Discussion Overview
The discussion centers around the need for a solved numerical example of a genetic algorithm, specifically for one iteration, with a focus on the knapsack problem. Participants express a desire for clear, step-by-step explanations suitable for exam preparation.
Discussion Character
- Homework-related
- Debate/contested
Main Points Raised
- One participant requests a clear, hand-solved example of a genetic algorithm, expressing frustration at the lack of resources available online.
- Another participant suggests downloading Python code examples and using a debugger to understand the algorithm better.
- Some participants challenge the availability of downloadable code, stating that most resources are libraries rather than standalone examples.
- There is a disagreement regarding the usefulness of a specific GitHub repository, with some participants finding it inadequate for their needs.
- One participant emphasizes that the code linked may not be in the conventional 'worked example' format but still serves as a solved example.
- Another participant outlines specific exam requirements, including explaining the steps of the genetic algorithm and performing one round of the algorithm using a given fitness function.
- There is mention of variability in the number of generations required to find optimal solutions due to randomization in genetic algorithms.
Areas of Agreement / Disagreement
Participants do not reach a consensus on the adequacy of the provided code examples or the clarity of the resources available. Some believe the linked code is a valid example, while others find it insufficient for their understanding.
Contextual Notes
Participants express varying levels of familiarity with genetic algorithms and coding, which affects their ability to engage with the provided resources. The discussion reflects differing expectations regarding the format and detail of examples needed for academic purposes.