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'