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Summary IB Computer Science: Genetic Algorithms Unveiled $8.99
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Summary IB Computer Science: Genetic Algorithms Unveiled
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  • Course
  • IB computer science HL
  • Institution
  • The University Of Birmingham (UBir)

Delve into the intricacies of genetic algorithms, tailored to the IB Computer Science curriculum. These student notes provide a comprehensive understanding of how genetic algorithms, inspired by nature's evolutionary principles, play a pivotal role in problem-solving within the realm of computer sc...

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Document information

  • October 13, 2023
  • 11
  • 2022/2023
  • Summary

Subjects

  • ib computer science
  • databases
  • sql
  • structured data
  • database design
  • exam preparation
  • international baccalaureate

Written for

  • The University of Birmingham (UBir)
  • Unknown
  • IB computer science HL
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https://www.mathworks.com/help/gads/what-is-the-genetic-algorithm.html#:~:text=The%20g
enetic%20algorithm%20is%20a,a%20population%20 of%20 individual%20solutions.


Genetic algorithm - a search heuristic

Inspired by natural evolution




Five phases are considered in a genetic algorithm.

1. Initial population
2. Fitness function
3. Selection
4. Crossover
5. Mutation


An individual is a solution to the problem, and a population is a set of individuals
Individuals are characterized by parameters (genes). A chromosome is a string of genes.

The fitness function determines how effective the solution is.

A pair of parents are chosen based on their fitness

Crossover swap the genes of the parent to create the offspring. The crossover point is
randomized.




Mutation also causes from genes to be changed, but with low probability

When a set of population converges (offspring are similar to parents) the solution is found.

, Each generations are better than the last

https://www.wikiwand.com/en/Genetic_algorithm

Solutions are traditionally represented by binary

Initial population is usually generated randomly

Other heuristics may be employed as well, such as speciation where crossover between
similar parents are penalized.

Doesn’t scale well with complexity

Elitism - Best version is unaltered to ensure that the quality doesn’t drop in the next
simulation

https://www.youtube.com/watch?v=kHyNqSnzP8Y&ab_channel=MITOpenCourseWare

https://www.geeksforgeeks.org/genetic-algorithms/




https://www.ripublication.com/ijcir17/ijcirv13n7_15.pdf

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