Memetic Algorithm

Memetic Algorithm #

Name #

Memetic Algorithm, MA, Hybrid Genetic Algorithm, Baldwinian Evolutionary Algorithm, Lamarckian Evolutionary Algorithm, Cultural Algorithm

Taxonomy #

Memetic Algorithms are a class of metaheuristics that combine evolutionary algorithms, specifically genetic algorithms, with local search techniques to solve optimization problems.

  • Computational Intelligence
    • Biologically Inspired Computation
      • Evolutionary Computation
        • Evolutionary Algorithms
          • Genetic Algorithms
            • Memetic Algorithms

Strategy #

Memetic Algorithms extend the traditional Genetic Algorithm (GA) by incorporating local search techniques to improve the quality of individual solutions within the population. The core idea behind MAs is to leverage the global exploration capabilities of GAs while enhancing the local exploitation through the use of problem-specific knowledge in the form of local search heuristics.

The algorithm begins by initializing a population of candidate solutions, typically using random generation or heuristic-based methods. Each individual in the population represents a potential solution to the optimization problem at hand. The fitness of each individual is evaluated based on the objective function or a set of predefined criteria.

After initialization, the MA enters the main evolutionary loop. In each iteration, known as a generation, the algorithm applies genetic operators such as selection, crossover, and mutation to create a new offspring population. The selection operator chooses parent individuals based on their fitness, favoring the survival and reproduction of high-quality solutions. Crossover combines genetic material from selected parents to generate offspring, while mutation introduces random variations to maintain diversity and explore new regions of the search space.

The key distinguishing feature of MAs is the incorporation of local search techniques. After the creation of the offspring population, each individual undergoes a local search process to improve its fitness. The local search can be performed using various optimization techniques such as hill climbing, simulated annealing, or problem-specific heuristics. The goal is to exploit the local neighborhood of each solution and find better solutions within that vicinity.

The locally improved individuals are then integrated back into the population, either replacing their original counterparts or competing with them for survival in the next generation. This process of genetic operators followed by local search is repeated for a predefined number of generations or until a termination criterion is met, such as reaching a satisfactory solution quality or exhausting a computational budget.

Throughout the evolutionary process, MAs maintain a balance between exploration and exploitation. The global search performed by the GA allows for the exploration of diverse regions of the search space, while the local search focuses on refining promising solutions and exploiting their local neighborhoods. This synergy between global and local search enables MAs to efficiently navigate complex fitness landscapes and find high-quality solutions.

Procedure #

Data Structures:

  • Individual: Represents a candidate solution to the optimization problem.
  • Population: A collection of individuals.
  • Fitness Function: Evaluates the quality or fitness of an individual solution.


  • Population Size: The number of individuals in the population.
  • Crossover Rate: The probability of applying the crossover operator to selected parents.
  • Mutation Rate: The probability of applying the mutation operator to an individual.
  • Local Search Intensity: The extent or duration of the local search process for each individual.
  • Termination Criteria: Conditions for stopping the algorithm, such as a maximum number of generations or a fitness threshold.


  1. Initialize the population with random or heuristically generated individuals.
  2. Evaluate the fitness of each individual in the population.
  3. While termination criteria are not met, repeat steps 4-9.
  4. Select parents from the population based on their fitness.
  5. Apply the crossover operator to the selected parents to create offspring.
  6. Apply the mutation operator to the offspring with a certain probability.
  7. For each offspring individual, perform local search: 7.1. Apply a local search technique (e.g., hill climbing, simulated annealing) to improve the individual’s fitness. 7.2. Update the individual with the locally improved solution.
  8. Evaluate the fitness of the offspring individuals.
  9. Replace individuals in the population with the offspring based on a replacement strategy (e.g., elitism, tournament selection).
  10. Return the best solution found.

Considerations #


  • Combines the global exploration of genetic algorithms with the local exploitation of problem-specific knowledge, leading to improved solution quality and faster convergence.
  • Adaptable to a wide range of optimization problems by incorporating suitable local search techniques and problem-specific heuristics.
  • Maintains population diversity while focusing on promising regions of the search space, reducing the risk of premature convergence.


  • Increased computational complexity compared to traditional genetic algorithms due to the additional local search phase.
  • Requires careful design and selection of local search techniques and their integration with the evolutionary process.
  • The effectiveness of the algorithm heavily depends on the quality and suitability of the chosen local search heuristics for the specific problem domain.

Heuristics #

Population Size:

  • Determine the population size based on the complexity of the problem and available computational resources.
  • Larger populations can provide better exploration but may require more computational effort.
  • Smaller populations may converge faster but risk premature convergence to suboptimal solutions.

Crossover and Mutation Rates:

  • Set the crossover rate high enough to promote the exchange of genetic material between individuals, typically in the range of 0.6 to 0.9.
  • Keep the mutation rate relatively low, usually between 0.01 and 0.1, to introduce minor variations without disrupting the overall solution structure.
  • Adjust the rates based on the problem characteristics and desired balance between exploration and exploitation.

Local Search Intensity:

  • Determine the appropriate intensity or duration of the local search phase for each individual.
  • Intensive local search can improve solution quality but may increase computational overhead.
  • Balancing the local search intensity with the global search is crucial to maintain a proper exploration-exploitation trade-off.
  • Consider adapting the local search intensity dynamically based on the progress of the algorithm or the characteristics of individual solutions.

Selection and Replacement Strategies:

  • Employ selection mechanisms that favor high-quality individuals while maintaining population diversity, such as tournament selection or rank-based selection.
  • Use elitism to preserve the best individuals across generations, ensuring that the algorithm does not lose the most promising solutions.
  • Consider replacing a portion of the population with offspring based on their fitness to promote competition and maintain selection pressure.

Problem-Specific Heuristics:

  • Incorporate problem-specific knowledge and heuristics into the local search phase to guide the search towards promising regions of the solution space.
  • Design local search techniques that exploit the structure and characteristics of the problem domain, such as neighborhood operators or domain-specific optimization methods.
  • Adapt the local search heuristics based on the feedback and progress of the algorithm, allowing for dynamic adjustment of the search strategy.

Termination Criteria:

  • Define clear termination criteria based on the problem requirements and available resources.
  • Common criteria include reaching a maximum number of generations, achieving a satisfactory solution quality, or observing convergence of the population.
  • Monitor the progress of the algorithm and consider early stopping mechanisms if the improvement in solution quality stagnates.