Universal Darwinism: How Computer Science has Validated the Theory of Evolution

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Universal Darwinism How Computer Science has
Validated the Theory of Evolution

• Don Baker
• September 2, 2007
• Lonestar Mensa

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About the Speaker

• Ph.D. in Computer Science
• Work for a telecommunications company as a
  researcher in the field of Computer-Supported
  Cooperative Work (CSCW)
• Adjunct Assistant Professor, Computer Science at
  UT
• Taught the theory of computation
• Vice President of the Atheist Community of Austin
• Interests include atheism, memetics, evolutionary
  biology
• Disclaimer I have no professional expertise in
  biology.

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Why I'm Giving this Talk

• Interest in Alan Turing and his contributions
• Turing is considered the father of Computer
  Science
• Laid the foundations of computability and its
  power and limitations
• Turing Machines as problem solvers
• In 1948, Turing suggested that evolution may be
  an alternate way to generate a problem solving
  mechanism There is the genetical or
  evolutionary search by which a combination of
  genes is looked for, the criterion being the
  survival value.
• Interest in evolutionary biology and its
  application to other areas
• Richard Dawkins advanced the concepts of
  replicators and memes
• The advance of the Intelligent Design movement
• Despite a lack of evidence for ID, it has gotten
  a lot of traction
• Much of the traction among the lay audience is
  because the theory of evolution requires some
  thinking and imaginationits not an immediately
  compelling explanation
• ID has effectively traded in building doubt in
  this gap

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Why Evolution is not Compelling (in an armchair
reasoning sense)

• So much apparent design in living things
• Amazing complexity within organisms
• eyes, sonar, wings, camouflage, hunting
  strategies
• Stunning diversity of species
• The gist of Darwins theory is very simple
• People generally cannot reason about the vast
  amounts of time involved

How can something so simple create something so
complex in so little time?
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Evolution is Difficult to Study Experimentally

• Need a controlled environment with many organisms
  that are given a chance to evolve over many
  generations
• But we have learned
• Animal husbandry and agronomy have yielded
  important practical results
• Dog breeds
• Hybrid crops
• We have seen evolution in bacteria and other
  species

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How Might Computer Science Help?

• One definition of computer science Constructive
  models of the world and their implications.
• Evolution is effectively an algorithm
• Computers can be used to create virtual
  environments in which simulations can run at
  speeds faster than their biological counterparts

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Recasting the Problem

• Can we capture the essence of evolution in a
  simulation and, by inference, learn about the
  potential of evolution?
• In particular, is evolution alone able to create
  complex designs?
• Can diverse designs be created, perhaps with
  different characteristics?

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What is the Essence of Evolution?

• Outward traits as an expression of instructions
• Phenotype determined by genotype
• Genotype may either be a blueprint or a recipe
• Instruction inheritance from parent(s) to child
• Genotypic inheritance
• The possibility of copying mistakes (mutation)
• Culling of individuals through natural selection
  based on a fitness criteria applied to outward
  traits
• i.e. death vs. having lots of offspring
• Competition for resources
• Better instructions become more prevalent over
  time

Evolution really operates on the instructions.
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Outward Traits as an Expression of Instructions

• Instructions are digitally encoded
• Instructions are drawn from a space of
  possibilities
• Examples
• Cooking recipe ? The dish it describes
• Architectural blueprint ? The building it
  describes
• A game playing strategy ? A game where one side
  uses it
• Computer program ? Output or other behavior
• Electronic circuit diagram ? Signal response of
  the physical circuit

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Instruction Inheritance from Parent(s) to Child

• High-fidelity (digital) copy, but not perfect
• Telephone game vs. computer viruses
• Instructions with mistakes have some possibility
  of generating a viable result
• Sexual (crossover) or asexual inheritance
• Mechanism of copying is not important
• In computer simulations, inheritance is performed
  explicitly
• A set of instructions need only have a slight
  positive effect on the prevalence of instructions
  in future generations

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Natural Selection on External Traits

• Continued existence of an individual is based on
  a fitness criteria as measured on the external
  traits
• No intelligent designer making choices
• More fit individuals generate more offspring
• Even a large amount of random chance is ok
• The fitness criteria creates a landscape over the
  instruction space
• Natural selection can be likened to hill climbing
  (optimization)

