Module Code
COM00037H
DEPARTMENT OF COMPUTER SCIENCE
Evolutionary & Adaptive Computing
Open Individual Assessment
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Module Code
COM00037H
Rubric
Your submission should be a single zip file named after your exam number,
Yxxxxxxx.zip, which contains:
• For Part 1 of this assessment
–a single Jupyter or Colab notebookEVAC1.ipynbcombining code and
explanations
–a PDFEVAC1.pdfof the state of the same notebook after all of its code
has been executed.
• For Part 2 of this assessment
–a pair of NetLogo filesEVAC2-train.nlogoandEVAC2-test.nlogo,
possibly accompanied by additional image and data files. The first of these
files should allow to run the evolutionary algorithm (with a relevant
description in its Info section). The second file should contain an already
evolved behaviour for every dog, and allow for the performance of the
evolved herding dogs to be tested. It should also contain an Info section
with a report on the results and all remaining comments.
If there are discrepancies between the contents and output of the Jupyter/Colab
notebook and what is shown in the PDF, the former will be used for marking.
Do not use archive formats other than zip.Your submission MUST be a single zip
archive. Your code should assume any auxiliary files that you may generate are in the
same folder as the notebook from which they are accessed.
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Module Code
COM00037H
1 Evolution of a function (this part is worth 50 marks)
1.1 Scenario
The scenario for this part of the assessment is the approximation of the function to
predict the number of e-scooters that will be hired given a particular date, hour, holiday
and weather information. The dataset contains a count of e-scooters rented per hour,
with the corresponding weather data and holiday information.
The data for this assessment may be found on the Assessment page of the module
VLE site.
1.2 Task
This assessment requires you to use an evolutionary algorithm to evolve a function that
will generate a prediction of the number of e-scooters required, given a time, day and
weather information. The evolutionary algorithm may beEITHERGenetic
ProgrammingORa Genetic Algorithm evolving a neural network.
You must follow these rules of implementation:
(a) YouMUSTuse Python and the DEAP library to implement an evolutionary
algorithm.
(b) YouMUSTuse either a Genetic Programming approach or a neural network
representation.
(c) YouMUSTsubmit a Colab or Jupyter notebook containing runnable code for your
evolved solution.
(d)
YouMUSTevaluate thoroughly the performance of the evolved solution in terms of
accuracy, precision, and so on as appropriate to the problem.
1.3 Data
For this assessment, you will use the data in the file eScooterDemand.csv, a file that
contains the following variables:
1. Date : day/month/year
2. Count - Count of e-scooters rented
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Module Code
COM00037H
3. Hour - Hour of the day
4. Temp - Temperature in Celsius
5. Humidity - %
6. Windspeed - m/s
7. Visibility - 10m
8. Dew point - Celsius
9. Sunshine - MJ/m2
10. Rain - mm
11. Snow - cm
12. Seasons - Winter, Spring, Summer, Autumn
13. Public Holiday - Yes/No (Public Holiday or not)
14. HireAvailable - Yes/No (hire scheme operating)
1.4 To do
You should report on this part of the assignment by adding comments to the
Colab/Jupyter notebook you have created.
The marking criteria assume there is working code. Partial marks may be allocated for
design alone. No results will be accepted if the corresponding parts of the code
involved in their generation cannot be executed.
1. Description of the algorithm implemented [15 marks]
Give a full description of the chosen representation for individuals, and of the
algorithm that you finally have chosen. You should give a clear justification for all
the design choices made.
2. Quality of code [5 marks]
You must provide runnable code and a solution for the evolutionary algorithm
using Python and DEAP. Your code should be well commented, and demonstrate
the competent use of features of DEAP.
3. Investigation of parameters and representation [10 marks]
You should make a systematic investigation of the effect of parameters and of the
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Module Code
COM00037H
choice of representation in order to improve evolution of the solution. Where you
have tested ideas experimentally you should present statistics to support
decisions about parameters.
4. Evaluation of your solution [20 marks]
You should evaluate your algorithm or algorithms thoroughly. You should present
appropriate summary statistics and plots in your notebook to support statements
about the effectiveness of the solution, and give appropriate interpretation and
discussion of results.
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COM00037H
1.5 Marking criteria
QMarksSophisticatedSatisfactoryNeeds work
1.115Description of the algorithm implemented
15 - 1110 - 65 - 0
Algorithm design is very
clearly explained. Justific-
ation for all design choices
is robust. Choice of rep-
resentation is clearly mo-
tivated.
Explanation of the al-
gorithm is mostly clear.
Use of evolution is ap-
propriate. Representation
is clear and well justified.
Overall design choices jus-
tified.
Algorithm is unclear or
does not use evolution,
or uses it inappropriately.
Representation is unclear.
Little or no justification for
design choices.
1.25Quality of code
5 - 43 - 21 - 0
Evidenceofrunnable
code, with competent use
of a range of DEAP fea-
tures. Implementation is
very clear, with good use
of comments to explain
what has been done.
Evidenceofrunnable
code, with straightforward
use of DEAP implement-
ation is mostly clear,
with use of comments to
explain what has been
done.
No code, or code not run-
nable. Code does not use
DEAP for evolution. Code
not documented. Code
runs but does not appear
to evolve the solution.
1.310Investigation of parameters and representation
10 - 76 - 43 - 0
Clear investigation of the
effect of parameters and
the choice of representa-
tion, clearly linked to prin-
ciples taught across the
module (or beyond).
Some investigation of how
to improve the solution
Some lack of clarity on
what has been done or
the degree of improve-
ment Not clearly linked to
what has been taught.
