Brandon Andrews

 What are genetic algorithms?  3 steps  Applications to Bioinformatics

 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

 Selection step Based on the calculate fitness  Reproduction step Mutations Strategies for crossing  Termination step When the goal is met

 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

 Could be anything  The goal is to minimize or maximize the fitness function normally after each step

 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

 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

 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

 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

 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

 Obitko M. (1998). Genetic Algorithms. Retrieved from ‑ algorithms/  Radenbaugh A. (2008). Applications of genetic algorithms in bioinformatics. Retrieved from cgi?article=4491&context=etd_theses