GENETIC ALGORITHM

Overview

Genetic Algorithms are evolutionary optimization techniques inspired by natural selection. They evolve candidate solutions through selection, crossover, and mutation operations to find optimal or near-optimal solutions to complex problems.

Evolution Process

  • 1.Initialize population
  • 2.Evaluate fitness
  • 3.Selection for reproduction
  • 4.Crossover & mutation
  • 5.Replace old generation

GA Pseudocode

population = initialize_population()

while not termination_condition():
  fitness = evaluate(population)
  
  parents = selection(population, fitness)
  offspring = crossover(parents)
  offspring = mutation(offspring)
  
  population = replacement(
    population, offspring
  )

return best_individual(population)

Applications

Optimization Problems
Neural Network Training
Scheduling & Routing
Feature Selection