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
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
