| Type: | Package |
| Title: | Genetic Algorithm Optimization |
| Version: | 0.3.3 |
| Description: | Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package is for learning purposes and allows users to optimize various functions or parameters by mimicking biological evolution processes such as selection, crossover, and mutation. Ideal for tasks like machine learning parameter tuning, mathematical function optimization, and solving an optimization problem that involves finding the best solution in a discrete space. |
| License: | MIT + file LICENSE |
| URL: | https://danymukesha.github.io/genetic.algo.optimizeR/, https://github.com/danymukesha/genetic.algo.optimizeR |
| BugReports: | https://github.com/danymukesha/genetic.algo.optimizeR/issues |
| Imports: | dplyr, ggplot2, magrittr, rsconnect, stats, stringr, tinytex, biocViews, DiagrammeR |
| Suggests: | BiocStyle, knitr, learnr, rmarkdown, spelling, testthat (≥ 3.0.0) |
| Depends: | R (≥ 4.1.0) |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Language: | en-US |
| biocViews: | ExperimentalDesign, Technology |
| NeedsCompilation: | no |
| Packaged: | 2025-01-24 17:24:06 UTC; dany.mukesha |
| Author: | Dany Mukesha 
[aut, cre] |
| Maintainer: | Dany Mukesha <danymukesha@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-01-24 18:30:07 UTC |
genetic.algo.optimizeR: Genetic Algorithm Optimization
Description
Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package is for learning purposes and allows users to optimize various functions or parameters by mimicking biological evolution processes such as selection, crossover, and mutation. Ideal for tasks like machine learning parameter tuning, mathematical function optimization, and solving an optimization problem that involves finding the best solution in a discrete space.
Author(s)
Maintainer: Dany Mukesha danymukesha@gmail.com (ORCID)
See Also
Useful links:
Report bugs at https://github.com/danymukesha/genetic.algo.optimizeR/issues
Crossover of selected parents from the fitting population
Description
This function performs crossover between the selected individuals that fit the best
based on the predefined condition(aim/objective).
e.g.: To optimize the function f(x) = x^2 - 4x + 4
to find the value of x that minimizes the function.
x: represents a possible value the an individual from the population can have.
Usage
crossover(selected_parents, offspring_size)
Arguments
selected_parents |
The list of selected individuals from the population. |
offspring_size |
The number of offspring that the selected population should have. |
Value
The output expected should be a list of offspring for the next generation.
Author(s)
Dany Mukesha
Examples
population <- c(1, 3, 0)
# Evaluate fitness
fitness <- evaluate_fitness(population)
print("Evaluation:")
print(fitness)
# Selection
selected_parents <- selection(population, fitness, num_parents = 2)
print("Selection:")
print(selected_parents)
# Crossover
offspring <- crossover(selected_parents, offspring_size = 2)
print("Crossover:")
print(offspring)
Evaluating the fitness of the population
Description
The function described below applies this equation:
f(x) = x^2 - 4x + 4
It assesses every individual x of a given population,
to then provide an overview of how that population fits the equation.
Usage
evaluate_fitness(population)
Arguments
population |
The list of individuals of the population. |
Value
The output expected should be a list of f(x) values
calculated from individuals in the population.
Author(s)
Dany Mukesha
Examples
# example of usage
population <- c(1, 3, 0)
# Evaluate fitness
genetic.algo.optimizeR::evaluate_fitness(population)
Initialize population
Description
The function described below creates a population of individuals(values).
Usage
initialize_population(population_size, min = -100, max = 100)
Arguments
population_size |
The number of individuals of the population. |
min |
The minimum number an individual could have. |
max |
The maximum number an individual could have. |
Details
(Note: the values are random and the population should be highly diversified)
The space of x value is kept integer type and on range from 0 to 3, for simplification.
Value
The output expected should be a list of individuals of the population with the size indicated in the input.
Author(s)
Dany Mukesha
Examples
# example of usage
genetic.algo.optimizeR::initialize_population(population_size = 3, min = 0, max = 3)
Mutating the offspring
Description
The function described below mutates offspring from the selected individuals that fit the best
based on the predefined condition(aim/objective).
e.g.: To optimize the function f(x) = x^2 - 4x + 4
to find the value of x that minimizes the function.
x: represents a possible value the an individual from the population can have.
Usage
mutation(offspring, mutation_rate)
Arguments
offspring |
The list of offspring. |
mutation_rate |
The probably of a single offspring to be modified/mutated. |
Details
The mutation is needed to increase the diversity in the population and help the next generation close to the fitness.
Value
The output expected should be a list of mutated offspring.
Author(s)
Dany Mukesha
Examples
# example of usage
population <- c(1, 3, 0)
# Evaluate fitness.
fitness <- genetic.algo.optimizeR::evaluate_fitness(population)
print("Evaluation:")
print(fitness)
# Selection
selected_parents <- genetic.algo.optimizeR::selection(population, fitness, num_parents = 2)
print("Selection:")
print(selected_parents)
# Crossover
offspring <- genetic.algo.optimizeR::crossover(selected_parents, offspring_size = 2)
print("Crossover:")
print(offspring)
# Mutation
mutated_offspring <- genetic.algo.optimizeR::mutation(offspring, mutation_rate = 0)
# (no mutation in this example)
print(mutated_offspring)
Replacing non-selected individual(s)
Description
This function replace the individual(s) that was/were not selected (i.e. not the best fit)
based on the predefined condition(aim/objective).
e.g.: To optimize the function f(x) = x^2 - 4x + 4
to find the value of x that minimizes the function.
x: represents a possible value the an individual from the population can have.
Usage
replacement(population, offspring, num_to_replace)
Arguments
population |
The list of individuals of the population |
offspring |
The list of offspring. |
num_to_replace |
The number of selected individuals that should be replaced in the population. |
Value
The output expected should be a list of selected individuals that fit the best with the predefined aim.
Author(s)
Dany Mukesha
Examples
# example of usage
population <- c(1, 3, 0)
# Evaluate fitness
fitness <- genetic.algo.optimizeR::evaluate_fitness(population)
print("Evaluation:")
print(fitness)
# Selection
selected_parents <- genetic.algo.optimizeR::selection(population, fitness, num_parents = 2)
print("Selection:")
print(selected_parents)
# Crossover and mutation
offspring <- genetic.algo.optimizeR::crossover(selected_parents, offspring_size = 2)
mutated_offspring <- mutation(offspring, mutation_rate = 0) # (no mutation in this example)
print(mutated_offspring)
# Replacement
population <- genetic.algo.optimizeR::replacement(population, mutated_offspring, num_to_replace = 1)
print("Replacement:")
print(population)
Selecting the fitting the population
Description
The function described below selects the individuals that fit the best
based on the predefined condition(aim/objective).
e.g.: To optimize the function f(x) = x^2 - 4x + 4
to find the value of x that minimizes the function.
x: represents a possible value the an individual from the population can have.
Usage
selection(population, fitness, num_parents)
Arguments
population |
The list of individuals of the population |
fitness |
The list of individuals(value) obtained from the function
of |
num_parents |
The number of selected individuals that fit the best with the predefined aim. |
Value
The output expected should be a list of selected individuals that fit the best with the predefined condition(aim/objective).
Author(s)
Dany Mukesha
Examples
# example of usage
library(genetic.algo.optimizeR)
population <- c(1, 3, 0)
# Evaluate fitness
fitness <- genetic.algo.optimizeR::evaluate_fitness(population)
print("Evaluation:")
print(fitness)
# Selection
selected_parents <- genetic.algo.optimizeR::selection(population, fitness, num_parents = 2)
print("Selection:")
print(selected_parents)