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

## Project description

## 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)

## Installation

pip install optimal

## Usage

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.

## Major Changes

### 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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