What are the differences between genetic algorithms and genetic programming?

Question

I would like to have a simple explanation of the differences between genetic algorithms and genetic programming (without too much programming jargon). Examples would also be appreciated.

Apparently, in genetic programming, solutions are computer programs. On the other hand, genetic algorithms represent a solution as a string of numbers. Any other differences?

Answer 1

Genetic algorithms (GA) are search algorithms that mimic the process of natural evolution, where each individual is a candidate solution: individuals are generally "raw data" (in whatever encoding format has been defined).

Genetic programming (GP) is considered a special case of GA, where each individual is a computer program (not just "raw data"). GP explore the algorithmic search space and evolve computer programs to perform a defined task.

Answer 2

Genetic programming and genetic algorithms are very similar. They are both used to evolve the answer to a problem, by comparing the fitness of each candidate in a population of potential candidates over many generations.

Each generation, new candidates are found by randomly changing (mutation) or swapping parts (crossover) of other candidates. The least 'fit' candidates are removed from the population.

Structural differences

The main difference between them is the representation of the algorithm/program.

A genetic algorithm is represented as a list of actions and values, often a string. for example:

1+x*3-5*6 

A parser has to be written for this encoding, to understand how to turn this into a function. The resulting function might look like this:

function(x) { return 1 * x * 3 - 5 * 6; } 

The parser also needs to know how to deal with invalid states, because mutation and crossover operations don't care about the semantics of the algorithm, for example the following string could be produced: 1+/3-2*. An approach needs to be decided to deal with these invalid states.

A genetic program is represented as a tree structure of actions and values, usually a nested data structure. Here's the same example, illustrated as a tree:

      -    /     \   *       *  / \     / \ 1   *   5   6    / \   x   3 

A parser also has to be written for this encoding, but genetic programming does not (usually) produce invalid states because mutation and crossover operations work within the structure of the tree.

Practical differences

Genetic algorithms

• Inherently have a fixed length, meaning the resulting function has bounded complexity
• Often produces invalid states, so these need to be handled non-destructively
• Often rely on operator precedence (e.g. in our example multiplication happens before subtraction) which could be seen as a limitation

Genetic programs

• Inherently have a variable length, meaning they are more flexible, but often grow in complexity
• Rarely produces invalid states, these can usually be discarded
• Use an explicit structure to avoid operator precedence entirely