Step 1: Download OakGP

You can include the latest release version of OakGP as a dependency of your Java project by adding the following to its Maven pom.xml file:

To use the latest development version of OakGP specify the following instead:

Step 2: Create New RunBuilder

Step 3: Set Return Type

Specify the required return type of the programs automatically generated by the process.

Step 4: Set Random Number Generator (Optional)

Genetic programming uses random variables. You can optionally specify the random number generator to use. Note: If you do not specify a random number generator then a default one (which uses java.util.Random) will be used. It is recommended that you use the default random generator rather than setting one explicitly.

Step 5: Set Primitive Set

Specify the constants, variables and functions that are available to the process to use in the construction of the programs it automatically generates.

Note: As an alternative to using setConstants/setVariables/setFunctions you can instead use setPrimitiveSet to specify the primitive set.

Step 6: Set Fitness Function

Specify the fitness function to use to determine the suitability of automatically generated programs for solving the problem being investigated.

Step 7: Set Initial Population

Set the initial population that will be used as a basis for evolving future generations. This can be done by specifying the the population size and max tree depth of the initial population which will then be randomly generated using the previously configured constant, variable and function sets.

Note: As an alternative to using setInitialPopulationSize/setTreeDepth you can instead use setInitialPopulation to explicitly specify the contents of the initial population.

Step 8: Set Evolution Strategy (Optional)

You can optionally specify how generations evolve - e.g. selection strategies, genetic operators and elitism. Note: If you do not specify how generations evolve then a default strategy will be used. The default strategy is sufficient for allowing people to quickly get started with OakGP.

Step 9: Set Termination Criteria

Set the termination condition(s) for the process. The following options are available to set the termination criteria:

• setTargetFitness(double) - will stop the process once a solution has been generated that has the specified fitness value.
• setMaxGenerations(int) - will stop the process once the specified number of generations have been processed.
• setMaxGenerationsWithoutImprovement(int) - will stop the process once the specified number of consecutive generations have been processed without an improvement in the fitness of the solutions being generated.
• setTerminator(final Predicate<List<RankedCandidate>>) - allows a user-defined termination criteria to be specified.
• ...or any combination of the above.

Step 10: Start Run

Examples