ART Artificial Reasoning Toolkit Evolving a complex system - PowerPoint PPT Presentation

1 / 21
About This Presentation
Title:

ART Artificial Reasoning Toolkit Evolving a complex system

Description:

ART Artificial Reasoning Toolkit. Evolving a complex system. Marco Lamieri ... Evolution itself does not guarantee the creation of fitter individuals. ... – PowerPoint PPT presentation

Number of Views:117
Avg rating:3.0/5.0
Slides: 22
Provided by: Mar5339
Category:

less

Transcript and Presenter's Notes

Title: ART Artificial Reasoning Toolkit Evolving a complex system


1
ART Artificial Reasoning ToolkitEvolving a
complex system
Spss training day 03-05-2004
Marco Lamieri lamieri_at_econ.unito.it
2
  • Agenda
  • What is a genetic algorithm and how it works
  • Some improvement to the method, the ART project
  • Move to real world with an industry application
    the Penelope project
  • References

3
The idea
  • Starting from Survival of the fittest Darwin,
    1959
  • Genetic Algorithms (GA) are evolutionary programs
    that manipulate a population of individuals
    represented by fixed-format strings of
    information.
  • The background theory is the artificial
    adaptation discussed by Holland Holland,1992.
  • GA are used to solve real-world optimization
    problems within a very large solution space and
    non well defined problems.

4
How does a GA work
  • An initial population of individuals (solutions)
    is generated
  • individuals represent potential solutions to the
    given problem and are described as binary
    strings
  • each character in the individuals data string is
    called a gene and each possible value that the
    gene can take on is called an allele.
  • Using a fitness proportional approach parents and
    individuals that are going to survive to the next
    generation are selected
  • The selected individuals are evolved by means of
    reproduction using two operators
  • crossover,
  • mutation.
  • Process go on untill the population converge to a
    specific individual.

5
Example (square root of 2)
  • The solution space is bounded between 0 and 1.
  • We use a binary representation on 10 digits.
  • There are 1024 numbers 210, starting from 0
    and ending at 1023 210 -1.

6
Generate random population
  • A population of solutions is generated randomly.
  • For the square root problem, a fixed number of 10
    character binary strings are generated randomly.

7
Define the fitness function
  • Darwinian evolution of a population implies that
    the strongest individuals will probably survive.
  • The fitness of an individual is a numerical
    assessment of that individuals ability to solve
    the problem - it is the ability of the individual
    to satisfy the requirements of the environment.
  • In terms of the square root problem, the perfect
    individual is the numerical value approximated by
    1.414213562373.
  • In economic problems,the profit can be used to
    generate a fitness function

8
(No Transcript)
9
Selection process (roulette wheel)
  • To select individuals is used the roulette wheel
    technique.
  • The roulette wheel implementation implicitly
    forces fitness-proportionate reproduction.
  • Selection is divided in 2 steps
  • Individuals that are going to survive to the next
    generation are selected
  • Individuals that are going to reproduce are
    selected.

10
Crossover
  • Crossover swaps some of the genetic material of
    two individuals, creating two new individuals
    (children), who are possibly better than their
    parents.

11
Mutation
  • In order to recover from this loss of genetic
    material, the individuals are allowed to change
    their genes randomly.

12
Convergence
  • John Hollands Schema Theorem Holland, 1992 is
    widely accepted as mathematical proof that the
    genetic algorithm, due to its fitness-proportionat
    e reproduction, converges to better solutions.
  • Via the convergence method is possible to solve
    non well-defined problems where the best
    solution is not known a priori.

13
Remarks
  • There is no ultimate goal or problem that must be
    solved by natural evolution.
  • Evolution itself does not guarantee the creation
    of fitter individuals.
  • The GA use a fuzzy logic that not always lead to
    the best solution but to a good one.
  • The algorithm is problem independent.

14
ART Some improvement to the method
  • ART, starting from John Holland's work,
    introduces some extensions and innovations
  • extended alphabet each gene can be represented
    by up to 32000 values. In a standard
    representation the genes have a binary alphabet
    and can become meaningless. With the extended
    alphabet each allele can be a meaningful part of
    the solution and the translation process is
    easier.
  • multi genome the multi genome schema give a
    high degree of freedom to the user in formalizing
    problems in which coexist different binded
    aspects.
  • rescale fitness operatorthe natural selection
    process has been modified in order to improve
    efficiency and manage negative fitness values.
  • univocal genome using this option each value of
    the alphabet is unique within the genome.

15
An industry application the Penelope Project
  • Penelope is an optimizing automated production
    planning engine.
  • It is mainly applied to the textile industry.
  • Penelope, consists of
  • Enterprise Simulator (ES)a model of the firm's
    supply chain developed in Swarm.
  • Genetic algorithm (GA) searching the solutions
    space to find the best production plan.

16
(No Transcript)
17
The Enterprise Simulation
  • Daily about 200 bulk orders arrive whit a defined
    delivery deadline
  • Delay has economic value in term of customer
    satisfaction
  • There are 20 machines available for the process
  • Each machine can perform different operations
    with setup costs and setup time.
  • A limited number of workers has to take care of
  • machine set up
  • patrolling
  • ? Economic value of the production plan(fitness)

18
The algorithm
  • Solution space is
  • Evaluating this number of solutions via brute
    force would take millions of years.
  • The GA solve it in about 20 minutes.
  • The individual is defined by
  • 1 univocal genome with order number
  • 1 random genome with machine number
  • ? The priority is derived from the combination of
    the two genomes

19
Results
  • scheduling time reduction of nearly 80
  • Random planning ? cost 100
  • Fifo standard ? cost 60
  • Human planner ? cost 40
  • Penelope ? cost 25
  • wider elaboration cases set (non obvious plan)
  • best cost/time rate solution
  • disposer software costs reduction (50)
  • economic saving in terms of skilled resources
  • more knowledge on production process and precise
    prediction of production time give strong
    contractual power to the enterprise
  • overall increase of the performance of the
    company that can be more then 2 of the yearly
    value-added.

20
References
  • ART project http//eco83.econ.unito.it/golem
  • Penelope project http//www.penelopeproject.org
  • This presentation is available at
    http//eco83.econ.unito.it/golem/ppt/20040503-sp
    ss-art.ppt
  • For any further information
  • lamieri_at_econ.unito.it

21
Questions ?
Write a Comment
User Comments (0)
About PowerShow.com