This repository contains code for both deterministic EAs where only one crossover operator can be used throughout the whole process, and a probabilistic EA where multiple crossover operators can be adopted with specific probability proportions.

A meta-EA is also implemented to find the best probability proportion scheme of multiple crossovers operators.

1. Master branch is used for running evaluations with the deterministic benchmark EAs.
2. PythonCommandLine branch is used for the grid search trivial tuning and data analysis.
3. meta_opt branch is used for the development of the meta-EA (CMA-ES) in Python.

1. Under the main folder, use the Makefile (command "make") to compile.

2. Run with java -jar testrun.jar -submission=player19 -evaluation=KatsuuraEvaluation -seed=1

   or java -jar testrun.jar -submission=player19 -evaluation=SchaffersEvaluation -seed=1

   or java -jar testrun.jar -submission=player19 -evaluation=BentCigarFunction -seed=1