ICT619%20Intelligent%20Systems%20Topic%205:%20Genetic%20Algorithms

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ICT619 Intelligent SystemsTopic 5 Genetic
Algorithms
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Genetic Algorithms

• Introduction
• How GAs work
• The TSP as an example
• Business Applications of GA
• Advantages of GA systems
• Some issues related to GA based systems
• Case Study

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What is a genetic algorithm?

• GA part of the broader soft computing (aka
  "computational intelligence") paradigm known as
  evolutionary computation
• First introduced by Holland (1975)
• Inspired by possibility of problem solving
  through a process of evolution

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What is a GA? (contd)

• GA mimics biological evolution to generate
  better solutions from existing solutions through
• survival of the fittest
• crossbreeding and
• mutation

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What is a GA? (contd)

• A GA is capable of finding solutions for many
  problems for which no usable algorithmic
  solutions exist
• GA methodology particularly suited for
  optimization
• Optimization searches a solution space consisting
  of a large number of possible solutions
• GA reduces the search space through evolution of
  solutions, conceived as individuals in a
  population

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Intelligence and Evolution

• One way of understanding intelligence is as the
  capability of a creature to adapt itself to an
  ever-changing environment
• We normally think of adaptation as changes in the
  characteristics (including behaviours) of a
  single animal in response to experiences over its
  history
• But adaptation is also change in the
  characteristics of a species, over the
  generations, in response to environmental change
• An individual creature is in competition with
  other individuals of the same species for
  resources, mates etc.
• There is also rivalry from other species which
  may be a direct (predator)or indirect (food,
  water, land, etc.) threat
• In nature, evolution operates on populations of
  organisms, ensuring by natural selection that
  characteristics that serve the members well tend
  to be passed on to the next generation, while
  those that dont die out

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Evolution as Optimisation

• Evolution can be seen as a process leading to the
  optimisation of a populations ability to survive
  and thus reproduce in a specific environment.
• Evolutionary fitness - the measure of the ability
  to respond adequately to the environment, is the
  quantity that is actually optimised in natural
  life
• Consider a normal population of rabbits. Some
  rabbits are naturally faster than others. Any
  characteristic has a range of variation that is
  due to i) sexual reproduction and ii) mutation
• We may say that the faster rabbits possess
  superior fitness, since they have a greater
  chance of avoiding foxes, surviving and then
  breeding
• If two parents have superior fitness, there is a
  good chance that a combination of their genes
  will produce an offspring with even higher
  fitness. We say that there is crossover between
  the parents genes
• Over successive generations, the entire
  population of rabbits tends to become faster to
  meet their environment challenges in the face of
  foxes

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How GAs work

• A population of candidate solutions -
  mathematical representations - is repeatedly
  altered until an optimal solution is found
• The GA evolutionary cycle
• Starts with a randomly generated initial
  population of solutions (1st generation)
• Selects a population of better solutions (next
  generation) by using a measure of 'goodness' (a
  fitness evaluation function)
• Alters new generation population through
  crossbreeding and mutation
• Processes of selection (step 2) and alteration
  (step 3) lead to a population with a higher
  proportion of better solutions

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How GAs work (contd)

• The GA evolutionary cycle continues until an
  acceptable solution is found in the current
  population,
• or
• some control parameter such as the maximum number
  of generations is exceeded

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How solutions are represented

• A series of genes, known as a chromosome,
  represents one possible solution
• Each gene in the chromosome represents one
  component of the solution pattern
• Each gene can have one of a number of possible
  values known as alleles
• The process of converting a solution from its
  original form into a chromosome is known as
  coding

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How solutions are represented (contd)

• The most common form of representing a solution
  as a chromosome is a string of binary digits (aka
  a binary vector) eg 1010110101001
• Each bit in this string is a gene with two
  alleles 0 and 1
• Other forms of representation are also used, eg,
  integer vectors
• Solution bit strings are decoded to enable them
  to be evaluated using a fitness measure

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GA Selection

• Selection in GA based on a process analogous to
  that of biological evolution
• Only the fittest survive and contribute to the
  gene pool of the next generation
• Fitness proportional selection
• Each chromosomes likelihood of being selected is
  proportional to its fitness value.
• Solutions failing selection are bad, and are
  discarded

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Alteration Crossover Mutation

• Alteration refines good solutions from current
  generation to produce next generation of
  solutions
• Carried out by performing crossover and mutation
• Crossover by splicing two chromosomes at a
  crossover point and swapping the spliced parts
• A better chromosome may be created by combining
  genes with good characteristics from one
  chromosome with some good genes in the other
  chromosome
• Crossover carried out with a probability
  typically 0.7
• Chromosomes not crossed over are cloned

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Crossover and Mutation

• Mutation
• A random adjustment in the genetic composition
• Can be useful for introducing new characteristics
  in a population
• May be counterproductive
• Probability kept low typically 0.001 to 0.01

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An albino is a common mutation
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The typical Genetic Algorithm

1. Represent the solution as a chromosome of fixed
   length, choose size of population N, crossover
   probability pc and mutation probability pm.
2. Define a fitness function f for measuring fitness
   of chromosomes.
3. Create an initial solution population randomly of
   size N x1, x2, , xN
4. Use the fitness function f to evaluate the
   fitness value of each solution in the current
   generation f(x1), f(x2), , f(xN)

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The typical Genetic Algorithm (contd)

1. Select good solutions based on fitness value.
   Discard rest of the solutions.
2. If acceptable solution(s) found in the current
   generation or maximum number of generations is
   exceeded then stop.
3. Alter the solution population using crossover and
   mutation to create a new generation of solutions
   with population size N.
4. Go to step 4.

