A genetic algorithm (GA) is a method that can leverage evolution based heuristic techniques to solve an optimization problem. Optimization is described as the process of making things better by every run, or the process of finding the best possible values for the input, so that the expected output values are deduced. GAs have shown tremendous success in solving complex optimization problems.
By Day 7
Select source codes from one of links below and understand how genetic algorithms are implemented to solve a problem (do not follow the steps in the tutorial):
Mathur, N. (2019, April 4). Using genetic algorithms for optimizing your models. Retrieved from https://hub.packtpub.com/using-genetic-algorithms-for-optimizing-your-models-tutorial/
Gad, A. (2018). Genetic algorithm implementation in Python. Retrieved from https://towardsdatascience.com/genetic-algorithm-implementation-in-python-5ab67bb124a6
(i) Examine the source codes from your chosen link.
(ii) Describe the overall goals of the program in your own words.
(iii) Explain the basis of the program, including algorithm(s) used.
(iv) Describe in a step-by-step fashion the procedure, functions, or methods used for the program.
1.. Add at least two new features or functions to the program code and explain the features or functions.
2.. Run the program and present all the results, including screenshots.
3.. Clearly summarize what, if any, conclusions you were able to draw from the results in the lab.
4.. Discuss any possible errors that may have occurred during the lab. What are the major sources of errors? How were these errors resolved? How could the program be improved?