Evolutionary computation: Keith Downing at TEDxTrondheim

Keith Downing is a professor of Computer Science at the Norwegian University of Science and Technology, specializing in Artificial Intelligence and Artificial Life. He has a particular interest in evolutionary algorithms, which have applications ranging from the development of the Mars Rover antenna, patented circuits, early driverless cars, to even art. For computer scientists to learn from nature, he believes there needs to be a shift in our traditional ways thinking.

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[danielf]

Genetic algorithms are really interesting.

[JasperPeters]

Is this a yt issue or just this video, but the audo video desync is huge, and becoming bigger over time.

[mikebellamy]

What Mr Downing is showing are examples of the classic Monte Carlo Methods
used by engineers to optimise a design. Boeing use these for aerofoil
design etc. And yes they are examples of the evolutionary "algorithm"
ie CHANGE -> SELECT -> COPY -> REPEAT. However the
assumption inherent that they demonstrate how natural selection could
work in biological evolution is seriously flawed.


1- The probability of a beneficial change is both unrealistically high
and fixed

2 - Given a beneficial change the probability of selection is very high
approaching 1

3 - The whole experiment is INTELLIGENTLY DESIGNED to for a specific
PURPOSE. Nature has by definition no purpose which actually precludes the
creation of information in the DNA which always has a purpose

4 - Drawing a comparison between these simplified models and biological
evolution ignores the second law of thermodynamics which demands any
state of order may only be achieved at the cost of greater disorder
in the surroundings. In all such simulations the cost in the
surroundings is proportional to the number of cycles of the
experiment. But the number of cycles required is NOT proportional to
the size of the problem. It is an EXPONENTIAL FUNCTION of the size of
the problem which becomes very very large for evolution by natural
selection where there is very very low probability of beneficial
change and low probability of selection.

5 - Artificial selection in these models closely or directly uses the
parameter that is being changed as the element upon which survival is
based. However in biological evolution selection is at the phenotype
performance level while the elements being changed are the DNA which
will only in relatively few cases have any significant effect on
phenotype performance. This is why probability of selection in nature
is very low.

6 - As may be demonstrated by the failure of the travelling salesman
example artificial selection models suffer from logical traps when
the complexity of the model increases. Demonstrating in the real world
that assumed natural selection will encounter the same thing. For
example the butterfly. A creature with two body plans of which only
the second can lay eggs. There is no known way this could evolve from
a creature with only one body plan since there is no advantage (selection value) in an
incomplete second body plan.

The problem here is evolution education does not encourage critical thinking. You are being told what to believe not how to think. I'll leave it there for now..

[cuiyungao]

biology really gives us some inspirations~!

[helixalgorithm]

believe birds use the V-design for commuting in order to avoid incoming stool.

[VivekNair-vivekaxl]

Academy award for bio-inspired approach? Any references to that

[dinmohammadyosufi]

I am working on spider monkey optimization algorithm.