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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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genetic algorithms

Genetic Algorithms

Brandon Andrews


What are genetic algorithms?

3 steps

Applications to Bioinformatics

what are genetic algorithms
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
3 Steps
  • Selection step
    • Based on the calculate fitness
  • Reproduction step
    • Mutations
    • Strategies for crossing
  • Termination step
    • When the goal is met
steps expanded
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
Fitness Function

Could be anything

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

crossover probability
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
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
  • 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
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
  • 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


    • 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