Evolutionary Algorithm implementation in Python

Learn to use Genetic Algorithm and Evolutional Algorithm.

---

Environment

Python 3.6.4

NumPy

Pandas

matplotlib

---

Evolutionary algorithm includes:

• Genetic algorithm

  • This is the most popular type of EA. One seeks the solution of a problem in the form of strings of numbers (traditionally binary, although the best representations are usually those that reflect something about the problem being solved), by applying operators such as recombination and mutation (sometimes one, sometimes both). This type of EA is often used in optimization problems. Another name for it is fetura, from the Latin for breeding.

• Genetic programming

• Evolutionary programming

• Gene expression programming

• Evolution strategy

  • Works with vectors of real numbers as representations of solutions, and typically uses self-adaptive mutation rates.

• Differential evolution

• Learning classifier system

---

Genetic Algorithm

Example 1: Find max Humidity

Dataset: Air Quality Dataset

Code

Example 2: Travel Sales Problem

What is TSP?

Code

---

Evolutionary Strategy

---

Reference:

1. S. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005
2. https://morvanzhou.github.io/tutorials/machine-learning/evolutionary-algorithm/
3. https://en.wikipedia.org/wiki/Evolutionary_algorithm
4. https://towardsdatascience.com/introduction-to-evolutionary-algorithms-a8594b484ac