genetic algorithms · JAVA · logical operators

Genetic Algorithms + logical operators + SQLite + JAVA


00:10 import into Eclipse the downloaded zip file for ‘Genetic Algorithm Tutorial 06 – Data Mining + Logical Operators + JAVA Project’
00:31 create a new project and copy code from imported project
01:05 create a new package that will contain all 3 drivers for DB, File, & Hardcoded data
01:35 refactor code
03:34 refactor Expression class: add ability to use additional logical operators (nand, nor, xnor)

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genetic algorithms · JAVA · sqlite · traveling salesman problem

TSP + Genetic Algorithms + SQLite DB + JAVA


00:10 import into Eclipse downloaded zip file for ‘TSP Genetic Algorithm w/ JAVA Project’
00:32 create new project and copy code from imported project
01:09 add and setup sqlite jdbc jar file
01:50 create new package that will contain all 3 drivers for DB, File, & Hardcoded data
02:50 create file containing cities and their latitudes and longitudes
03:19 create and populate an sqlite database containing cities and their latitudes and longitudes
04:45 code the ‘data from database’ DBDriver class
05:50 create the database
06:15 test run the ‘data from database’ DBDriver
07:28 code the ‘data from file’ FileDriver class
09:09 test run the ‘data from file’ FileDriver

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genetic algorithms · scala

Genetic Algorithms w/ Scala – Tutorial 01


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Genetic Algorithms w/ Java – Tutorial 01 @ https://youtu.be/UcVJsV-tqlo
Genetic Algorithms w/ Python – Tutorial 01 @ https://youtu.be/zumC_C0C25c

genetic algorithms · Python · time scheduling

Class Scheduling (Genetic Algorithms w/ Python Tutorial 02)


00:07 demo the application
01:40 list of potential conflicts for this simple class scheduling application
04:56 code the application
05:10 various classes that make up the application
06:47 application Class class
07:30 coding the Course class
08:10 coding the Department class
08:32 coding the Instructor class
08:57 coding the MeetingTime class
09:20 coding the Room class
09:43 coding the Class class
10:45 coding the Data class
11:54 coding the Schedule class
14:21 calculating the schedule fitness
16:43 coding the Population class
17:24 DisplayMgr class code
18:18 test run generation # 0
20:54 coding the GeneticAlgorithm class
22:16 evolve the population
22:05 coding population crossover
22:15 handing elitism
24:22 coding population mutation
24:50 perform crossover on 2 schedules
25:15 perform mutation on a schedule
25:57 perform tournament selection
26:35 evolve the population from one generation to the next until we find a schedule with no conflicts
27:04 test run the finished application
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