UNIT: 6 (Genetic Algorithm)
Genetic Algorithms(GAs):
• Genetic Algorithms(GAs) are adaptive heuristic search
algorithms that belong to the larger part of evolutionary
algorithms.
• Genetic algorithms are based on the ideas of natural
selection and genetics.
• These are intelligent exploitation of random searches
provided with historical data to direct the search into the
region of better performance in solution space.
• They are commonly used to generate high-quality
solutions for optimization problems and search
problems.
In this selection is directly propositional to
fitness.
Rank Selection:
Tournament Selection:
Tournament selection is a genetic algorithm technique
that chooses individuals from a population by running
"tournaments" between randomly selected individuals:
1.Randomly select a few individuals, or "chromosomes",
from the population
2.Run several "tournaments" between the selected
individuals
3.The winner of each tournament is selected to perform
crossover
In tournament selection, individuals with higher fitness
scores are more likely to win the match and advance to the
next population.
Example of Tournament
Selection:
F
15
B F
6 15
B C F H
6 4 15 9
A B C D E F G H
3 6 4 1 7 15 5 9
Steady State Selection:
Mutation Operator:
Different approaches of mutation:
Bit Flip Mutation
In this bit flip mutation, we select one or more random
bits and flip them. This is used for binary encoded GAs.
Swap Mutation
In swap mutation, we select two positions on the chromosome at
random, and interchange the values. This is common in
permutation based encodings.
Scramble Mutation
Scramble mutation is also popular with permutation
representations. In this, from the entire chromosome,
a subset of genes is chosen and their values are
scrambled or shuffled randomly.
Inversion Mutation
In inversion mutation, we select a subset of genes
like in scramble mutation, but instead of shuffling
the subset, we merely invert the entire string in the
subset.
Thank you!