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The typical Genetic Algorithm (contd)
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The Travelling Salesperson Problem

• Given a set of n cities (A, B, C, ...) find a
  closed tour of all cities with the shortest total
  distance d
• Tour 'cost' may be something other than distance
  d
• This is an optimization problem with following
  constraints
• 1. Each city to be visited once and only once
• 2. Total distance travelled must be shortest
  possible
• The time required to find a solution by
  exhaustive search increases exponentially - the
  problem is NP-hard
• Possible number of tours for n cities n!/2n
• 1 million centuries for 50 cities at the rate of
  1 billion tours per sec!

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The Travelling Salesperson Problem
In 1987, Martin Groetschel and Olaf Holland found
an optimal tour of 666 interesting places in the
world. Source http//www.tsp.gatech.edu//index.ht
ml
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The TSP example (contd)

• Representation and coding of TSP solutions
• The representation might be an ordered list of
  numbers each representing a city nominally (known
  as order-based GA)
• 1) London 3) Dunedin 5) Beijing 7) Tokyo
• 2) Venice 4) Singapore 6) Phoenix 8) Victoria
• CityList1 (3 5 7 2 1 6 4 8)
• CityList2 (2 5 7 6 8 1 3 4)
• Alternatively, the representation of the solution
  may be encoded in binary on a matrix...

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The TSP example (contd)

• Representation and coding of TSP solutions
• A solution to the TSP problem is an ordered list
  of the n cities
• Each city is assigned 1 out of n possible
  positions
• Representation of the solution may be visualised
  as a table
• Each row represents a city
• Each column associated with a tour position for
  cities

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The TSP example (contd)

• The tour represented above is CAEBDC
• One possible bit string code for this solution
• 01000 00010 1000 00001 00100
• (rows written end to end)
• Binary bit strings can produce "faulty"
  chromosomes needing repair
• An integer vector scheme produced a 100 city tour
  9.4 above optimal cost

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An Optimal 100-City Tour
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Business Applications of GA

• Increasing number of industrial and business
  applications of GA since late 1980s
• In business, applications include (Kingdon 1997)
• Portfolio optimisation
• Bankruptcy prediction
• Financial forecasting
• Fraud detection
• Scheduling
• Design of complex
• machines eg. jet engines

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Business Applications of GA (contd)

• First Quadrant - investment firm in California
• Started using GA technique in 1993
• Uses GA to manage US5 billion worth of
  investments in 17 different countries
• Their evolved model earns, on average, 255 for
  every 100 invested over six years, as opposed to
  205 for other types of modeling systems (Begley
  Beals, 1995)

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Advantages of GA systems

• Useful when no algorithms or heuristics are
  available for solving a problem
• No formulation of the solution is required - only
  "recognition" of a good solution
• A GA system can be built as long as a solution
  representation and an evaluation scheme can be
  worked out
• So minimal domain expert access is required

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Advantages of GA systems

• GA can act as an alternative to -
• Expert Systems if
• number of rules is too large or
• the nature of the knowledge-base too dynamic
• Traditional optimization techniques if
• constraints and objective functions are
  non-linear and/or discontinuous

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Advantages of GA systems (cont'd)

• GA does not guarantee optimal solutions, but
  produce near optimal solutions which are likely
  to be very good
• Solution time with GA is highly predictable
  Determined by
• Size of the population
• Time taken to decode and evaluate a solution and
• Number of generations of population
• GA uses simple operations to solve problems that
  are computationally prohibitive otherwise
• Example the TSP problem

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Advantages of GA systems (cont'd)

• Because of simplicity, GA software are
• Reasonably sized and self-contained
• Easier to embed them as a module in another
  system
• GA can also aid in developing intelligent
  business systems that use other methodologies,
  eg,
• Building the rule base of an expert system
• Finding optimal neural networks

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Some issues related to GA based systems

• Level of explainability
• Capability to explain why a particular solution
  was arrived at is practically nil
• The system does not know what a fitness value
  really means
• Scalability
• Moderately scalable
• Accommodates increased number of variables by
  increasing the length of the chromosome
• But
• A longer chromosome means a larger population
  space (more potential combinations of genes)
• More time required for decoding and fitness
  evaluation

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Some issues related to GA based systems (contd)

