Steady State Genetic Algorithm

Steady-State Genetic Algorithm #

Name #

Steady-State Genetic Algorithm (SSGA)

Taxonomy #

The Steady-State Genetic Algorithm is a variation of the standard Genetic Algorithm, which belongs to the field of Evolutionary Computation, a subfield of Computational Intelligence.

  • Computational Intelligence
    • Evolutionary Computation
      • Evolutionary Algorithms
        • Genetic Algorithms
          • Steady-State Genetic Algorithm

Strategy #

The Steady-State Genetic Algorithm differs from the standard Genetic Algorithm in its population management strategy. Instead of generating an entirely new population at each iteration, the SSGA selects a small number of individuals (usually one or two) for replacement in each generation.

Selection and Replacement #

The SSGA typically uses a tournament selection method to choose parents for reproduction. After generating offspring through crossover and mutation, the algorithm selects a small number of individuals from the current population to be replaced by the new offspring. The replacement strategy can be based on fitness, age, or other criteria.

Preservation of Genetic Diversity #

By replacing only a few individuals in each generation, the SSGA maintains a more stable population compared to the standard GA, which replaces the entire population at once. This approach helps preserve genetic diversity and can prevent premature convergence to suboptimal solutions.

Steady-State Evolution #

The SSGA’s incremental replacement strategy mimics the concept of steady-state evolution in nature, where populations evolve gradually over time. This approach allows for a more continuous optimization process, as the population is constantly being updated with new offspring.

Procedure #

Data Structures:

  • Population: A list of individuals representing potential solutions to the problem.
  • Individual: A single solution encoded as a string of bits, integers, or other representations.


  • Population Size: The number of individuals in the population.
  • Replacement Rate: The number of individuals to be replaced in each generation.
  • Crossover Rate: The probability of applying crossover to selected parents.
  • Mutation Rate: The probability of applying mutation to offspring.
  1. Initialize the population with randomly generated individuals.
  2. Evaluate the fitness of each individual in the population.
  3. Repeat until a termination criterion is met (e.g., maximum generations or sufficient fitness):
    1. Select parents for reproduction using a tournament selection method.
    2. Apply crossover to the selected parents with a probability equal to the crossover rate to generate offspring.
    3. Apply mutation to the offspring with a probability equal to the mutation rate.
    4. Evaluate the fitness of the new offspring.
    5. Select individuals from the current population for replacement based on a replacement strategy (e.g., worst fitness or oldest age).
    6. Replace the selected individuals with the new offspring.
  4. Return the best individual found during the optimization process.

Considerations #


  • Maintains genetic diversity, reducing the risk of premature convergence.
  • Allows for a more continuous optimization process, as the population is constantly being updated.
  • Can be more efficient than the standard GA in terms of the number of evaluations required to reach a satisfactory solution.


  • The incremental replacement strategy may result in slower convergence compared to the standard GA.
  • The performance of the algorithm can be sensitive to the choice of replacement strategy and rate.
  • May require more fine-tuning of parameters compared to the standard GA to achieve optimal performance.

Heuristics #

Population Size #

  • Choose a population size that balances diversity and computational efficiency.
  • Larger populations can explore more of the search space but may require more evaluations.
  • Smaller populations may converge faster but risk getting stuck in suboptimal solutions.

Replacement Rate #

  • A low replacement rate (e.g., 1 or 2 individuals per generation) helps maintain population stability and diversity.
  • Higher replacement rates can lead to faster convergence but may reduce diversity and increase the risk of premature convergence.

Selection Method #

  • Tournament selection is commonly used in the SSGA due to its simplicity and effectiveness.
  • The tournament size should be chosen to balance selection pressure and diversity preservation.

Crossover and Mutation Rates #

  • Crossover rates are typically high (e.g., 0.8 to 0.95) to promote the exchange of genetic material between parents.
  • Mutation rates are usually low (e.g., 0.01 to 0.1) to introduce small variations without drastically disrupting promising solutions.
  • Experiment with different rates to find the optimal balance for the specific problem and representation.

Replacement Strategy #

  • Fitness-based replacement, where the least fit individuals are replaced, can drive the population towards better solutions.
  • Age-based replacement, where the oldest individuals are replaced, can help maintain diversity by removing stagnant solutions.
  • A combination of fitness and age-based replacement can provide a balance between exploitation and exploration.