[Turing Data Story] Genetic Algorithms and Multi-Objective Optimisation

[jack89roberts]

Story description

Please provide a high level description of the Turing Data Story

I gave a tech talk with an introduction to genetic algorithms and multi-objective optimisation (though we ran out of time for that so it will be in a part 2). It used this notebook: https://github.com/jack89roberts/NSGA-II/blob/main/NSGA-II.ipynb

Adapt that into two Turing data stories:

• 1: Genetic algorithms
• 2: Multi-objective algorithms

And adapt to link it more closely to real-world applications (e.g. the spatial inequality in the smart city project).

Which datasets will you be using in this Turing Data Story? Cite the dataset that are going to be used in this story (these of course can change whilst developing the story).

ONS 2011 Census data (output area populations)

Additional context Add any other context or screenshots about the story here.

https://github.com/jack89roberts/NSGA-II/blob/main/NSGA-II.ipynb

Ethical guideline

Ideally a Turing Data Story has these properties and follows the 5 safes framework.

• [x] The analysis you produce is openly available and reproducible.
• [x] Any data used are open and have an explicit licence, provenance and attribution.
• [x] Any data used are not personal data (i.e. the data is anonymous or anonymised).
• [x] Any linkage of datasets in your data story does not lead to an increased risk of the personal identification of individuals.
• [x] The Story must be truthful and clear about any limitations of analysis (and potential biases in data).
• [x] The Story will not lead to negative social outcomes, such as (but not limited to) increasing discrimination or injustice.

Current status

• [x] Scoping of the story
• [x] Write story outline
• [x] Add material to the story notebook
• [ ] Combine materials into a readable story
• [ ] Proofread
• [ ] Request reviews
• [ ] Address reviews
• [ ] Merge to master branch.

Updates