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Genetic Algorithms. Overview. Genetic Algorithms: a gentle introduction What are GAs How do they work/ Why? Critical issues Use in Data Mining GAs and statistics decile performance maximization multi-objective models. Natural Genetics to AI.

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Presentation Transcript
overview
Overview
  • Genetic Algorithms: a gentle introduction
    • What are GAs
    • How do they work/ Why?
    • Critical issues
  • Use in Data Mining
    • GAs and statistics
    • decile performance maximization
    • multi-objective models
natural genetics to ai
Natural Genetics to AI
  • Computational models inspired by biological evolution
    • survival of the fittest
    • reproduction through cross-breeding
genetic algorithms4
Genetic Algorithms
  • Population based search (parallel)
    • simultaneous search from multiple points in search space
    • useful in complex, unstructured search spaces

(less prone to local failures)

Population members: potential solutions

  • Population of solutions evolve from one generation to the next
genetic algorithms5
Genetic Algorithms
  • Search objective
    • Fitness score for population members

(fitness function)

  • Survival of the fittest
    • selection
  • Generating new solutions
    • “Mating” and reproduction of individuals

(crossover, mutation)

basic operation
Basic Operation

Recombination

Selection

Crossover

Mutation

Generation t

Generation t+1

gas parallel search
GAs: Parallel Search

Fitness

X

Hill

climber

X

x

gas basic principles
GAs: Basic Principles
  • Representation of individuals
    • String of parameters (genes) : chromosome

eg. optimize a function F(p,q,r,s,t)

Population members: p q r s t

    • genotype and phenotype
binary representation
Binary representation?
  • Population members as bit strings

F( p,q,r,s,t) as:

1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 1 0 1 0

p q r s t

    • early theory in terms of binary strings (schema theorem)
    • unnecessary perversity?
gas basic principles10
GAs: Basic Principles
  • Survival of the fittest (Fitness function)
    • numerical “figure of merit”/utility measure of an individual
    • tradeoff amongst a multiple evaluation criteria
    • efficient evaluation
gas basic principles11
GAs: Basic Principles
  • Iterative search
    • population evolves over generations
  • Convergence
    • progression towards uniformity in population
    • premature convergence?

(local optima)

typical ga run
Typical GA Run

Fitness

Best

Average

Generations

operators selection
Operators: Selection
  • Fitness proportionate selection (fi/f )
  • number of reproductive trials for individuals
selection
Selection
  • Roulette-wheel selection

(stochastic sampling with replacement)

    • wheel spaced in proportion to fitness values
    • N (pop size) spins of the wheel
  • Stochastic universal sampling
    • N equally spaced pins on wheel
    • single turn of the wheel
selection15
Selection
  • Premature converge
  • Fitness scaling

f = f - (2*avg. - max.)

  • Ranked fitness
  • Elitism
  • Steady-state selection
  • Demetic grouping
operators crossover
Operators: Crossover

Parent 1: axpsqvqbtpihd

Parent 2: qzxxaycgbtphw

crossover sites

Offspring 1: azpsavcbtpphd

Offspring 2: qxxxqyqgbtihw

(Uniform crossover)

  • combining good building blocks
operators mutation
Operators: Mutation
  • alters each gene with small probability

x 1 y x 0 y 0 y y 0 x y x y

x 1 y x 0 y 1 y y 0 x x x y

non binary representations
Non-Binary Representations
  • Integer, real-number, order-based, rules, ...
  • Binary or Real-valued?

real representations give faster, more

consistent, more accurate results

  • High-level representation
    • intuitive, can utilize specialized operators
    • effective search over complex spaces
real valued representation
Real-valued representation

Parent1: 3.45 0.56 6.78 0.976 2.5

Parent2: 0.98 1.06 4.20 0.34 1.8

Offspring1: 3.22 0.56 6.78 0.652.12

Offspring2: 1.43 1.06 4.20 0.411.93

(Arithmetic crossover)

high level representation
High-level representation

Parent1:

Parent2:

Offspring1:

Offspring2:

high level representation21
High-level representation
  • Generalize/Specialize
tree structured representation gp
Tree-structured representation (GP)
  • Automated learning of programs (originally)
  • parse tree expressions
  • Non-linear interaction terms
  • Function set : internal nodes
    • {+,-,*,/,log}
  • terminal set: leaf nodes
    • {constants, variables}

*

/

log

y

x

5

(x log(y))/5)

tree structured representation

if

AND

0

+

<

>

y

y

7

x

2

*

x

2

Tree-structured representation
  • Representing complex patterns

If (y<7) and (x>2)

then 0

else 2x+y

genetic search issues
Genetic search: Issues
  • Coding scheme, fitness function critical
    • the “art” in GA design!
    • General mechanism so robust that, within reasonable margins, parameter settings are not critical.
  • Representation to match problem, domain
    • utilizing domain knowledge
      • problem-specific crossover, mutation, selection
  • Flexibility in fitness function formulation
    • modeling business objectives
genetic search issues25
Genetic search: Issues
  • Stochastic search
    • initial populations, probabilistic operators
    • multiple runs with different random streams
    • Initializing population with known solutions
    • seeding initial population with solutions from multiple, independent runs
genetic search issues26
Genetic search: Issues
  • Guarantees optimality?
    • But...
  • GAs and traditional techniques
    • especially useful where traditional approaches fail
    • in conjunction with traditional techniques
  • Parallelizable for large data
    • multi-processor, networked machines
using gas
Using GAs ?
  • When to use a GA?
  • GA and traditional techniques
  • How long does it take?
  • Will it perform better?
using gas28
Using GAs
  • population size
  • mutation, crossover rates
  • how many generations
  • multiple runs
is it a black box

?

Huh?

Is it a “black-box”?
  • Data characteristics
  • Fitness function
  • GA parameters
ga application examples
GA Application Examples
  • Function optimizers
    • difficult, discontinuous, multi-modal, noisy functions
  • Combinatorial optimization
    • layout of VLSI circuits, factory scheduling, traveling salesman problem
  • Design and Control
    • bridge structures, neural networks, communication networks design; control of chemical plants, pipelines
ga application examples31
GA Application Examples
  • Machine learning
    • classification rules, economic modeling, scheduling strategies

Portfolio design, optimized trading models, direct

marketing models, sequencing of TV advertisements,

adaptive agents, data mining, etc.