Genetic Programming (GP) is a subset of evolutionary algorithms inspired by Darwin’s theory of natural selection. In GP, programs are evolved over generations to solve specific problems.
- Key Inspiration: Evolution through natural selection.
- Representation: Programs are typically represented as tree structures:
- Nodes: Operations or functions.
- Leaves: Variables or constants.
- Initialization: Generate a random population of programs.
- Fitness Evaluation: Evaluate how well each program performs the task.
- Selection: Choose the best-performing programs for reproduction.
- Genetic Operators:
- Crossover: Combine parts of two programs to create new ones.
- Mutation: Introduce random changes to a program.
- Replacement: Replace less fit programs with new ones.
- Termination: Stop the process when a solution is found or after a set number of generations.
- Fitness Function: Determines how well a program solves the problem.
- Genetic Representation: Programs are often expressed as syntax trees.
- Diversity: Maintaining a variety of programs prevents premature convergence.
Genetic Programming is used in various fields, including:
- Symbolic Regression: Deriving mathematical expressions that fit data.
- Game AI: Creating programs to play games automatically.
- Optimization: Solving optimization problems in complex domains.
- Feature Selection: Improving machine learning models by selecting optimal features.