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Memetic Algorithms
Overview
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Philosophy Behind Memetics
Genetic Algorithm – Intuition and Structure
Genetic Algorithm Operators
Memetic Algorithms
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TSP Using Memetic Algorithm
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Genes and biological evolution
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A gene is a unit of biological information
transferred from one generation to another.
Genes determine our physical traits, what you
look like, what you inherit from either one of
your parents.
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Biological
Evolution
• Natural Selection
• Survival of The Fittest
• Origin of New Species
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Examples of Biological Evolution and
Natural Adaptation
Gills in Pisces
Frog Skin
Hollow Bones in Birds
Biological Evolution of Human
• Characteristic Thumb
• Erect Vertebral Column
• Lower Jaw
Biological
Evolution
Cultural
Evolution..??
Source: www.wikipedia.org
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Biological
Evolution
Meme..!!!
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Meme
“the basic unit of cultural transmission, or
imitation”
- Richard Dawkins
“an element of culture that may be considered
to be passed on by non-genetic means”
- English Oxford Dictionary
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Examples of Meme
Fashion
 Latest trends are ideas of fashion designers
Science
 Scientists sharing their thoughts
Literature
 Novel, poetry
Music
 Even birds are found to imitate songs of other birds!!!
Genes and Memes, where they are similar
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Genes propagate biologically from chromosome
to chromosome
Memes propagate from brain to brain via
imitation
Survival of fittest in meme
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Concept of God is survived though no scientific
evidence is present
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Genes and Memes, where they differ
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Genes are pre-decided
Genes are static through generations, memes
can be changed!
Memes allow improvement
After learning language, we contribute to it through
literature
 New heuristics to 8-puzzle problem solved in class
 We use this property to improve genetic algorithms
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Genetic Algorithm
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solves (typically optimization) problems by
combining features of complete solutions to
create new populations of solutions.
applicable when it is hard or unreasonable to try
to completely identify a subproblem hierarchical
structure or to approach the problem via an
exact approach.
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Solving the Traveling salesman
problem with a Memetic Algorithm
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Memetic Algo for TSP-representation
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Array pop stores population
Size of pop=P
No of cities=N
Tour represented as 1234....N
Fitness function-cost of the tour
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TSP - Crossover
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Distance Preserving Crossover
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d(p1,p2) = d(p1,child) = d(p2,child)
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d(x, y) = #edges not common in x and y
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Distance Preserving Crossover
Source: B. Freisleben et al, “New Genetic Local Search Operators for the Traveling
Salesman Problem”
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Performance
Source: Slides of A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
Hybridisation with other techniques: Memetic Algorithms
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Conclusion
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A genetic algorithm promises convergence
but not optimality.
But we are assured of exponential
convergence, possibly at different optimal
chromosomes.
Do very well in identifying the regions where
those optima lie.
Optimal solution=Genetic Algo + Local
Search
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References
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R. Dawkins, “The Selfish Gene – new edition”, Oxford
University Press, 1989 pp 189-201
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David E. Goldberg, Genetic Algorithms in Search, Optimization and
Machine Learning, 1st edition, Addison-Wesley Longman Publishing
Co., 1989 pp 170-174
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B. Freisleben and P. Merz, New Genetic Local Search Operators for
the Traveling Salesman Problem. In H.-M. Voigt, W. Ebeling, I.
Rechenberg, and H.-P. Schwefel, editors, Proceedings of the 4th
Conference on Parallel Problem Solving from Nature - PPSN IV,
pages 890--900. Springer, 1996
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S. Lin and B. W. Kemighan, An effective heuristic algorithm for the
Traveling Salesman problem, Operation Research 21 (1973) 498516
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Thank you!
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