This document provides an introduction to genetic algorithms and genetic programming. It discusses how genetic algorithms are inspired by natural selection and genetics, using operations like crossover and mutation to evolve solutions to problems. It also outlines the basic steps of a genetic programming framework, including generating an initial population randomly, evaluating fitness, selecting parents, performing crossover and mutation to create offspring, and iterating until a solution is found. Representation using syntax trees and example genetic operators like single point crossover are described.

1. Introduction to Genetic
Algorithms
Dr R Ramya, AP(Sl G)
Department of CSE
SRM Institute of Science and Technology
Ramapuram Campus, Chennai

2. Genetic Algorithms
• Genetic algorithms are a part
of evolutionary computing, which is
a rapidly growing area of artificial
intelligence.
• It is a search-based optimization
technique based on the principles
of Genetics and Natural Selection
(Survival of the Fittest).
• It is frequently used to find optimal
or near-optimal solutions to difficult
problems which otherwise would
take a lifetime to solve.

3. Ant Colony Optimization
• Real ants follow a path between
nest and food source.
• An obstacle appears on the path:
• Ants choose whether to turn left or
right with equal probability.
• Pheromone is deposited more
quickly on the shorter path.
• All ants have chosen the shorter
path.

4. Genetic Algorithms-A Biological
Background
• All living organisms consist of
cells.
• In each cell there is the same
set of chromosomes.
• Chromosomes are strings
of DNA and serves as a model
for the whole organism.
• DNA carrying genetic
instructions for the
development, functioning,
growth and reproduction of
all known organisms.

5. Framework of GP
5

6. Outline of the Basic Genetic
Programming
• Step 1 Generate random population of n chromosomes (suitable solutions for the
problem)
• Step 2 Evaluate the fitness f(x) of each chromosome x in the population
• Step 3 Create a new population by repeating following steps until the new
population is complete
– Select two parent chromosomes from a population according to their fitness
(the better fitness, the bigger chance to be selected)
– With a crossover probability cross over the parents to form a new offspring
(children). If no crossover was performed, offspring is an exact copy of
parents.
– With a mutation probability mutate new offspring at each locus (position in
chromosome).
– Place new offspring in a new population
• Step 4 Use new generated population for a further run of algorithm
• Step 5 If the end condition is satisfied, stop, and return the best solution in current
population
• Step 6 Go to step 2

7. Representation
• Expressed as Syntax trees rather than lines of
code.
• Variables and constants are the leaves of the
trees, which are called Terminals, while the
arithmetic operations are called Functions.
• The Root node acts as a glue

8. Example
i =1;
while (i < 20)
{
i = i +1
}

9. Genetic Operators

10. Reproduction in GP
• Crossover
• Mutation

12. Crossover Operations
• Single Point Crossover
• Two-point Crossover
• Uniform Crossover
• Multi-point Crossover

13. Single Point Crossover

14. Two-point Crossover

15. Multi-point Crossover

16. Uniform Crossover

17. Selection

18. Selection Operators

19. Merits of Genetic Programming
• After the termination criteria is satisfied the
single best program in population produced is
the solution.
• It saves time by freeing the human from
having to design complex algorithms.
• Not only designing the algorithms but
creating ones that give optimal solutions.