Micro Genetic Algorithm

Micro Genetic Algorithm #

Name #

Micro Genetic Algorithm (MGA), Micro-GA, μGA

Taxonomy #

The Micro Genetic Algorithm is a compact variant of the Genetic Algorithm, a population-based metaheuristic optimization technique inspired by the principles of natural selection and genetics. It belongs to the broader fields of Evolutionary Computation and Computational Intelligence.

  • Artificial Intelligence
    • Computational Intelligence
      • Biologically Inspired Computation
        • Evolutionary Computation
          • Evolutionary Algorithms
            • Genetic Algorithms
              • Micro Genetic Algorithm

Strategy #

The Micro Genetic Algorithm is designed to efficiently solve optimization problems using a small population size, typically between 5 and 50 individuals. This compact population allows for faster convergence and reduced computational complexity compared to traditional Genetic Algorithms.

The MGA follows an iterative process where each iteration represents a generation. In each generation, the population undergoes selection, crossover, and mutation operations to create new offspring. The selection process favors individuals with higher fitness values, giving them a better chance of being chosen as parents for reproduction.

Crossover is performed on the selected parents to generate new offspring by exchanging genetic information between them. This operation aims to combine the desirable characteristics of the parents to produce potentially better solutions. Mutation is then applied to the offspring with a certain probability, introducing random changes to maintain diversity and explore new regions of the search space.

After the offspring are generated, they replace the least fit individuals in the population, ensuring that the population size remains constant throughout the optimization process. This elitist replacement strategy preserves the best solutions found so far and prevents the loss of valuable genetic material.

The MGA continues this iterative process until a termination criterion is met, such as reaching a maximum number of generations or finding a satisfactory solution. Due to its small population size, the MGA may converge quickly but also has a higher risk of premature convergence to suboptimal solutions. To mitigate this, techniques like restarting the population with new random individuals or employing diversity preservation mechanisms can be incorporated.

Procedure #

Data Structures:

  • Population: An array of individuals representing potential solutions to the optimization problem.
  • Individual: Represents a single solution, typically encoded as a string of bits, integers, or real numbers.
  • Fitness: A numeric value assigned to each individual, indicating the quality of the solution it represents.


  • Population Size: The number of individuals in the population, typically small (5-50).
  • Crossover Probability: The probability of applying the crossover operation to a pair of selected parents.
  • Mutation Probability: The probability of applying the mutation operation to an offspring.
  • Max Generations: The maximum number of generations allowed before terminating the algorithm.


  1. Initialize the population with a small number of randomly generated individuals.
  2. Evaluate the fitness of each individual in the population.
  3. While the termination criterion is not met, repeat steps 4-9.
  4. Select parents from the population using a selection method (e.g., tournament selection).
  5. With a probability equal to the crossover probability, perform crossover on the selected parents to create offspring.
  6. With a probability equal to the mutation probability, apply mutation to the offspring.
  7. Evaluate the fitness of the offspring.
  8. Replace the least fit individuals in the population with the offspring.
  9. If the termination criterion is met, return the best solution found. Otherwise, go to step 4.

Considerations #


  • Fast convergence due to the small population size, making it suitable for problems with limited computational resources or time constraints.
  • Reduced memory requirements compared to traditional Genetic Algorithms, as it maintains a compact population.
  • Potential for finding good solutions quickly, especially in problems with a small search space or when near-optimal solutions are sufficient.


  • Higher risk of premature convergence to suboptimal solutions due to the small population size and lack of diversity.
  • May struggle to escape local optima, as the limited population size reduces exploration capabilities.
  • Not suitable for highly complex or large-scale optimization problems that require extensive exploration of the search space.

Heuristics #

Population Size:

  • Start with a small population size, typically between 5 and 50 individuals.
  • Increase the population size if the problem is complex or requires more diversity.
  • Consider the trade-off between convergence speed and the risk of premature convergence when choosing the population size.

Crossover and Mutation Probabilities:

  • Set the crossover probability high (e.g., 0.8-0.9) to encourage the exchange of genetic material between parents.
  • Keep the mutation probability low (e.g., 0.01-0.1) to introduce small variations without disrupting good solutions.
  • Adjust the mutation probability based on the problem’s characteristics and the desired balance between exploration and exploitation.

Termination Criterion:

  • Set a maximum number of generations as a termination criterion to prevent indefinite running of the algorithm.
  • Consider additional termination criteria, such as reaching a target fitness value or observing no improvement over a certain number of generations.

Restarts and Diversity Preservation:

  • Implement a restart mechanism to reinitialize the population with new random individuals if the algorithm appears to be stuck in a suboptimal region.
  • Employ diversity preservation techniques, such as niching or crowding, to maintain a diverse population and prevent premature convergence.

Problem Encoding:

  • Choose an appropriate encoding scheme (e.g., binary, integer, real) based on the problem’s characteristics and constraints.
  • Ensure that the encoding allows for effective crossover and mutation operations that produce valid solutions.

Fitness Evaluation:

  • Define a suitable fitness function that accurately represents the quality or performance of individuals in the context of the optimization problem.
  • Consider normalizing or scaling the fitness values if necessary to avoid dominance by extreme values.

Selection Method:

  • Use a selection method that balances the exploration of promising regions and the exploitation of good solutions.
  • Tournament selection is a common choice, as it allows for adjustable selection pressure by varying the tournament size.

Parameter Tuning:

  • Experiment with different parameter settings (e.g., population size, crossover and mutation probabilities) to find the most effective configuration for the specific problem.
  • Utilize parameter tuning techniques, such as grid search or adaptive parameter control, to automatically adjust the parameters during the optimization process.