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Evolving Neural Networks
Learning and Evolution: Their secret
conspiracy to take over the world.
Adaptation
 There are two forms of adaptation
 Learning
 Training on a set of examples.
 Fitting your behavior to training data.
 Minimizing error.
 Evolution
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Population based search.
Random Mutation
Reproduction
Fitness Selection
Combining The Two
 Combining the strategies of evolutionary
algorithms with learning produces
“evolutionary artificial neural networks”
or EANNs.
 This increases the adaptability of both in
a way that neither system could achieve
on their own.
 This also can give rise to extremely
complex relationships between the two.
What Can an EANN Do?
 Adjust the network weights.
 Learning rules.
 Evolution
 Build an architecture to fit the problem at
hand
 Does it need hidden layers?
 Is the propagation delay too large?
 Is the environment dynamic?
 Perhaps the m ost lofty goal is evolving
a learning rule for the network.
Evolving The Weights
 Why evolve the weights? What’s wrong
with back propagation?
 Backpropagation is a gradient accent
algorithm. These algorithms can get stuck
on a local maximum or local minimum
solutions.
Optimal solution
Local Max
Initial weights
Overcoming Local Min/Max
 Since evolutionary algorithms are population
based they have no need for gradient
information.
 Subsequently they are better in noisy or
complex environments
 Though getting stuck on local maxes can be
avoided there is no guarantee that any maxima
will be found.
Population Samples
The Permutation Problem
 A gigantic problem that reflects the noisy real
world solution is the permutation or competing
convention problem.
 Caused by a many-to-one mapping from genotype to
phenotype within neural nets.
 This problem kills the efficiency and effectiveness of
crossover because effective parents can produce
slacker offspring…..sorry mom and dad.
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Does Evolution Beat
Backpropagation?
 Absolutely!
 D.L. Prados claims that GA-based training algorithms
completed the tests in 3 hours 40 minutes, while
networks using the generalized delta rule finished in
23 hours 40 minutes.
 No way man!
 H. Kitano claims that when testing a Genetic Algorithm
backpropagation combination, it was at best as
efficient as other backpropagation variants in small
networks, but rather crappy in large networks.
 It just goes to show you that the best algorithm
is always problem dependent.
Evolving Network
Architectures
 The architecture is very important to the
information processing capabilities of the
neural network.
 Small networks without a hidden layer can’t solve
problems such as XOR, that are not linearly
separable.
 Large networks can easily overfit a problem to
match the training data, constricting their ability
to generalize a problem set.
Constructing the Network
 Constructive algorithms take a minimal
network and build up new layers nodes and
connections during training.
 Destructive algorithms take a maximal
network and prunes unnecessary layers nodes
and connections during training.
 The network is evaluated based on specific
performance criteria, for example, lowest
training error or lowest network complexity.
The Difficulties Involved
 The architectural search space is infinitely large
 Changes in nodes and connection can have a
discontinuous effect on network performance.
 Mapping from network architecture to behavior
is indirect, dependent on evaluation and
epistatic…don’t ask.
 Similar architectures may have highly divergent
performance.
 Similar performance may be attained by diverse
architectures.
Evolving The Learning Rule
 The optimal learning rules is highly dependent
on the architecture of the network
 Designing an optimal learning rule is very hard
when little is known about the architecture,
which is generally the case.
 Different rules apply to different problems.
 Certain rules make it easier to learn patterns and in
this regard are more efficient.
 Less efficient learning rules can learn exceptions to
the patterns.
 Which one is better? Depends on who you ask.
Facing Reality
 One can see the advantage of having a network
that is capable of learning to learn. Combined
with the ability to adjust it architecture, this sort
of neural net would seem to be approaching
intelligence.
 The reality is that the relationship between
learning and evolution is extremely complex.
 Subsequently research into evolving learning
rules is really in it’s infant stages.
Lamarckian Evolution.
 On a side note Belew McInerney &
Schraudolf state that their findings
suggest a reason why Lamarckian
inheritance cannot be possible.
 Due to issues like the permutation
problem it is impossible to transcribe
network behavior into a genomic
encoding since it is possible that there
are infinitely many encodings that will
produce a phenotype.
Can It Be Implemented?
 Yes it can.
 That’s all I got for this slide. It really seems
like a waste, but at least its not paper..
Go-Playing Neural Nets
 Alex Lubberts and Risto Miikkulainen
have created a Co-Evolving Go-playing
Neural Network.
 No program play go at any significant
level of experience.
 They decided that a parasite host
relationship will foster a competitive
environment for evolution.
The Fight to Survive
 The host
 Attempt to find an optimal solution for
winning on scaled down 5X5 Go board.
 The parasites
 Attempt to find and exploit weaknesses
within the host population.
 Populations are evaluated one at a time
so each population takes turns being a
host or a parasite.
Tricks of the Trade
 Competitive Fitness Sharing
 Unusual or special individuals are rewarded.
 If an individual whups an opponent that is very tough
even if that individual lost most of it’s other games
that host may still be rewarded.
 Shared Sampling
 To cut down on the number of games a sample set is
pitted against opponents.
 Hall of Fame
 Old timers that have competed well are put into a
steel cage match with the tyros to ensure that new
generations are improving.
Conclusions
 They found that co-evolution did in fact
increase the playing ability of their networks.
 After 40 generations using their tournament
style selection the co-evolved networks had
nearly tripled the number of wins against a
similar network that was evolved without a
host parasite relationship.
EANN Used to Classify
Galaxies
 Erick Cantu-Paz & Chandrika Kamath
 Attempting to bring automation in the
classification of galaxies using neural
networks.
 The learning algorithm must be carefully
tuned to the data.
 The relevant features of a classification
problem may not be known before building
the network or tuning the learning rule.
How It Worked
 Six combinations of GA and NN where
compared.
 The interesting part is the evolution of feature
selection
 GAs where to select what features are important
enough that the NN needs to know them in order to
classify a galaxy.
 GAs consistently selected half the features and
supposedly of the half they selected most of the
features where relevant to classification such as
symmetry measures and angles.
 Two point crossover was used along with a
fitness bias towards networks that learn quickly.
Findings
 Several of the evolved networks where
competitive with human designed
networks.
 The best evolving feature selection.
 Identifying Bent-Double Galaxies 92.99%
accuracy.
 Identifying Non-Bent 83.65% accurac
y.
Alright! He’s About to Shut
Up
 So in conclusion there is a lot of room for
improvement in EANNs and there is a lot
to explore.
 So quit what your doing and build an
evolving learning program. Tell your
friends that you have already given it a
body and soon you will make it capable
of replicating itself. If they are gullible
enough you might get to watch them
squirm.
References, The lazy way.

Richard Belew, John McInerney, Nicol Schraudolph: Evolving
Networks
http://www-cse.ucsd.edu/users/rik/papers/alife91/evol-net.ps

Erick Cantu-Paz C. Kamath: Evolving Neural Networks for the
Classification of Galaxies
http://www.llnl.gov/CASC/sapphire/pubs/147020.pdf

Xin Yao Evolving Artificial Neural Networks
http://www.cs.bham.ac.uk/~xin/papers/published_iproc_sep99.pdf

Alex Lubberts, Risto Miikkulainen Co-Evolving a Go-Playing
Neural Network
http://nn.cs.utexas.edu/downloads/papers/lubberts.coevolutiongecco01.pdf