The Perils & Pleasures of Interdisciplinarity

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David E. Goldberg reflects on living an interdisciplinary life at a talk given at a Workshop on the Challenges in Top-Down, Bottom-Up and Computational Approaches in Synthetic Biology

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  • 1. The Perils and Pleasures of Interdisciplinarity:Practical & Philosophical Reflections on an Interdisciplinary Life
    David E. Goldberg
    Illinois Genetic Algorithms Laboratory & iFoundry
    University of Illinois at Urbana-Champaign
    Urbana, IL 61801 USA
    Email: deg@illinois.edu; Web: http://www.ifoundry.illinois.edu
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    David E. Goldberg 2010
  • 2. Reflections on an Interdisciplinary Life
    30th year in GAs; 27th year since dissertation. 22th year since GASOML.
    Have been blessed to be part of growth of an interdisciplinary field.
    Could easily have been otherwise.
    Almost every central turning point was unlikely event.
    Want to reflect on those times personally, practically & philosophically.
    Not a moral exemplar. Just interesting stories.
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    David E. Goldberg 2010
  • 3. Roadmap
    • Whats a nice civil engineer doing in a place like this?
    • 4. A cocktail party in Canterbury.
    • 5. One September in Ann Arbor.
    • 6. A professor named Holland.
    • 7. The education of a genetic algorithmist.
    • 8. My philosophical turn & starting a company.
    • 9. Reflections on existentialism, paradigms, and the education of engineering and computer scientists in an interdisciplinary age.
    • 10. Finding a lifes impedance match.
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    David E. Goldberg 2010
  • 11. Once Upon a Time
    Once upon a time
    There was a civil engineer
    working for Stoner Associates
    doing hydraulics software for pipelines.
    Was starting to do real-time control &
    wondered how human operators
    controlled gas pipelines
    like you or I drive a car.
    Went to British Hydromechanics Research Association to represent company.
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    David E. Goldberg 2010
  • 12. A Cocktail Party in Canterbury
    At the opening reception.
    My advisor walks in
    Like the parting of the Red Sea.
    Another prof asks WHEN will I return for PhD.
    Not cost effective.
    A phone call & a big night.
    E. Benjamin Wylie (b. 1928)
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    David E. Goldberg 2010
  • 13. One Fine September Day in A2
    First day of classes and was signed up for standard AI course.
    Expert systems were the rage, Prolog was hip, LISP was cool.
    Class was cancelled with little sign on the door.
    Hopes and dreams down the drain.
    Searched and searched for a replacement.
    Found CCS 524, Intro to Adaptive Systems, taught byJohn Holland.
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    David E. Goldberg 2010
  • 14. A Professor Named Holland
    Youngish looking prof:
    Talking about biology & genetics.
    Samuels checker player.
    Schemas and building blocks.
    Classifier systems.
    Whats nice civil engineer doing in class like this?
    When was Prof Holland going to get to real AI I could use for pipelines?
    Or maybe this was the real AI.
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    David E. Goldberg 2010
  • 15. Education of a Genetic Algorithmist
    1984 took position in Engineering Mechanics at Alabama, Tuscaloosa.
    Education began then, but there was a lot I needed to learn:
    Focus on 4 core lessons:
    Learning to ask
    Learning to label
    Learning to decompose
    Learning to model
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    David E. Goldberg 2010
  • 16. Lesson 1: Learning to Ask
    • In 1984 had many questions about how GAs work, when they fail?
    • 17. Wasnt experienced in asking good framing questions.
    • 18. Key problem: Using GAs to solve engineering problems, but GAs werent engineered well.
    • 19. Philosophical terms: Socrates 101.
    David E. Goldberg 2010
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    Socrates (470-399 BCE)
  • 20. Whats a Good Question?
    Socrates asked variety of questions.
    What is truth? What is courage?
    More often the critic. Rarely gave answers.
    In creative enterprises, many good questions are framing questions:
    Get at heart of the issue.
    Help define the problem or elicit definition.
    Sometimes cause problem to be represented in novel way or from unusual or creative perspective.
    Fundamental importance of dialectic. Creative process of asking and answering questions.
    GA example from 1985: Alleles, Loci & Traveling Salesman Problem. How is inversion for orderings, similar to and different from mutation & crossover for alleles?
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    David E. Goldberg 2010
  • 21. Lesson 2: Learning to Label
    • In the early days, language was nonexistent or unsettled.
    • 22. Challenge of being category creator vs. category enhancer.
    • 23. Tabula rasa or a green field.
    • 24. Some borrowed from biology, fitness, linkage & landscape.
    • 25. Others invented: deception, niching, abeyance,
    • 26. Philosophical terms: Aristotle 101.
    • 27. Underappreciated as means to understanding and solving problems.
    • 28. GA example: Use of term linkage learning leads to practical schemes such as mGA, fmGA, LLGAs, and adaptive EDAs.
    David E. Goldberg 2010
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    Aristotle (384-322 BCE)
  • 29. Terms Really Do Matter
    Terms gather thoughts under consistent rubrics.
    Can be part of larger taxonomy.
    Defines attention areas.
    Can have influence on how others think.
    Catchy or sticky terms propagate virally.
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    David E. Goldberg 2010
  • 30. Lesson 3: Learning to Decompose
    • Wasnt experienced at decomposing big problem into little problems.
    • 31. Looked for magic bullets in equations of motion or transform methods.
    • 32. 1990 talk by Gary Bradshaw on the Wright Brothers and their explicit decomposition of powered flight.
    • 33. Philosophical terms: Descartes 101?
    David E. Goldberg 2010
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    Ren Descartes (1596-1650)
  • 34. Design Decomposition for GA Design
    ICGA 1991: Shared theory tutorial with GunarLiepins.
    Need design theory that works:
    Understand building blocks (BBs), notions or subideas.
    Ensure BB supply.
    Ensure BB growth.
    Control BB speed.
    Ensure good BB decisions.
    Ensure good BB mixing (exchange).
    Know BB challengers.
    Read about it in DoI.
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    David E. Goldberg 2010
  • 35. Lesson 4: Learning to Model
    • Knew quite a bit about modeling mathematically.
    • 36. Engineers as Pavlovian dogs when it comes to equations.
    • 37. Didnt know how to model conceptually:
    • 38. Causal chain.
    • 39. Categorize according to list of types or kinds.
    • 40. Need to understand problem qualitatively in words and diagrams prior to quantitative modeling undertaken.
    • 41. Philosophical terms: Hume 101 or Aristotle 102.
    David E. Goldberg 2010
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    David Hume (1711-1776)
  • 42.