Genetic algorithm - timetable: Difference between revisions

Latest revision as of 14:58, 13 December 2021

This a problem set for you to work through ^[1]

This is a problem set. Some of these are easy, others are far more difficult. The purpose of these problems sets are:

to build your skill applying computational thinking to a problem
to assess your knowledge and skills of different programming practices

Learning objective[edit]

The learning objective for this problem set is to apply your understanding of genetic algorithms to satisfy course requests.

The Problem[edit]

Please find below a python file which creates 4 dictionaries of requests, one for grade 9, 10, 11 and 12. Please execute this program and review the dictionaries and lists.

Please construct a genetic algorithm which ideally meets the maximum number of requests.
Your algorithm should meet the maximum number of requests.
Your algorithm should have a fitness function
Your algorithm should have selection strategy

Please understand we are only dealing with requests. Creating the timetable (when each class meets) is a different problem set.

timetable generator[edit]

I am very grateful to a 12th grade student who wrote this timetable generator for me in 2021. :-)

import random

COURSE_SIZE = 40
CLASS_SIZE = 50
CAPACITY_MIN = 30
CAPACITY_MAX = 60
REQUEST_COURSE_SIZE = 6

courses = [i for i in range(COURSE_SIZE)]

grade_9_students = [i for i in range(CLASS_SIZE)]
grade_10_students = [i for i in range(CLASS_SIZE, CLASS_SIZE*2)]
grade_11_students = [i for i in range(CLASS_SIZE*2, CLASS_SIZE*3)]
grade_12_students = [i for i in range(CLASS_SIZE*3, CLASS_SIZE*4)]

# Capacity is the course id and then capacity of course:
capacity = [[i, random.randint(CAPACITY_MIN, CAPACITY_MAX)] for i in range(COURSE_SIZE)]

def generate_request_dict(grade):
    request_dict = {}
    for i in grade:
        request_dict[i] = random.sample(courses, REQUEST_COURSE_SIZE)
    return request_dict

requests_dict_9, requests_dict_10, requests_dict_11, requests_dict_12 = generate_request_dict(grade_9_students), generate_request_dict(grade_10_students), generate_request_dict(grade_11_students), generate_request_dict(grade_12_students)


# to see the dictionaries for each part of our schedule, uncomment the lines below
# and execute this program
# print(requests_dict_9)
# print(requests_dict_10)
# print(requests_dict_11)
# print(requests_dict_12)

How you will be assessed[edit]

Your solution will be graded using the following axis:

Scope

To what extent does your code implement the features required by our specification?
To what extent is there evidence of effort?

Correctness

To what extent did your code meet specifications?
To what extent did your code meet unit tests?
To what extent is your code free of bugs?

Design

To what extent is your code written well (i.e. clearly, efficiently, elegantly, and/or logically)?
To what extent is your code eliminating repetition?
To what extent is your code using functions appropriately?

Style

To what extent is your code readable?
To what extent is your code commented?
To what extent are your variables well named?
To what extent do you adhere to style guide?

References[edit]

↑ http://www.flaticon.com/

A possible solution[edit]

Click the expand link to see one possible solution, but NOT before you have tried and failed!

not yet!

[1] ttp://www.flaticon.com/

[1]

@@ Line 9: / Line 9: @@
 == Learning objective ==
-The learning objective for this problem set is to apply your understanding of genetic algorithms.
+The learning objective for this problem set is to apply your understanding of genetic algorithms to satisfy course requests.
 == The Problem ==
 Please find below a python file which creates 4 dictionaries of requests, one for grade 9, 10, 11 and 12.
+Please execute this program and review the dictionaries and lists.
-. Please construct a genetic algorithm which ideally meets the maximum number of requests.
+# Please construct a genetic algorithm which ideally meets the maximum number of requests.
-. Your algorithm should meet the maximum number of requests.
+# Your algorithm should meet the maximum number of requests.
-. Your algorithm should have a fitness function
+# Your algorithm should have a fitness function
-. your algorithm should have selection strategy
+# Your algorithm should have selection strategy
+Please understand we are only dealing with requests. Creating the timetable (when each class meets) is a different problem set.
 == timetable generator ==
+I am very grateful to a 12th grade student who wrote this timetable generator for me in 2021. :-)
 <syntaxhighlight lang="python">
 import random
-COURSE_SIZE = 15
+COURSE_SIZE = 40
 CLASS_SIZE = 50
-TEACHERS_SIZE = 10
+CAPACITY_MIN = 30
-ROOM_SIZE = 10
+CAPACITY_MAX = 60
-BLOCKS_SIZE = 4
-CAPACITY_MIN = 18
-CAPACITY_MAX = 25
 REQUEST_COURSE_SIZE = 6
@@ Line 41: / Line 41: @@
 grade_12_students = [i for i in range(CLASS_SIZE*3, CLASS_SIZE*4)]
-teachers = [i for i in range(TEACHERS_SIZE)]
+# Capacity is the course id and then capacity of course:
-rooms = [i for i in range(ROOM_SIZE)]
+capacity = [[i, random.randint(CAPACITY_MIN, CAPACITY_MAX)] for i in range(COURSE_SIZE)]
-# for this timetable, there are 4 blocks per day
-blocks = [i for i in range(BLOCKS_SIZE)]
-# Capacity is the room ID and then capacity of course:
-capacity = [[i, random.randint(CAPACITY_MIN, CAPACITY_MAX)] for i in rooms]
 def generate_request_dict(grade):
@@ Line 58: / Line 51: @@
 requests_dict_9, requests_dict_10, requests_dict_11, requests_dict_12 = generate_request_dict(grade_9_students), generate_request_dict(grade_10_students), generate_request_dict(grade_11_students), generate_request_dict(grade_12_students)