• Data requirements
• In general, GA do not require extensive access to
  data but some applications may need it to
  evaluate solutions
• This makes the quality and quantity of data is
  important
• Local maxima
• Local maxima are regions that hold good solutions
  relative to regions around them, but which do not
  necessarily contain the best overall solutions
• The region(s) that contain the best solutions are
  called global maxima
• GAs are less prone to being trapped in local
  maxima because of the use of mutation and
  crossover

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Some issues related to GA based systems (contd)

• Premature convergence
• A GA is said to have converged prematurely if it
  explores a local maximum extensively
• It may be then dominated by very similar
  solutions within the region
• Most significant factor leading to such
  convergence is a mutation rate which is too slow
• Mutation interference is an effect opposite to
  that of premature convergence

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Some issues related to GA based systems (contd)

• Mutation interference
• Finding a mutation rate which allows the GA to
  converge but which also allows adequate
  exploration of the solution space is essential
  for satisfactory performance
• Mutation interference occurs when mutation rates
  in a GA are too high, and as a result
• Solutions are frequently or drastically mutated
• The algorithm never manages to explore any region
  of the space thoroughly
• Any good solutions found tend to be destroyed
  rapidly

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Case Study - Help Desk Task Scheduling (Dhar
Stein 1997, pp.219-227)

• GA based system developed at Moodys for
  scheduling service tasks to its customer service
  representatives
• Major constraints
• The system
• Must minimise computer downtime and customer
  dissatisfaction
• Must integrate with existing database system
  which kept track help desk requests.

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Case study constraints (contd)

• Must be flexible to
• Accommodate new types of task definitions and
  changes in employee, training etc.
• Allow administrator to modify solutions
• Must generate and reevaluate schedules quickly
  (under 15 minutes) and consistently
• Must not take administrator or CSRs away from
  their jobs for any extended period of time
• Must be developed quickly

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Case study constraints (contd)

• Should be scalable in case of future growth in
  number of requests for help and the number of
  CSRs
• Must not be too complicated for its users the
  administrator and CSRs
• The main difficulties in meeting the constraints
  
• the large number of tasks
• the large number of CSRs
• the varying capabilities of CSRs, and
• the wide variety of tasks

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Case Study - Variables and issues needing to be
considered

• The priority of a task, which is determined by
  the severity of the problem
• The length of time required to perform the task
  and how it would affect the servicing of other
  users
• The ability of various CSRs to perform different
  levels of tasks (expertise must match the
  complexity)
• Low priority tasks must not be kept waiting
  indefinitely
• The measure of goodness of a schedule to be based
  on amount of downtime each schedule cost the
  organisation.

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Possible solution methodologies considered

• Traditional linear programming (a numerical
  optimisation technique)
• A rule based expert system
• A GA based system
• ES ruled out because
• Expertise to solve this problem not expressible
  as a set of rules
• Help desk administrator not available for
  knowledge extraction
• Linear programming ruled out because
• It fails if no optimal solution can be found
• It does not produce any sub-optimal solutions,
  which is the case with GA.

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Case Study - the solution

• SOGA (Schedule Optimising for GA)
• A hybrid system consisting of GA and fuzzy system
  components
• The GA component deals with the scheduling task.
• Each task in the queue is represented by a gene
• The entire task list forms the chromosome
• Each chromosome is decoded by a scheduling module
  that assigns tasks to available CSRs who can
  perform them

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Case Study - the solution (contd)

• Fitness of each chromosome is determined by
  calculating the amount of downtime that would
  result based on the schedule represented by the
  chromosome.
• Schedules generated by the GA component are
  modified by the FS component
• SOGA runs in the background behind the help
  request tracking system
• Updates schedules based upon a predefined time
  interval (eg, every 10 or 15 minutes)
• CSRs access their current job queue through their
  interface to accept jobs.

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Case Study - Results

• The system is timely generating schedules in
  about 5 minutes.
• The solutions are found to be good by the help
  desk administrator
• The system is flexible enough to allow for task
  definitions
• The system scales up well to larger domains
  (higher number of tasks)
• The SOGA system was developed in two months using
  one programmer overseen by its designers

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REFERENCES

• Begley, S. and Beals, G. "Software au naturel."
  Newsweek, May 8, 1995, p.70
• Dhar, V., Stein, R., Seven Methods for
  Transforming Corporate Data into Business
  Intelligence., Prentice Hall 1997, pp. 126-148,
  203-210.
• Goldberg, D. E., Genetic and Evolutionary
  Algorithms Come of Age, Communications of the
  ACM, Vol.37, No.3, March 1994, pp.113-119.
• Holland, J. H., Adaptation in Natural and
  Artificial Systems, Univ. of Michigan Press,
  1975.
• Kingdon, J., Intelligent Systems and Financial
  Forecasting, Springer Verlag, London 1997.
• Medsker,L., Hybrid Intelligent Systems, Kluwer
  Academic Press, Boston 1995.
• Michalewicz, Z., Genetic Algorithms Data
  Structures Evolution Programs, Springer-Verlag,
  Berlin 1996.
• Negnevitsky, M. Artificial Intelligence A Guide
  to Intelligent Systems, Addison-Wesley 2005.