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Fitness Criteria

• In a computer simulation, the fitness criteria
  ranks the possible results according to some
  fixed metric
• Speed, cost, size, etc.
• Even simplicity or elegance (if they can be
  quantified)
• Any combination of the above
• In biological evolution, the fitness criteria is
  just the fecundity of the individual
• Changes in selection pressure due to weather,
  catastrophes, continental drift, etc.
• Changes in selection pressure due to the
  evolution of other species (co-evolution)

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Capturing Evolution in a Genetic Algorithm (GA)

• A genetic algorithm is a probabilistic search
  algorithm that iteratively transforms a set
  (called a population) of mathematical objects
  (typically fixed-length binary character
  strings), each with an associated fitness value,
  into a new population of offspring objects using
  the Darwinian principle of natural selection and
  using operations that are patterned after
  naturally occurring genetic operations, such as
  crossover (sexual recombination) and mutation.
  -- Koza

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Genetic Algorithms Can Solve a Large Variety of
Problems

• Relate the problem to an instruction space
  (genome)
• e.g. all possible electronic circuits involving
  resistors, capacitors, inductors of standard
  values
• Create a means of random mutation and possibly
  crossover inheritance
• e.g. Alter the circuit by adding or removing a
  component or changing value
• Create a fitness criteria as a function of the
  instruction space
• e.g. the least squares distance in frequency
  response of the circuit compared with the
  idealized frequency response of an amplifier
• Start with an Eve individual from the
  instruction space
• e.g. a circuit with no components
• Run the GA and wait
• May want to define a termination criteria based
  on a good enough result
• Might still have to wait many generations
  (hundreds to thousands)

Note that the GA hasnt been told how to design,
but the winners in this race are solution designs.
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Areas Where GAs have been Applied

• Acoustics
• Aerospace engineering
• Astronomy and astrophysics
• Chemistry
• Electrical engineering
• Financial markets
• Game playing
• Geophysics
• Materials engineering
• Mathematics and algorithmics
• Military and law enforcement
• Molecular biology
• Pattern recognition and data mining
• Robotics
• Routing and scheduling
• Systems engineering

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Hemispherical Coverage Antenna
a circularly polarized seven-segment antenna
with hemispherical coverage. Each individual in
the GA consisted of a binary chromosome
specifying the three-dimensional coordinates of
each end of each wire. Fitness was evaluated by
simulating each candidate according to an
electromagnetic wiring code, and the best-of-run
individual was then built and tested. The authors
describe the shape of this antenna, as "unusually
weird" and "counter-intuitive" (p.52), yet it had
a nearly uniform radiation pattern with high
bandwidth both in simulation and in experimental
testing, excellently matching the prior
specification.
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Load-bearing Truss

• that could be assembled in orbit and used for
  satellites, space stations and other aerospace
  construction projects. The result, a twisted,
  organic-looking structure that has been compared
  to a human leg bone, uses no more material than
  the standard truss design but is lightweight,
  strong and far superior at damping out damaging
  vibrations, as was confirmed by real-world tests
  of the final product.

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Lowpass Filter Circuit
Evolved Campbell Filter (U. S. patent
1,227,113 George Campbell American Telephone and
Telegraph 1917)
Lowpass filter frequency response
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Acoustics

• Sato et al. 2002 used genetic algorithms to
  design a concert hall with optimal acoustic
  properties, maximizing the sound quality for the
  audience, for the conductor, and for the
  musicians on stage. This task involves the
  simultaneous optimization of multiple variables.
  Beginning with a shoebox-shaped hall, the
  authors' GA produced two non-dominated solutions,
  both of which were described as "leaf-shaped"
  (p.526). The authors state that these solutions
  have proportions similar to Vienna's Grosser
  Musikvereinsaal, which is widely agreed to be one
  of the best - if not the best - concert hall in
  the world in terms of acoustic properties.

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Robocup Autonomous Robot Soccer Controller
To solve this difficult problem, Andre and Teller
provided the genetic programming algorithm with a
set of primitive control functions such as
turning, moving, kicking, and so on. Out of 34
teams in its division, Darwin United ultimately
came in 17th, placing squarely in the middle of
the field and outranking half of the
human-written entries.
A (human designed) entry
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Are the Designs Intelligent?

