Natural Computation and Applications

Xin Yaohttp://www.cs.bham.ac.uk/~xin

Natural Computation Group

School of Computer ScienceThe University of Birmingham

UK

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 …

The Solution

What did we do when we had problems as a kid? Who do we normally turn to?

Ask our mother!

Motivation: Mother Nature

Nature Inspired Computation

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

Natural Inspired Computation

Evolutionary computation Neural computation Molecular computation Quantum computation Ecological computation Chemical computation …

Overview of Methods

Natural Computation Methods: Selected Examples

Evolutionary Algorithms Inspired by the biological process of evolution

Artificial Neural Networks Inspired by the function of neurons in the brain

Agent-based techniques Inspired by human social interaction

Ant colony / Swarm techniques

Inspired by the behaviour of social insects

Evolutionary Algorithms

Replacement

Selection

Recombination

MutationPopulation

Parents

Offspring

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

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

Ant Colony Optimisation

Selected Examples

Container Packing

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

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

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.

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

Time Series Prediction

Telecommunications traffic flow prediction

Blue-green algae activity prediction in fresh water lakes

Energy consumption prediction

Financial modelling

Recognition and Classification

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

prediction

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

Creative Technologies: Evolutionary Art

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

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.

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)