Grammar Guided Genetic Programming

Grammar-Guided Genetic Programming #

Name #

Grammar-Guided Genetic Programming (GGGP), Grammar-based Genetic Programming, Grammar-Guided GP, G3P

Taxonomy #

Grammar-Guided Genetic Programming is a specialized form of Genetic Programming (GP) that incorporates a grammar to constrain and guide the evolutionary process. It is closely related to other grammar-based evolutionary algorithms such as Grammatical Evolution (GE) and is an extension of traditional GP.

  • Artificial Intelligence
    • Computational Intelligence
      • Evolutionary Computation
        • Evolutionary Algorithms
          • Genetic Programming (GP)
            • Grammar-Guided Genetic Programming (GGGP)

Strategy #

Grammar-Guided Genetic Programming leverages the power of evolutionary computation and the structure provided by a grammar to evolve solutions to complex problems. The key idea behind GGGP is to use a grammar to specify the search space of possible solutions, ensuring that the evolved programs are syntactically correct and adhere to the desired structure.

Representation #

In GGGP, individuals in the population are represented as derivation trees or abstract syntax trees (ASTs) that are derived from the provided grammar. The grammar defines the valid building blocks and the rules for combining them to create valid programs. This grammar-based representation allows for a more focused search within the constrained solution space.

Initialization #

The initial population in GGGP is generated by randomly selecting production rules from the grammar and applying them to create valid derivation trees. The initialization process ensures that all individuals in the population adhere to the grammatical constraints imposed by the provided grammar.

Genetic Operators #

GGGP employs specialized genetic operators that operate on the derivation trees while respecting the grammatical structure:

  • Crossover: The crossover operator selects two parent individuals and swaps compatible subtrees between them to create offspring. The compatibility of subtrees is determined by the grammar, ensuring that the resulting offspring are still valid according to the grammatical rules.

  • Mutation: The mutation operator introduces random changes to an individual’s derivation tree. It selects a node in the tree and replaces it with another compatible subtree derived from the grammar. The mutation operator helps maintain diversity in the population and allows for the exploration of new solutions.

Fitness Evaluation #

The fitness of each individual in the population is evaluated based on its performance on the given problem. The fitness function assesses the quality of the evolved programs by executing them and measuring their ability to solve the problem at hand. The specific fitness evaluation depends on the nature of the problem and can involve metrics such as accuracy, efficiency, or any other relevant criteria.

Selection and Replacement #

GGGP uses selection mechanisms to choose individuals for reproduction based on their fitness. Common selection methods include tournament selection, roulette wheel selection, or rank-based selection. The selected individuals undergo genetic operations to create offspring, which are then evaluated for their fitness. The offspring may replace less fit individuals in the population, allowing the population to evolve and improve over generations.

Procedure #

Data Structures:

  • Grammar: A formal grammar specifying the valid structure and building blocks of the programs.
  • Derivation Tree: A tree-based representation of an individual program derived from the grammar.
  • Population: A collection of derivation trees representing the current set of candidate solutions.


  • Population Size: The number of individuals in the population.
  • Max Generations: The maximum number of generations the algorithm will run for.
  • Crossover Rate: The probability of applying the crossover operator to selected individuals.
  • Mutation Rate: The probability of applying the mutation operator to an individual.
  • Selection Method: The method used for selecting individuals for reproduction (e.g., tournament selection, roulette wheel selection).
  • Replacement Strategy: The strategy for replacing individuals in the population with offspring (e.g., generational replacement, steady-state replacement).

Algorithm Steps:

  1. Initialize the population:
    1. Create an empty population.
    2. Repeat until the population size is reached:
      1. Start with the root symbol of the grammar.
      2. Recursively select and apply production rules to generate a valid derivation tree.
      3. Add the generated derivation tree to the population.
  2. Evaluate the fitness of each individual in the population.
  3. Repeat until the maximum number of generations is reached or a satisfactory solution is found:
    1. Select individuals from the population based on the selection method.
    2. Apply genetic operators to the selected individuals:
      1. Crossover:
        1. Select two parent individuals.
        2. Choose compatible crossover points in their derivation trees.
        3. Swap the subtrees at the selected crossover points to create two offspring.
      2. Mutation:
        1. Select an individual.
        2. Choose a mutation point in its derivation tree.
        3. Replace the subtree at the mutation point with a new compatible subtree derived from the grammar.
    3. Evaluate the fitness of the offspring.
    4. Replace individuals in the population with the offspring based on the replacement strategy.
  4. Return the best individual found throughout the evolution process.

Considerations #


  • Ensures syntactic validity of evolved programs by adhering to the provided grammar.
  • Allows for a more focused search within the constrained solution space defined by the grammar.
  • Enables the incorporation of domain knowledge through the design of the grammar.


  • Requires the definition of a suitable grammar, which can be challenging for complex problem domains.
  • The choice of grammar can significantly impact the performance and expressiveness of the evolved programs.
  • The evolutionary process may be computationally expensive, especially for large grammars and complex problems.

Heuristics #

Grammar Design #

  • Define a grammar that captures the essential components and structure of the problem domain.
  • Strike a balance between expressiveness and complexity in the grammar design. A more expressive grammar allows for a wider range of solutions but may increase the search space.
  • Consider incorporating domain-specific knowledge and constraints into the grammar to guide the search towards promising solutions.

Population Initialization #

  • Ensure diversity in the initial population by using randomized derivation tree generation.
  • Consider incorporating heuristics or domain-specific rules to generate more meaningful initial individuals.

Genetic Operator Configuration #

  • Adjust the crossover and mutation rates based on the problem complexity and desired exploration-exploitation balance. Higher crossover rates promote exploitation of good solutions, while higher mutation rates encourage exploration of new solutions.
  • Experiment with different crossover and mutation operators tailored to the specific problem domain and grammar structure.

Fitness Evaluation #

  • Design a fitness function that accurately captures the desired properties and objectives of the problem.
  • Consider incorporating multiple objectives or constraints into the fitness evaluation, if applicable.
  • Optimize the fitness evaluation process to minimize computational overhead, especially for computationally expensive problems.

Selection and Replacement Strategies #

  • Choose a selection method that balances the selection pressure and maintains diversity in the population. Tournament selection and rank-based selection are commonly used.
  • Experiment with different replacement strategies, such as generational replacement (replacing the entire population with offspring) or steady-state replacement (replacing a subset of individuals), to find the most suitable approach for the problem at hand.

Termination Criteria #

  • Define appropriate termination criteria based on the problem requirements, such as reaching a maximum number of generations, finding a solution of satisfactory quality, or observing convergence in the population.
  • Consider implementing early stopping mechanisms to avoid unnecessary computation if the algorithm converges to a satisfactory solution early.