Genetic algorithms and genetic programming are both evolutionary computation techniques used in AI. However, they differ in their approach and application.
A genetic algorithm (GA) is an optimization technique inspired by the process of natural selection. It is used to search for the optimal solution to a given problem. GAs operate on a population of potential solutions encoded as chromosomes, which are typically represented as binary strings. These chromosomes are subjected to genetic operators such as crossover and mutation to generate new candidate solutions that are evaluated and selected based on their fitness.
In contrast, genetic programming (GP) is a machine learning technique that evolves computer programs to solve problems. GP uses a tree structure to represent a program, and evolves this tree using the same genetic operators as GAs. The fitness function in GP evaluates the program's output rather than a binary string. GP can be used for symbolic regression, classification, and control problems.
The main difference between the two approaches is that GAs are used to optimize a set of parameters to minimize or maximize an objective function, while GP is used to evolve a program that can solve a specific problem. Additionally, GP can handle problems with variable-length solutions, while GAs require a fixed-length chromosome.