genetic algorithm

1. Macheine Learning
Genetic Algorithm

2. Introduction
● It is an optimization algo
● Used for phylogenetic idnetities
● Searches large amount of data
● To get a best result

3. Simulation
● It works on a simulation of natural selection
● In natural selection, individuals are encoded
soultions to problems of intrest
● Labelled phylogenetic trees are individuals
● And differential reproduction is affected by
allowing the number of offsprings produced by
each individual to be propotional to that
individual's rank likelihood score.

4. ● Natural selection increases the average
likelihood in the evolving population of
phtlogenetic trees.
● And genetic algorithm is allowed to proceed
until the likelihood of best individual ceases to
improve over time.

5. ● There is a fitness function
● There are candidate solutions generated at
randon
● Candidate soultions are encoded as
chromosomes

6. Fitness function
● Part of chromosome is exchanged
● Resulting recombinations are checked wrt
fitness function
● The highest scoring chromosomes are selected
● They are 'the best ones'

7. Fitness function
● Part of chromosome is exchanged
● Resulting recombinations are checked wrt
fitness function
● The highest scoring chromosomes are selected
● They are 'the best ones'