Memetic Algorithms

1
Memetic Algorithms

• Elena Marchiori

2
Bio-Inspired Algorithms Course

• Topics
• Ant Colony Optimization (ACO)
• Memetic Algorithms
• Multi-objective Optimization
• Structure
• Theory learn above mentioned topics
• Practice work in groups on application-oriented
  problems
• See http//www.cs.ru.nl/elenam/BIA.html

3
Overview of todays sample lesson on memetic
algorithms

• Introduction
• Genetic Algorithm Intuition and Structure
• Genetic Algorithm Operators
• Memetic Algorithms
• MAXclique Using Memetic Algorithm

4
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.

5
Biological Evolution

• Natural Selection
• Survival of The Fittest
• Origin of New Species

6
Examples of Biological Evolution and Natural
Adaptation

• Frog Skin
• Hollow Bones in Birds
• Biological Evolution of Human
• Characteristic Thumb
• Erect Vertebral Column
• Lower Jaw

7
Biological Evolution
Cultural Evolution..??
Source www.wikipedia.org
8
Biological Evolution
Meme..!!!
9
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

10
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!!!

11
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

12
Genes and Memes, where they differ

• Genes are pre-decided
• Genes are (almost always) static through
  generations, memes can be changed!
• Memes allow improvement
• After learning language, we contribute to it
  through literature
• New heuristics to search problems
• We use this property to improve algorithms

13
Genetic Algorithm

• solves (typically optimization) problems by
  combining features of candidate solutions to
  create new populations of candidate solutions.
• applicable to complex optimization problems,
  e.g., when it is hard to tackle the problem via
  an exact search approach.

14
Genetic Algorithm
While not stop criterion do
Evaluate Pop
Return the best solution in Pop
15
Crossover

• Purpose to combine features of feasible
  solutions already visited in order to provide new
  potential candidate solutions with better
  objective function value.
• Mechanism that restarts the search by exploring
  the space between solutions.

parents
offspring
0 0 0 0 0 0 0
1 1 1 1 1 1 1
16
Mutation