• How do we measure the intelligence of a non-human
  designer?
• Can compare against human performance
• Are humans intelligent? sometimes
• Rephrasing Can GAs create human-competitive
  designs?

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Eight Possible Criteria for Human-Competitiveness
(Koza)

• The result was patented as an invention in the
  past, is an improvement over a patented
  invention, or would qualify today as a patentable
  new invention.
• The result is equal to or better than a result
  that was accepted as a new scientific result at
  the time when it was published in a peer-reviewed
  scientific journal.
• The result is equal to or better than a result
  that was placed into a database or archive of
  results maintained by an internationally
  recognized panel of scientific experts.
• The result is publishable in its own right as a
  new scientific resultindependent of the fact
  that the result was mechanically created.
• The result is equal to or better than the most
  recent human-created solution to a long-standing
  problem for which there has been a succession of
  increasingly better human-created solutions.
• The result is equal to or better than a result
  that was considered an achievement in its field
  at the time it was first discovered.
• The result solves a problem of indisputable
  difficulty in its field.
• The result holds its own or wins a regulated
  competition involving human contestants (in the
  form of either live human players or
  human-written computer programs).

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To Date, 36 Human-Competitive Results Have Been
Obtained

• According to the criteria just given
• Several new patents have been generated
• Many designs are considered counterintuitive or
  not well understood by experts in the field from
  which the design is drawn

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Improving on Genetic Algorithms

• Improving on nature
• Many variations on inheritance mechanism are
  being considered by the GA community
• GAs are computationally expensive, but inherently
  parallel
• Many challenges to build efficient GAs and
  computer hardware to support them
• GAs over computer programs are called Genetic
  Programming
• Most general type of GA, as most design problems
  can be expressed in the form of a computer program

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Genetic Programming (Evolution of computer
programs)

• Program represented as parse tree in GP
• Mutations involve random changes to parse tree
  including
• Localized statement changes
• Structural rearrangements
• Duplication of subtrees
• Changing sub-trees into routines
• Etc.
• Phenotype is the behavior of the running program
• Fitness measured by comparison of actual output
  to desired output for a given input

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Genetic Programming is a General Problem Solving
Technique

• Computer programs can solve entire classes of
  problems (i.e. Universal Turing Machine)
• But programs have to be designed
• In 1948, Turing suggested that evolution may be a
  way to generate a problem solving mechanism
• The General Problem Solver has been something
  of a holy grail of computer science research
• Work in this area has spawned the field of
  artificial intelligence
• This important goal has been achieved!

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Universal Darwinism

• Darwins theory applies to more than just
  biological evolution
• Evolution alone is able to create complex designs
  to a diversity of problems
• Application of Darwinian evolution within
  Computer Science has provided a powerful
  validation of his theory
• Genetic Programming is a general problem solving
  technique that has resulted from Darwins ideas

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Parting Shot

• Once a Darwinian process gets going in a world,
  it has an open-ended power to generate surprising
  consequences us, for example. Richard Dawkins

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Further Exploration(My material came primarily
from these sources)

• Evolving Inventions, Koza, et. al.
• Scientific American, February 2003
• Genetic Programming web site (Kozas work)
• http//www.genetic-programming.org
• Genetic Algorithms and Evolutionary Computation
  by Adam Marczyk available via the Talk Origins
  archives
• http//talkorigins.org/faqs/genalg/genalg.html
• Does Intelligent Design Require a Creator? by
  Don Baker
• http//www.christianitymeme.org/intel-design.shtml
  

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Richard Dawkins Biomorphs Introduced in The
Blind Watchmaker
http//www.well.com/hernan/biomorphs/biomorphs.ht
ml
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Controversies

• Real evolution doesnt have a goal
• Isnt defining the fitness criteria in effect
  playing God?
• There is design in the starting point
• No new information is being generated

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Response to the ID Proponents

• If something is a science then
• Predictions can be made from it
• Technology can arise from it
• Benefits to mankind can be derived
• Paleys watchmaker argument (1802) is older than
  the theory of Evolution (1859)
• ID/creationism has born no technologic fruit
• Evolution has made advances in biology and other
  fields, including computer science