Super simple genetic algorithm library for Python.

# primordial-ooze

Dirt simple genetic algorithm library for Python

Docs are below.

## Simulation

A GA simulation. The general workflow for this is:

import primordialooze as po
import pandas
import matplotlib.pyplot

sim = po.Simulation(nagents, shape, fitnessfunction)
bestagent, fitness = sim.run()
print("The agent {} has the best fitness, which is: {}".format(bestagent, fitness))

# Dump and plot
fname = "stats.csv"
sim.dump_history_csv(fname)

df = df.drop(['GenerationIndex'], axis=1)
df.plot()
plt.show()


## Statistics

The Simulation class stores a list statistics, which contains instances of the class Statistics. The Statistics class contains the following properties:

• generationidx, which is the zero-based index indicating which generation this Statistics instance is for.
• maxval, which is the maximum fitness score for this generation.
• minval, which is the minimum fitness score for this generation.
• avgval, which is the average fitness score for this generation.

The Simulation class also stores a list best_agents, which contains the best agent at each generation.

### init

def __init__(self, population, shape, fitnessfunc, *, seedfunc=None, selectionfunc=None,
crossoverfunc=None, mutationfunc=None, elitismfunc=None, nworkers=0,
max_agents_per_generation=None, min_agents_per_generation=None)


#### Args

The following list contains the arguments that are needed. These do not have default values since the values for these will change dramatically depending on the problem.

• population: The number of agents in the first generation. We will generate this many agents in the initial generation, each of which is a Numpy Array of shape=shape. They will be mutated according to mutationfunc, and evaluated each generation by fitnessfunc.
• shape: The shape of each agent in the population. Must be a list-like. The shape of the agents must be a 1D array of whatever length like (7,).
• fitnessfunc: The function to use to evaluate the fitness of each agent in the generation. Must have signature: fitnessfunc(agent) -> scalar float. This function will be evaluated on every single agent in the gene pool at each generation. If this function is slow, it probably makes sense to use multiprocessing, unless the gene pool is quite small. See nworkers.

#### Keyword Args

These arguments contain (mostly) sensible defaults, but you should definitely make sure these defaults work for you. You will almost certainly want to change some of these to fit your problem.

• seedfunc: The function to use to create the first generation of agents. The function must have the signature seedfunc() -> agent of shape 'shape'. We call this function population times. When None, defaults to uniform random over the range [-1.0, 1.0) in each dimension.
• selectionfunc: The function to use to select the agents that are allowed to breed to create the next generation. Signature must be selectionfunc(population, evaluations) -> selected_agents, where population is an n-dimensional array of shape (nagents, agent_length), evaluations is an array of shape (nagents,); evaluations[i] contains the fitness value for population[i, :]; selected_agents is an n-dimensional array of shape (nagents_selected, agent_length), which must contain the selected agents. population and evaluations are pre-sorted so that population[0, :], corresponds to evalutaion[0] and has the highest evaluation value. Agents which are not selected are simply discarded, i.e., they will not appear in the next generation (unless randomly created again as part of crossover/mutation). If None, defaults to selecting the top ten percent.
• crossoverfunc: Crossover function to use. Must have signature crossoverfunc(agents) -> new_agents, where agents is an n-dimensional array of shape (nselected_agents, agent_length), and where new_agents must be an n-dimensional array of shape (nagents, agent_length). This function is applied after the selection function is used to determine which agents will enter the new generation and this function is used exclusively on those selected agents. Typically, new_agents will constitute the entirety of the new generation, with one exception being if elitism is used (see below) and another exception being if the mutation function adds new individuals to the gene pool, rather than just mutating existing ones. If None, defaults to 2-point crossover used on randomly selected pairs from the breeding agents until population agents (or, if elitismfunc is None, 0.9 * population).
• mutationfunc: The function to use to apply mutations to the gene pool. The signature must be mutationfunc(agents) -> new_agents, where agents is the value returned from crossoverfunc and new_agents must be an n-dimensional array of shape (nagents, agent_length). This function is applied to the result of crossoverfunc. When None, defaults to setting each value in 0.05 of the agents to a random value, where the random value is drawn from a Gaussian distribution of mean = the value being replaced and stdev = 0.25.
• elitismfunc: A function of signature elitismfunc(generation_index) -> float in range [0.0, 1.0]. This function takes the index of the generation (0 for the first generation, 1 for the second, etc.) and returns the fraction of top-performers to hold over as-is to the next generation. The elites are duplicated and then, after the new generation is created via the selectionfunc -> crossoverfunc -> mutationfunc pipeline, they are reintroduced into the gene pool. This means that if the above pipeline generates 100 agents and the elitism is set to take 10, the new generation will be composed of 110 agents. If this is confusing, see max_agents_per_generation and min_agents_per_generation. When None, defaults to a function that simply returns 0.1 (or 10%) of the gene pool regardless of the generation.
• nworkers: The number of processes to use to parallelize the fitness function. This will default to 0, which will mean no parallelism at all. None will use the number of cores. Otherwise, should be a positive integer.
• max_agents_per_generation: The maximum agents to allow into a generation. If the selection, crossover, mutation, and elitism functions are not handled properly, it is possible for the number of agents to change per generation. While this may be desired in some circumstances, it is often not. If this value is negative, we will allow the generations to grow to arbitrary size. If it is nonzero, after selection, crossover, mutation, and elitism, we will take all of the candidates as long as they do not number more than this value. If they do, we take this many at random. This value defaults to None, which means we use population as the max.
• min_agents_per_generation: The minimum agents to allow making a new generation. If the selection, crossover, mutation, and elitism functions are not handled properly, it is possible for the number of agents to change per generation. While this may be desired in some circumstances, it is often not. If this value is negative or zero, we will allow the generations to shrink to zero, after which the simulation will stop. If it is nonzero, after selection, crossover, mutation, and elitism, we will cycle through the candidate agents in random order, duplicating them until this value is met. Note that we attempt to spread out the duplication evenly amongst all candidates. This value defaults to None, which means we use population as the min.

### run

    def run(self, niterations=100, fitness=None)


Runs the constructed simulation.

Either runs until niterations have passed, or runs until the best fitness is fitness or greater. Returns the best agent along with its fitness.

#### Keyword Args

• niterations: The number of iterations to run the simulation to. Defaults to 100. If None, fitness will be used (and must not be None). If both this and fitness is specified, we will stop as soon as one or the other condition is met.
• fitness: The fitness level to converge on. As soon as one or more agents have this fitness level or higher, the simulation will stop. Defaults to None. If None (the default), niterations will be used (and must not be None). If this and niterations is specified, we will stop as soon as one or the other condition is met.
• printprogress: If True (the default), we will print a progress indication after each generation.

#### Returns

• The agent with the highest fitness score after the simulation ends.
• The fitness of this agent.

## Project details

Uploaded Python 3