https://github.com/nemat-al/evolutionary_computing
Tasks for Evolutionary Computing Course @ ITMO University.
https://github.com/nemat-al/evolutionary_computing
evolutionary-algorithms evolutionary-computation optimization queens-problem queens-puzzle
Last synced: 7 months ago
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Tasks for Evolutionary Computing Course @ ITMO University.
- Host: GitHub
- URL: https://github.com/nemat-al/evolutionary_computing
- Owner: nemat-al
- Created: 2024-04-19T15:56:32.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-19T17:01:00.000Z (over 1 year ago)
- Last Synced: 2025-01-23T08:44:32.209Z (8 months ago)
- Topics: evolutionary-algorithms, evolutionary-computation, optimization, queens-problem, queens-puzzle
- Language: Jupyter Notebook
- Homepage:
- Size: 81.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Evolutionary_Computing
Tasks for Evolutionary Computing Course at ITMO University
----
## Index
1. [First Task : Function Optimization](#first-task--function-optimization)
2. [Second Task]()
## [First Task : Function Optimization](https://github.com/nemat-al/Evolutionary_Computing/blob/main/ec_lab1_Aloush.ipynb)
The goal is to play around basic implementation of Genetic Algorithm and tune it in a way that it will able to find approximate optimal solution of given function.
The target function is modification of [Rastrgin function](https://www.sfu.ca/~ssurjano/rastr.html). It is reversed, displaced and scaled in such a way that result values are in range [0.0, 10.0]. Possible range of input variables are [-5, 5] for all variables.
[Deap](https://deap.readthedocs.io/en/master/) framework was used.
It was required that the algorithm must find solutions with fitness values around 9.9, and the algorithm successfully found solutions with best fitness value equal to 9.954.
## [Second Task](https://github.com/nemat-al/Evolutionary_Computing/blob/main/ec_lab2_Aloush.ipynb)
The goal is to develop an Evolutionary algorithm to solve the queens puzzle. This is classic optimisation problem, where you have to allocate N queens across NxN chessboard in such a way, so they will not conflict to each other. The N is a parameter and can be varied. Try to think how to represent your solutions and how to perform mutation and crossover.
[Deap](https://deap.readthedocs.io/en/master/) framework was used.
The solution is based on the following [tutorial](https://nbviewer.org/github/concision/n-queens/blob/master/notebook.ipynb).