Genetic Algorithm Implementation

This code implements a simple genetic algorithm (GA) for optimization problems using binary chromosomes, tournament selection, one-point crossover, mutation, and replacement.

Overview

Genetic Algorithm is a search heuristic inspired by the process of natural selection. This code demonstrates the main steps of a GA: initialization, selection, crossover, mutation, and replacement, using binary strings as chromosomes and fitness defined by the sum of bits.

Features

• Binary Chromosomes: Each individual is represented as a binary string of fixed length (default: 20).
• Population Initialization: Randomly generates initial population.
• Tournament Selection: Selects parents based on their fitness (sum of bits).
• One-Point Crossover: Combines parents to create offspring by swapping segments at a random point.
• Bit-Flip Mutation: Randomly flips bits in the offspring with a given mutation rate.
• Replacement: Replaces the least fit individuals in the population with the new offspring.

Usage

1. Input Parameters:
   The program asks for the following inputs:

   • Generation number: Number of generations to run.
   • Population number: Number of individuals in the population.
   • Tournament selection ratio: Fraction of the population to consider for selection.
   • Mutation ratio: Fraction of bits to mutate in each offspring.

2. Execution:
   The algorithm runs for the specified number of generations, printing the state of the population and the results of each step (selection, crossover, mutation, replacement) for each generation.

Example

This will run the genetic algorithm for 3 generations, with a population of 5, tournament selection ratio of 0.4, and mutation ratio of 0.1.

Code Structure

• GeneticAlgorithm Class:
  • __init__: Initializes parameters and population.
  • CreateChromo: Randomly generates initial chromosomes.
  • printChromo: Prints current population and fitness.
  • Tournament: Selects parents using tournament selection.
  • Crossover: Performs one-point crossover on selected parents.
  • Mutation: Mutates offspring by flipping bits.
  • Replace: Replaces the least fit individuals with new offspring.

Output

The program prints the population, selected parents, crossover results, mutation results, and replacement actions for each generation.

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Note:
This is a simplified GA for educational purposes. For real-world optimization, consider more advanced selection, crossover, and mutation strategies.