Natural Computation and Applications

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Natural Computation and Applications

• Xin Yao
• http//www.cs.bham.ac.uk/xin
• Natural Computation Group
• School of Computer Science
• The University of Birmingham
• UK

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Frustration About Computers

• Brittle
• Non-adaptive
• Doesnt learn
• Hopeless in dealing with noisy and inaccurate
  information
• Doesnt do the homework for me although I told it
  that I want a mark over 70
• Never grow up
• Slow

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The Solution

• What did we do when we had problems as a kid? Who
  do we normally turn to?
• Ask our mother!

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Motivation Mother Nature
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Nature Inspired Computation
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Characteristics of Nature Inspired Computation

• Flexible applicable to different problems

• Robust can deal with noise and uncertainty

• Adaptive can deal with dynamic environments

• Autonomous without human intervention

• Decentralised without a central authority

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Natural Inspired Computation

• Evolutionary computation
• Neural computation
• Molecular computation
• Quantum computation
• Ecological computation
• Chemical computation

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Overview of Methods
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Natural Computation Methods Selected Examples
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Evolutionary Algorithms
Selection
Parents
Recombination
Population
Mutation
Offspring
Replacement
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Artificial Neural Networks

• Simplified model of a brain
• Consist of inputs, processing and outputs
• All layers joined by artificial neurons
• Fault tolerant
• Noise resistant
• Can learn and generalise
• Good at perception tasks

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Agent-based Techniques

• Multiple independent agents follow individual
  strategies
• Macro-level behaviour develops
• Useful for modelling trading strategies
• Can simulate competitive markets
• Dynamically optimised scheduling

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Ant Colony Optimisation
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Selected Examples
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Container Packing

• How to put as many boxes of different sizes into
  containers in order to minimise space wastage

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Swarm intelligence for Animation

• Flocking can be simulated in computers
• Flocking uses rapid short-range communication
• Behaviour governed by mutual avoidance, alignment
  and affinity.
• Simple rules generate complex behaviour

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Channel Allocation Inspired by Fruit Flies

• Fruitflies have an insensitive exoskeleton
  peppered with sensors formed from short bristles
  attached to nerve cells. It is important that
  the bristles are more or less evenly spread out
  across the surface of the fly. In particular it
  is undesirable to have two bristles right next to
  each other.
• The correct pattern is formed during the fly's
  development by interactions among its cells. The
  individual cells "argue" with each other by
  secreting protein signals, and perceiving the
  signals of their neighbours.  The cells are
  autonomous, each running its own "algorithm"
  using information from its local environment. 
  Each cell sends a signal to its neighbours at
  the same time it listens for such a signal from
  its neighbours. 
• This "arguing" process is the inspiration for the
  channel allocation method.

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Constrained Dynamic Routing

• Dynamic call routing in telecommunication
  networks
• Finding optimal routes for salting trucks
• Evolutionary algorithms Robust, efficient and
  can be used for hard, dynamic problems for which
  there is little domain knowledge

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Time Series Prediction

• Telecommunications traffic flow prediction
• Blue-green algae activity prediction in fresh
  water lakes
• Energy consumption prediction
• Financial modelling

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Recognition and Classification

• Object recognition
• Medical diagnosis
• Credit card assessment
• Fraud detection
• Vehicle tracking
• Subscriber churn prediction

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Creative Technologies

• Natural computation techniques can be used
  effectively in the creative industry for
  graphics, images, music, games, etc.
• Highly effective at exploring the huge space of
  possible artefacts
• Boids
• Karl Simss artificial creatures

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Creative Technologies Evolutionary Art

• Evolutionary art from Andrew Rowbottom
• Genetic art by Peter Kleiweg
• Organic art by William Latham

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Summary

• Evolutionary computation is part of natural
  computation
• Evolutionary computation can be used in
  optimisation, data mining and creative design.
• Evolutionary computation are particularly good at
  solving complex real world problems where very
  little domain knowledge is available.
• Evolutionary computation complements the existing
  methods.

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Further Information

• http//www.cs.bham.ac.uk/research/NC/ (research
  group in the School)
• http//www.cs.bham.ac.uk/study/postgraduate-taught
  /msc-nc/ (MSc in Natural Computation)
• http//www.evonet.polytechnique.fr/CIRCUS2/
  (Evolutionary Computation Education Center -
  (EC)² )
• http//ieee-nns.org/pubs/tec/ (IEEE Transactions
  on Evolutionary Computation)