Genetic Algorithms

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# Genetic Algorithms - PowerPoint PPT Presentation

Genetic Algorithms. Brandon Andrews. Topics. What are genetic algorithms? 3 steps Applications to Bioinformatics. What are genetic algorithms?. Invented and published in 1975 by John Holland Cells have DNA which define properties

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Presentation Transcript

### Genetic Algorithms

Brandon Andrews

Topics

What are genetic algorithms?

3 steps

Applications to Bioinformatics

What are genetic algorithms?
• Invented and published in 1975 by John Holland
• Cells have DNA which define properties
• Reproduction crosses DNA from both parents merging properties from both
• During this step random mutations can occur
• A test of the fitness of the organism is performed
• Scores the organism against others based on criteria for survival
• Essentially evolution
3 Steps
• Selection step
• Based on the calculate fitness
• Reproduction step
• Mutations
• Strategies for crossing
• Termination step
• When the goal is met
Steps Expanded
• 1) Generate random properties (chromosomes) for N entities
• 2) Calculate their fitness and discard ones that fall below the threshold
• Can be determined through a simulation
• 3) Randomly cross over pairs that survive the selection step
• Also randomly choose properties and mutate them. This could be as simple as jittering them
• 4) Go to step 2 until a goal is reached
• Return the best set of properties
Fitness Function

Could be anything

The goal is to minimize or maximize the fitness function normally after each step

Crossover Probability
• How often crossovers happens
• 0% represents if no crossover and both parents are simply moved to the next step
• 100% represents that all of the parents are crossed and only their children are move to the next step
• The idea is that hopefully the good properties of both parents are merged or the good parent is preserved completely if it has no flaws that can be fixed via a crossing pair
Mutation Probability
• The probability that part of the chromosome is changed after a crossing
• 0% if none of it is changed
• Not useful since variety is needed to approach the best solution or you’re stuck with the first generated properties
• 100% if all of it is changed
• Not useful since it negates the point of crossing at all, causes a random search essentially
• The concept is to stop the algorithm from halting at a local maximum. The mutations have a chance to generate small better changes
Termination
• When the expected error is low
• Sometimes it’s hard to calculate an error since the solution isn’t known
• Or when the results stop minimizing for a few iterations or stops increasing depending on the problem
Approximate Solutions
• Might be obvious, but genetic algorithms are by design approximate solutions since they attempt to optimize to a solution
• Perfection is only as good as the fitness function and the number of iterations, crossing and mutation probabilities
Applications
• Multiple Sequence Alignment
• Initial generation – random generation of an alignment based on the alignments of the given sequences
• No authors agree on the initial size of the population
• Selection via a tournament style pairing crossing the possible alignments
• The fitness function
• “Sum of pair” Objective Function (everyone uses a different one)
• The survival rate is different for each alignment
• Sum all alignment scores together and take a percentage for each alignment
• Basically better alignments have a higher percentage to survive

Reproduction

• Crossing uses a “one-point crossover”
• Takes the first half of the first alignment and cross if with the second half of the second parent
• ABCD and EFGH -> ABGH
• Or “point-to-point crossover”
• Random index is chosen
• ABCD and EFGH -> ABCH
• Mutation
• Remove or insert a gap into the alignment
References