Basic algorithm. No evid-
ence of investigation of
parameters or the repres-
entation to improve per-
formance.
1.420Evaluation of solution
20 - 1413 - 87 - 0
Thorough evaluation of
what has been attempted.
Clear presentation of stat-
istics supporting insightful
interpretation and discus-
sion of results.
At least partial evaluation,
although it may be more
thorough in some as-
pects than others. Some
presentation of statistics
supporting interpretation
and discussion of results.
Little or no attempt to eval-
uate what has been done.
No presentation of statist-
ics. No or little interpreta-
tion or discussion.
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Module Code
COM00037H
2 Evolving Herding Dogs (this part is worth 50 marks)
2.1 Scenario
You need to implement a simple multi-agent environment with two types of agents,
sheep (plural) and dogs. All agents are placed on a square, two-dimensional, wrapped
around an orthogonal grid made ofN×Npatches. At the beginning of each run,S
sheep andDdogs are placed in the environment in a (pseudo-)random way. Both
sheep and dogs can choose from five possible actions: moving in one of the four
cardinal directions (North, South, West, East) or staying in the same square. The
environment is updated in rounds. All agents are prompted in turn to plan their next
move, after which all moves are carried out simultaneously. There is no limit on the
number of agents that can reside in the same patch.
Sheep will be using a simple, fixed behaviour, choosing exactly one of the following
actions, in order of preference (from highest to lowest):
1.
If there is a dog in your current patch, move, if possible, to a patch without a dog;
2. If there is a dog in any of the four adjacent patches (i.e. North, South, East or
West of the current one), move, if possible, to an adjacent patch that does not
contain a dog;
3. Move to a patch with no sheep, but which is adjacent to a patch with sheep;
4. Move to an adjacent patch containing fewer sheep than the current patch;
5.
Make a stochastic choice of action as follows: choose the same action as the last
one with 50% probability, or choose one of the remaining four actions, each with
12.5% probability. For the first move, assume for all sheep that their previous
move was to stay put.
The initial behaviour for all dogs is to choose one of the five possible actions with the
same 20% probability. Dogs can see the position (coordinates of the current patch) of
all other dogs and all sheep, and they can tell which species each agent belongs to.
2.2 Task
On the most general level, the goal here is to evolve a team (“pack”) of herding dogs,
which would constantly strive to keep all sheep in as tight a formation as possible.
Numerically, we can say the dogs will be aiming to minimise the score defined as the
sum of the variances of X and Y coordinates for all sheep:
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Score=
S
X
i=1
(x
i
− ̄x)
2
+
S
X
i=1
(y
i
− ̄y)
2
S
Design, implement, describe and evaluate a procedure that employs evolution to
produce herding behaviour for the dogs that outperforms as best as possible their
default behaviour. Each of the dogs should be able to evolve a separate behaviour,
which is potentially different from the rest. The size of the environment can be changed
at will for evolution purposes, but the resulting behaviour should be tested for 50 sheep,
5 dogs, and an environment of sizeN= 49, i.e. where patches have coordinates
ranging from (-24,-24) to (24,24).
2.3 To do
1. Representation of dogs’ behaviour [5 marks]
Choose a simple and efficient representation of your dogs’ behaviour that would
also allow for adaptation. Describe the chosen representation and explain the
reasons behind it.
2. Implementation of default behaviour and fitness estimation [10 marks]
Provide the necessary, working code implementing the simulation where your
chosen representation of dogs’ behaviour is set to their default behaviour, then
describe how running the simulation is going to provide data for estimating the
fitness of your dogs.
3. Design and implementation of adaptation [20 marks]
Describe the design of (10 marks), and implement (10 marks) a procedure that
uses adaptation to optimise the behaviour of your agents.
4. Design of evaluation procedure [10 marks]
Design and describe an evaluation procedure that allows you to compare the
behaviour obtained through adaptation to the initial, non-adaptive behaviour, and
draw conclusions that are grounded on sound statistical arguments.
5. Experimental evaluation [5 marks]
Collect experimental evidence, carry out, and show the results of the evaluation
procedure described above.
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Module Code
COM00037H
2.4 Marking Criteria
1. Three marks for a rational encoding making use of the most relevant information,
with up to another 2 marks for a well-argued effort to find a good trade-off in the choice
of representation that reduces the search space of distinct behaviours with as little loss
of expected performance as possible.
2. Up to 5 marks for a running, easy to use, and well-described implementation of the
simulator, and up to 5 marks for the design of a setup that evaluates the dogs’ fitness.
3. A maximum of 10+10 marks for a well-implemented, described and argued
procedure that provides at least the functionality of a generic GA (which would score
4+4 marks max), explores systematically the meta-parameter space to achieve best
performance (2+2 marks), and implements at least 3 of the following: fitness scaling or
equivalent (e.g. ranking), elitism, niching/crowding, adaptive mutation rates, fitness
sharing (up to 4+4 marks).
4. Four marks for a solution that includes an unbiased exploration of the space of initial
configurations, six marks for a sound choice and detailed description of a statistical
tool/mechanism for the comparison (e.g. choosing the most appropriate statistical test,
well argued for).
5. Full marks for a substantial range of experiments, well presented and summarised
through the evaluation procedure.
All of the above assumes there is working code implementing the individual objectives.
Partial marks may be allocated for design alone. No results will be accepted, if the
corresponding parts of the code involved in their generation cannot be executed.
The criteria to be used in the marking also include clarity, simplicity, generality and
rigour of the methods chosen, as well as the ability to describe, analyse and visualise
experimental results in an effective way.
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