Paper Review for ENGG6140 Memetic Algorithms

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Paper Review for ENGG6140Memetic Algorithms

• By Jin Zeng
• Shaun Wang
• School of Engineering
• University of Guelph
• Mar.
  18, 2002

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Contents

• Introduction
• MA and GA
• Basic MA
• Examples
• Conclusions

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IntroductionHistory of MA

• Meme word introduced by Richard Dawkins when
  he describe cultural evolution in his best-seller
  book The Selfish Gene (76).
• Memetic Algorithms Analogous role of gene but
  in the field of cultural evolution.Memetic
  Algorithms , firstly proposed by P. Moscarto.
  (89)
• MA has been widely applied in optimization and
  solving many NP hard problems successfully.

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Introduction What is Meme?

• Meme is the basic unit of cultural transmission,
  in analagy to gene in genetic transmission.
• Meme is replicated by imitation.
• It can be changed by the owner for adaption.
• Examples ideas, clothing fashion and NBA.
• High-extent variation occurs in cultural
  transmission.

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IntroductionCultural Evolution

• When a meme passed between individuals, the
  individual will adapt the meme as it sees best.
• Shared characteristics are not inherited due to
  simple processes of recombination of previous
  solutions
• Using historical information and an external
  logic to speed-up the process.

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Introduction What is MA?

• MA mimics the process of cultural evolution
• Characterization of evolutionary algorithms that
  can hardly fit the GAs methaphor - no, or small,
  relation with biology
• Hybrid GAs ?
  MAs
• Scatter Search (Glover, 77) ? MAs

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IntroductionWhy MA?

• In general, there are two ways to searching the
  solution space
• Exploration Investigate the new and unknown
  areas in the search space
• Exploitation Make use of knowledge found before
  to help find better solutions
• Both are necessary but contradictory in solving
  an optimization problem.

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Introduction Why MA? (cont.)

• The limitation of former algorithms
• GA using parallel searching technique.
• Good at avoiding local optima
• Not well suited for finely tuned search.
• LS improvement heuristics.
• Find local optima quickly.
• Highly depending on the start point.
• Hard to find a global optimum.

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Introduction Why MA? (cont.)

• Combination of GA Local Search MA
• GA For exploration
• LS For exploitation
• Result higher efficiency and better effect.

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IntroductionCombination Methods

• Two kinds of Combinations

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MA and GASimilarities

• Both MA and GA model an evolutionary process.
• Both MA and GA have the process of
  generalization, recombination (crossover) and
  mutation. Some changes occur in the process.
• Both MA and GA use fitness function to evaluate
  the changes in the process thus both of them are
  applied in optimization successfully.

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MA and GADifference
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Basic MAFlow Chart Process
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Basic MAPseudo Code of MA
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Basic MAGeneralization
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Basic MACrossover
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Basic MAMutation
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Basic MALocal Search

• Full Local Search and Partial Local Search
• Demo of FLS

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Basic MADemonstration of MA

• Example Problems Y f(x)
• Parameters of MA
• Population 5
• Xover rate0.4 ( of Xover 5x0.42)
• Mutation rate 0.4 ( of Mutation 5x0.42)
• Local Search Full

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Basic MADemonstration of MA (Continued)
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Basic MADemonstration of MA (Continued)
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Basic MAEffect of Crossover and Mutation

• Both can be used for exploring the search space
  by jumping to new regions to start new local
  search
• Crossover
• Searching the region between two or more
  specified points
• Mutation
• Searching the undirected region randomly

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Basic MAAdvantage of MA

• Combining the advantages of GA and LS while avoid
  the disadvantages of both
• GA ensures wide exploration in the solution space
• Through local search, the space of possible
  solutions can be reduced to the subspace of local
  optima.
• When the scale of problem increases, the
  advantages becomes remarkable.

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Basic MADisadvantage of MA

• The proportion of computations used in
  exploration and exploitation depends on the real
  optimization problem.
• It is hard to determine the best depth of local
  search,.

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MA ExamplesSome Implementation Examples of MA

• Quadratic Assignment Problem (QAP)
• Traveling Salesman Problem (TSP)
• Vehicle Routing
• Graph Partitioning
• Scheduling
• The Knapsack Problem

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MA ExamplesApply Local Search to MA in QAP

• For any permutation solution being explored, the
  procedure for the local search be executed once
  or several times partial local search (PLS)
• The procedure for the local search be repeated
  many times until no further improvement is
  possible full local search (FLS)

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MA ExamplesDerived Two different MAs for QAP

• PGA starts with an initial population of
  randomly generated individuals. For each
  individual, after xover and mutation, a PLS is
  performed.
• FGA relies on FLS, full local search are
  carried out on all individuals at the beginning
  and at the end of a SGA run.

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MA ExamplesBriefly Steps involved for the PGA

• The steps for PGA is same as the Basic MA.
• The procedures for the local search only executed
  once or several times after each xover and
  mutation.

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MA ExamplesBriefly Steps involved for the FGA

• 1. Randomly generate an initial population.
  Perform FLS on each individual.
• 2 While terminating criterion is not reached,
  continue with procedures as spelled out for the
  SGA.
• 3 Perform FLS on the best solution and output
  the final solution.

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MA ExamplesComparison of FGA and PGA

• The effectiveness of FLS depends on the starting
  solution and the exchange routine.
• PLS can be carried out more frequently, the
  algorithm is therefore able to spread out the
  search by exploring many small-localized regions,
  thus reducing the likelihood of the algorithms
  being trapped in a local optimum.

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MA ExamplesComparison of FGA and PGA (cont.)

• As the size of the problem scales up, it is
  difficult to carry out FLS freely due to its
  great computational intensity.
• PLS is carried out for almost all the individuals
  in addition to the SGA evolutionary mechanisms,
  the capability of the SGA in evolving towards
  fitter individuals is greatly enhanced.

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MA ExamplesComparison of FGA and PGA (cont.)

• FLS limits the exploratory capability of the SGA,
  it will reduce the chance of the FGA reaching the
  global optimum.
• PGA has a greater chance of obtaining the global
  optimum as compared to FGA.

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MA ExamplesComparison of a typical run on
problem Els19 for SGA, PGA and FGA
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Conclusion

• MA provides a more efficient and more robust way
  to the optimization problem.
• MA combines global and local search by using EA
  to perform exploration while another local search
  method performs exploitation.
• MA can solve some typical optimization problem
  where other meta-heuristics have failed.

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• Thank you!