Genome and Genetic Algorithm Library

Overview

This library consists of two main classes:

• GeneticAlgorithm: Implements a genetic algorithm to simulate and solve optimization problems using evolutionary strategies.
• Genome: Provides tools for biological sequence analysis, including DNA and RNA generation, transcription, translation, and complementary strand operations.

Features

GeneticAlgorithm Class

• Generate Population: Create an initial population of binary chromosomes.
• Fitness Evaluation: Map chromosomes to their fitness values using a custom fitness function.
• Selection: Use roulette wheel selection to choose individuals based on fitness.
• Mutation: Apply mutation to chromosomes with a given probability.
• Crossover: Perform crossover between pairs of chromosomes.
• Run: Execute the genetic algorithm over multiple generations to evolve solutions.

Genome Class

• Generate Single-Stranded DNA: Create random single-stranded DNA sequences.
• Generate RNA: Create random RNA sequences.
• Complementary DNA Strand: Compute the complementary DNA strand for a given sequence.
• Check DNA: Verify if two DNA sequences are complementary.
• Reading Frames: Calculate the six possible reading frames of a DNA sequence.
• Translate DNA: Convert DNA sequences to RNA and then to protein sequences.
• Protein Synthesis: Translate RNA sequences into proteins using a codon dictionary.

Installation

To use this library, you need to have Ruby installed. You can check your Ruby installation by running:

ruby -v

# Clone the repository and navigate to the directory:
# Copy code
git clone https://github.com/TMWcodes/https://github.com/TMWcodes/Evolutionary_Algorithms.git
cd https://github.com/TMWcodes/Evolutionary_Algorithms
Usage GeneticAlgorithm
ruby Copy code require './synth_genome'

Usage

GeneticAlgorithm Example

require './synth_genome'

ga = GeneticAlgorithm.new
population = ga.generate(8) # Generate a population of binary strings of length 8

fitness_function = lambda do |chromosome|
  ideal = '10101010'
  1.0 * ideal.chars.zip(chromosome.chars).count { |x, y| x == y } / ideal.size
end

result = ga.run(8) # Run the genetic algorithm for 100 iterations

Genome Example

require './bio_genome'

genome = Genome.new

# DNA and RNA operations
dna = genome.generate_ssDNA(10)
puts "DNA is #{dna}"

cdna = genome.complementary_DNA_strand(dna)
puts "Complementary strand: #{cdna}"

rna = genome.dna_to_rna(dna)
puts "mRNA: #{rna}"

protein = genome.protein(rna)
puts "Protein: #{protein}"

frames = genome.six_reading_frames(dna, cdna)
puts "Reading frames: #{frames}"

License

This project is licensed under the MIT License - see the LICENSE file for details.

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