an overview of core ci technologies n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
An Overview of Core CI Technologies PowerPoint Presentation
Download Presentation
An Overview of Core CI Technologies

Loading in 2 Seconds...

play fullscreen
1 / 15

An Overview of Core CI Technologies - PowerPoint PPT Presentation


  • 75 Views
  • Uploaded on

An Overview of Core CI Technologies. Lyndon While. Evolutionary Algorithms (EAs). Reading: X. Yao, “Evolutionary computation: a gentle introduction”, Evolutionary Optimization, 2002

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'An Overview of Core CI Technologies' - amela-harrell


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
evolutionary algorithms eas

Evolutionary Algorithms (EAs)

Reading:

X. Yao, “Evolutionary computation: a gentle introduction”, Evolutionary Optimization, 2002

T. Bäck, U. Hammel, and H.-P. Schwefel, “Evolutionary computation: comments on the history and current state”, IEEE Transactions on Evolutionary Computation 1(1), 1997

the nature inspired metaphor
The Nature-Inspired Metaphor
  • Inspired by Darwinian natural selection:
    • population of individuals exists in some environment
    • competition for limited resources creates selection pressure: the fitter individuals that are better adapted to the environment are rewarded more than the less fit
    • fitter individuals act as parents for the next generation
    • offspring inherit properties of their parents via genetic inheritance, typically sexually through crossover with some (small) differences (mutation)
    • over time, natural selection drives an increase in fitness
  • EAs are population-based “generate-and-test” stochastic search algorithms

An Overview of Core CI Technologies

terminology
Terminology
  • Gene: the basic heredity unit in the representation of individuals
  • Genotype: the entire genetic makeup of an individual – the set of genes it possess
  • Phenotype: the physical manifestation of the genotype in the environment
  • Fitness: evaluation of the phenotype at solving the problem of interest
  • Evolution occurs in genotype space based on fitness performance in phenotype space

5

An Overview of Core CI Technologies

the evolutionary cycle
The Evolutionary Cycle

Parent selection

Parents

Initialisation

Genetic variation

(mutation and recombination)

Population

Termination

Offspring

Survivor selection

An Overview of Core CI Technologies

an example
An Example

X

X

5

6

4

8

2

5

An Overview of Core CI Technologies

ea variants
EA Variants
  • There are many different EA variants/flavours:
    • differences are mainly cosmetic and often irrelevant
    • the similarities dominate the differences
  • Variations differ predominately on representation:
    • genetic algorithms (GAs): binary strings
    • evolution strategies (ESs): real-valued vectors
    • genetic programming (GP): expression trees
    • evolutionary programming (EP): finite state machines

and also on their historical origins/aims, and their emphasis on different variation operators and selection schemes

An Overview of Core CI Technologies

designing an ea
Designing an EA
  • Best approach for designing an EA:
    • choose a representation to suit the problem, ensuring (all) interesting solutions can be represented
    • create a fitness function with a useful search gradient
    • choose variation operators to suit the representation, being mindful of any search bias
    • choose selection operators to ensure efficiency while avoiding premature convergence to local optima
    • tune parameters and operators to the specific problem
  • Need to balance exploration and exploitation:
    • variation operations create diversity and novelty
    • selection rewards quality and decreases diversity

An Overview of Core CI Technologies

neural networks nns

Neural Networks (NNs)

Reading:

S. Russell and P. Norvig, Section 20.5, Artificial Intelligence: A Modern Approach, Prentice Hall, 2002

G. McNeil and D. Anderson, “Artificial Neural Networks Technology”, The Data & Analysis Center for Software Technical Report, 1992

the nature inspired metaphor1
The Nature-Inspired Metaphor
  • Inspired by the brain:
    • neurons are structurally simple cells that aggregate and disseminate electrical signals
    • computational power and intelligence emerges from the vast interconnected network of neurons
  • NNs act as function approximators or pattern recognisers which learn from observed data

Diagrams taken from a report on neural networks by C. Stergiou and D. Siganos

An Overview of Core CI Technologies

the neuron model
The Neuron Model

Bias Weight

  • A neuron combines values via its input and activation functions
  • The bias determines the threshold needed for a “positive” response
  • Single-layer neural networks (perceptrons) can represent only linearly-separable functions

Activation

Function

Input

Function

Output

Input

Links

Output Links

An Overview of Core CI Technologies

multi layered neural networks
Multi-Layered Neural Networks
  • A network is formed by the connections (links) of many nodes – inputs to outputs through one or more hidden layers
  • Link weights control the behaviour of the function represented by the NN
    • adjusting the weights changes the encoded function

An Overview of Core CI Technologies

multi layered neural networks1
Multi-Layered Neural Networks
  • Hidden layers increase the “power” of the NN at the cost of extra complexity and training time:
    • perceptrons capture only linearly-separable functions
    • an NN with a single (sufficiently large) hidden layer can represent any continuous function with arbitrary accuracy
    • two hidden layers are needed to represent discontinuous functions
  • There are two main types of multi-layered NNs:
    • feed-forward: simple acyclic structure – the stateless encoding allows functions of just its current input
    • recurrent: cyclic feedback loops are allowed – the stateful encoding supports short-term memory

An Overview of Core CI Technologies

training neural networks
Training Neural Networks
  • Training means adjusting link weights to minimise some measure of error (the cost function)
    • i.e. learning is an optimisation search in weight space
  • Any search algorithm can be used, most commonly gradient descent (back propagation)
  • Common learning paradigms:
    • supervised learning: training is by comparison with known input/output examples (a training set)
    • unsupervised learning: no a priori training set is provided; the system discovers patterns in the input
    • reinforcement learning: training usesenvironmental feedback to assess the quality of actions

An Overview of Core CI Technologies