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A python metaheuristic optimization library. Currently supports Genetic Algorithms, Gravitational Search, Cross Entropy, and PBIL.

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# Optimal (beta)
A python metaheuristic optimization library. Built for easy extension and usage.

Warning: Optimal is in beta. API may change. I will do my best to note any breaking changes in this readme, but no guarantee is given.

Supported metaheuristics:

* Genetic algorithms (GA)
* Gravitational search algorithm (GSA)
* Cross entropy (CE)
* Population-based incremental learning (PBIL)

# Installation
    pip install optimal

# Usage
```python
import math

from optimal import GenAlg
from optimal import Problem
from optimal import helpers

# The genetic algorithm uses binary solutions.
# A decode function is useful for converting the binary solution to real numbers
def decode_ackley(binary):
	# Helpful functions from helpers are used to convert binary to float
	# x1 and x2 range from -5.0 to 5.0
	x1 = helpers.binary_to_float(binary[0:16], -5.0, 5.0)
	x2 = helpers.binary_to_float(binary[16:32], -5.0, 5.0)
	return x1, x2

# ackley is our fitness function
# This is how a user defines the goal of their problem
def ackley_fitness(solution):
	x1, x2 = solution

# Ackley's function
	# A common mathematical optimization problem
	output = -20 * math.exp(-0.2 * math.sqrt(0.5 * (x1**2 + x2**2))) - math.exp(
		0.5 * (math.cos(2 * math.pi * x1) + math.cos(2 * math.pi * x2))) + 20 + math.e

# You can prematurely stop the metaheuristic by returning True
	# as the second return value
	# Here, we consider the problem solved if the output is <= 0.01
	finished = output <= 0.01

# Because this function is trying to minimize the output,
	# a smaller output has a greater fitness
	fitness = 1 / output

# First return argument must be a real number
	# The higher the number, the better the solution
	# Second return argument is a boolean, and optional
	return fitness, finished

# Define a problem instance to optimize
# We can optionally include a decode function
# The optimizer will pass the decoded solution into your fitness function
# Additional fitness function and decode function parameters can also be added
ackley = Problem(ackley_fitness, decode_function=decode_ackley)

# Create a genetic algorithm with a chromosome size of 32,
# and use it to solve our problem
my_genalg = GenAlg(32)
best_solution = my_genalg.optimize(ackley)

print best_solution
```

Important notes:

* Fitness function must take solution as its first argument
* Fitness function must return a real number as its first return value

For further usage details, see comprehensive doc strings.

# Breaking Changes
## 09/26/2017
Renamed helpers.binary\_to\_int offset option to lower\_bound,
and renamed helpers.binary\_to\_float minimum and maximum options to
lower\_bound and upper\_bound respectively.

## 08/27/2017
Moved a number of options from Optimizer to Optimizer.optimize

## 07/26/2017
Renamed common.random\_solution\_binary to common.random\_binary\_solution,
and common.random\_solution\_real to common.random\_real\_solution

## 11/10/2016
problem now an argument of Optimizer.optimize, instead of Optimizer.\_\_init\_\_.

## 11/10/2016
max\_iterations now an argument of Optimizer.optimize, instead of Optimizer.\_\_init\_\_.

## 11/8/2016
Optimizer now takes a problem instance, instead of a fitness function and kwargs.

## 11/5/2016
Library reorganized with greater reliance on \_\_init\_\_.py.

Optimizers can now be imported with:

from optimal import GenAlg, GSA, CrossEntropy

Etc.

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