Genetic Algorithms (01) - Python Prototype Project
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00:01 quickly go over the various parts of this tutorial
00:31 demo a prebuilt version of the application
01:26 a chromosome is a potential solution
01:54 define fitness in this app. context
02:18 population of chromosomes evolves from one generation to the next using selection, crossover, mutation, and elitism
03:00 code the app.
03:38 go over various classes in this app.
04:20 code the Chromosome class
06:13 code Chromosome get_fitness method
07:15 code Population class
08:22 instantiate new population and sort it by fitness
09:45 test run the app. (before writing rest of code)
10:12 code GeneticAlgorithm class
10:15 evolve population from one generation to the next
10:52 population crossover and population mutate methods
11:43 test run app. before adding mutation and crossover functionality (evolve method returns same population i.e. never ending loop)
13:35 implement crossover population functionality
14:00 move elite chromosomes as-is to next generation
15:45 use tournament selection functionality
17:52 implement mutate population functionality
18:58 implement mutate and crossover chromosome functionality
20:00 implement tournament selection functionality
21:08 test run completed app.
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