1. Memetic Algorithms

2. Overview Philosophy Behind Memetics
Genetic Algorithm – Intuition and Structure
Genetic Algorithm Operators
Memetic Algorithms
TSP Using Memetic Algorithm

3. Genes and biological evolution
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.

4. Survival of The Fittest Origin of New Species
Biological Evolution
Natural Selection
Survival of The Fittest
Origin of New Species

5. 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

6. Biological Evolution
Cultural Evolution..??
Source:

7. Biological Evolution
Meme..!!!

8. 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

9. Examples of Meme Fashion Science Literature Music
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!!!

10. Genes and Memes, where they are similar
Genes propagate biologically from chromosome to chromosome
Memes propagate from brain to brain via imitation
Survival of fittest in meme
Concept of God is survived though no scientific evidence is present

11. Genes and Memes, where they differ
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

12. Genetic Algorithm
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.

17. Solving the Traveling salesman problem with a Memetic Algorithm

18. Memetic Algo for TSP-representation
Array pop stores population
Size of pop=P
No of cities=N
Tour represented as N
Fitness function-cost of the tour

19. TSP - Crossover Distance Preserving Crossover
d(p1,p2) = d(p1,child) = d(p2,child)
d(x, y) = #edges not common in x and y

20. Distance Preserving Crossover
Source: B. Freisleben et al, “New Genetic Local Search Operators for the Traveling Salesman Problem”

23. Hybridisation with other techniques: Memetic Algorithms
Performance
Source: Slides of A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
Hybridisation with other techniques: Memetic Algorithms

24. Conclusion
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

25. References
R. Dawkins, “The Selfish Gene – new edition”, Oxford University Press, 1989 pp
David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1st edition, Addison-Wesley Longman Publishing Co., pp
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 Springer, 1996
S. Lin and B. W. Kemighan, An effective heuristic algorithm for the Traveling Salesman problem, Operation Research 21 (1973)

26. ?

27. Thank you!