very close while taking very little time. Basically, it can solve any type of problem, as long as the problem can be formulated and typed into the computer.

  2. 4 out of 5

    Jacob Okoro

    Awesome read

  3. 5 out of 5

    Joakim Ekblad

  4. 5 out of 5

    Marek

  5. 5 out of 5

    Chris

  6. 5 out of 5

    David

  7. 5 out of 5

    Subhajit Das

  8. 4 out of 5

    Jonathan Smith

  9. 4 out of 5

    Jan Wikholm

  10. 4 out of 5

    Mark Young

  11. 5 out of 5

    John

  12. 5 out of 5

    JEAN-BERNARD MOENS

  13. 5 out of 5

    Jonathan Lamarre

  14. 5 out of 5

    Randall

  15. 5 out of 5

    Michael Fransen

  16. 5 out of 5

    Benjamin Anderson

  17. 4 out of 5

    John Gair

  18. 5 out of 5

    João Bruno

  19. 4 out of 5

    Bill White

  20. 4 out of 5

    Marcin

  21. 5 out of 5

    Evans

  22. 5 out of 5

    Aleksander Shtuk

  23. 4 out of 5

    Randall

  24. 5 out of 5

    Nick Curran

  25. 5 out of 5

    Joe

  26. 5 out of 5

    Johnny Alvarado

  27. 5 out of 5

    Rod Hilton

  28. 5 out of 5

    wac

  29. 5 out of 5

    Mauve Co

  30. 4 out of 5

    John DeCuir

  31. 5 out of 5

    Will Bradley

  32. 5 out of 5

    Scott Stensland

  33. 4 out of 5

    Derek

  34. 5 out of 5

    B. Andersen

  35. 4 out of 5

    Kaiser

  36. 4 out of 5

    Munjal Subodh

  37. 5 out of 5

    Jon

  38. 4 out of 5

    Undertowe

  39. 4 out of 5

    Superguy2876

  40. 5 out of 5

    calavera

  41. 5 out of 5

    Vehbi Sinan

  42. 5 out of 5

    David

  43. 4 out of 5

    David Li

  44. 5 out of 5

    Hairuo

  45. 5 out of 5

    Mark

  46. 5 out of 5

    Madhu

  47. 4 out of 5

    Olabode

  48. 4 out of 5

    Jorge Vilhena

  49. 5 out of 5

    Rasheswori

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