COPE is an evolutionary algorithm much like a genetic algorithm. But unlike a classic genetic algorhtim, COPE does not generate a new generation from the last. It continuously generates new indivduals from the Population and inserts them back into the Population. This allows the genetic operators and evaluators to be applied to new individuals completely in parallel, and thus we can speed up the algorithm by adding more worker process to the pool.

The Population is the data structure that holds all the individuals of our population. It is implemented using a sortedList for fast selection and insertion.

The Manager is a separate process with selects two individuals and places them in the delivery queue. It will then check if the nursery queue has individuals waiting to be inserted into the population.

Workers in the worker pool do all their work in parallel and so we can spin up many workers to increase the speed of the algorithm. They take a pair of individuals from the devlivery queue, apply genetic operators like crossover and mutation, to produce a new individual. This new individual has it's fitness evaluated so the manager can insert it back into the population in the correct position.

create a virtualenv using your preferred method

virtualenv -p python3 env
source env/bin/activate

install requirements

pip install -r requirements-dev.txt

run the unit tests

./scripts/unit-test.sh

There are a couple example files for doing simple list genetic algorithms or gp etc.

python list_ga.py
python tree_ga.py
python gp.py