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Lecturer
Code8 and 9 are worth 6 marks each (plus 1 bonus for completing an extra homework).
Code10 is worth 6 marks.
Total: 20 marks.
For each of the following tasks, create a sub-directory called CODE8, CODE9, CODE10 that includes:
- All related python code
- A README.md file containing your report.
- Sub-directories CODEx/img for images; CODEx/data for data.
Using some URL shortener (e.g. goo.gl), shorten the URL to hw/code/x
and paste into the submission page.
For each of the following, write a Markdown readme.md file(1500 words+) Note that there will be some overlap in the content of these reports. Feel free to "borrow" text from your earlier reports for your later reports-- ideally, improving the borrowed bits each time.
Your report must
- Explain all algorithms it discussed (so I know that you know what is going on in them)
- Succinctly describe the results. Not 1000 tables, but key insights relating to a small number of key displayed
results
- No figure should be used unless it is discussed in the text;
- No text should claim a result unless there is an associated future.
Your report should be a technical report. For a list of sections of those reports, plus some tips, see
Two key parts of the reports are "threats to validity" and "future work". For notes on those sections, please see:
- threats to validity
- future work. Note that the better your future work section, the better your mark. In future work, we really can see what your learned from the experience of this work-- what strange quirks you have observered and what insights those quirks may bring.
For DE and MWS and SA, code up the Type1,Type2, Type3 comparison operators and use them to:
- Find the final era computed by DE, MWS, SA (with early termination)
- Computer the loss numbers between era0 the final era
- Important implementation note: repeat the above with 20 different baseline populations. For each baseline, run DE,MWS,SA.
Apply the above for DTLZ7 with 2 objectives 10 decisions.
Using the statistical machinery discussed in class (Scott-Knott, a12, bootstrap) to decide in any of DE, MWS, SA is best for this model. TO collect data, 20 times, generate a baseline population and for each baseline, the run DE, MWS, SA from that baseline.
GAs mutate by swapping parts of daddy into parts of mummy
- Defaults for mutation: at probability 5%
- Defaults for crossover: one point (i.e. pick a random decision, take all dad's decisions up to that point, take alll mum's decisions after that point)
- Defaults for select: for all pairs in the population, apply binary domination.
- Defaults for number of candidates: 100
- Defaults for number of generations: 1000 (but have early termination considered every 100 generations)
Do NOT analyze the result statistically, but offer succinct diagrams comparing the performance results on 20 repeats of DTLZ1,3,5,7 with 2,4,6,8 objectives and 10,20,40 decisions
Use DE to tune your defaults for GAs.
Create at least three options for mutation, crossover, select, number of candidates, number of generations. See if standard DE (default control settings) can improve on the scores seen in COde9.
Using the statistical machinery discussed in class (Scott-Knott, a12, bootstrap) to decide in any of tuned or untuned GAs are best for DTLZ1,3,5,7 with 2,4,6,8 objectives and 10,20,40 decisions. Remember to repeat your runs of tuned vs untuned using the same baseline populations.
Copyright © 2015 Tim Menzies.
This is free and unencumbered software released into the public domain.
For more details, see the license.