1. 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

2. Frustration About Computers  Brittle  Non-adaptive  Doesn’t learn  Hopeless in dealing with noisy and inaccurate information  Doesn’t do the homework for me although I told it that I want a mark over 70%  Never grow up  Slow ……

3. The Solution  What did we do when we had problems as a kid? Who do we normally turn to?  Ask our mother!

4. Motivation: Mother Nature

5. Nature Inspired Computation

6. 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

7. Natural Inspired Computation  Evolutionary computation  Neural computation  Molecular computation  Quantum computation  Ecological computation  Chemical computation ……

8. Overview of Methods

9. Natural Computation Methods: Selected Examples Evolutionary AlgorithmsInspired by the biological process of evolution Artificial Neural NetworksInspired by the function of neurons in the brain Agent-based techniquesInspired by human social interaction Ant colony / Swarm techniques Inspired by the behaviour of social insects

10. Evolutionary Algorithms Replacement Selection Recombination Mutation Population Parents Offspring

11. 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

12. 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

13. Ant Colony Optimisation

14. Selected Examples

15. Container Packing  How to put as many boxes of different sizes into containers in order to minimise space wastage

16. 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

17. 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.

18. 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

19. Time Series Prediction  Telecommunications traffic flow prediction  Blue-green algae activity prediction in fresh water lakes  Energy consumption prediction  Financial modelling

20. Recognition and Classification  Object recognition  Medical diagnosis  Credit card assessment  Fraud detection  Vehicle tracking  Subscriber churn prediction

21. 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 Sims’s artificial creatures

22. Creative Technologies: Evolutionary Art  Evolutionary art from Andrew Rowbottom  Genetic art by Peter Kleiweg  Organic art by William Latham

23. 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.

24. Further Information  http://www.cs.bham.ac.uk/research/NC/ (research group in the School) http://www.cs.bham.ac.uk/research/NC/  http://www.cs.bham.ac.uk/study/postgrad uate-taught/msc-nc/ (MSc in Natural Computation) http://www.cs.bham.ac.uk/study/postgrad uate-taught/msc-nc/  http://www.evonet.polytechnique.fr/CIRC US2/ (Evolutionary Computation Education Center - (EC)² ) http://www.evonet.polytechnique.fr/CIRC US2/  http://ieee-nns.org/pubs/tec/ (IEEE Transactions on Evolutionary Computation) http://ieee-nns.org/pubs/tec/