Update genetic algorithm #52
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- Update the genetic operators so that they create only valid chromosomes. Remove the validity check in the schedule_builder. This allows to control the number of schedules created during the execution of the genetic algorithm through parameters population_size and generation_number. This also increases the number of individuals in offspring obtained at each iteration, which improves the quality of the genetic algorithm. - Introduce combined operators of mutation and crossover. They combine operators for order and resources to make them easier to use in genetic algorithm - Update mutation operators of resources and resource borders by replacing the uniform distribution of possible gene mutations to improve the stability of chromosome changes - Replace loops in genetic operators with numpy operations for time improvement on large instances - Introduce two-point crossover operator for order. It has higher performance on large instances - Introduce resource borders updating function. It changes the resource borders to the peak values in the schedule obtained from the chromosome. This is necessary to track current resource borders when optimizing resources - Add to resources crossover operator ability to change resource borders based on parents. This is necessary to create valid chromosomes when optimizing resources - Update the schedule_builder and Genetic Algorithm, adding the ability to run it in a two-stage mode, which implements lexicographic optimization for planning to a deadline. This approach is better than integrating the deadline into the optimization criterion because it allows one to obtain solutions that exactly satisfy the deadline. - Introduce new tests for operators, two-stage genetic algorithm and resource borders updating function
StannisMod
requested changes
Sep 19, 2023
- Remove deadline from constructor of Genetic Scheduler - Remove rudimentary functionality from fitness functions. Evaluate method and evaluator attribute are removed - Extract generalized function mutate_values for resources and resource borders mutation - Remove asserts in genetic algorithm - Introduce categorical type for individuals. It is needed in genetic algorithm
- Delete try-except blocks - Add set_deadline method calling
Timotshak
added
the
refactor
StannisMod
requested changes
Sep 20, 2023
- Update logic of fitness functions in genetic algorithm. Genetic Scheduler initialisation takes constructor of fitness object, that creates in each schedule method calling. Schedule_builder takes now fitness object - Make IndividualType uppercase
StannisMod
previously approved these changes
Sep 20, 2023
StannisMod
approved these changes
Sep 20, 2023
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Update the genetic operators so that they create only valid chromosomes. Remove the validity check in the schedule_builder. This allows to control the number of schedules created during the execution of the genetic algorithm through parameters population_size and generation_number. This also increases the number of individuals in offspring obtained at each iteration, which improves the quality of the genetic algorithm.
Introduce combined operators of mutation and crossover. They combine operators for order and resources to make them easier to use in genetic algorithm
Update mutation operators of resources and resource borders by replacing the uniform distribution of possible gene mutations to improve the stability of chromosome changes
Replace loops in genetic operators with numpy operations for time improvement on large instances
Introduce two-point crossover operator for order. It has higher performance on large instances
Introduce resource borders updating function. It changes the resource borders to the peak values in the schedule obtained from the chromosome. This is necessary to track current resource borders when optimizing resources
Add to resources crossover operator ability to change resource borders based on parents. This is necessary to create valid chromosomes when optimizing resources
Update the schedule_builder and Genetic Algorithm, adding the ability to run it in a two-stage mode, which implements lexicographic optimization for planning to a deadline. This approach is better than integrating the deadline into the optimization criterion because it allows one to obtain solutions that exactly satisfy the deadline.
Introduce new tests for operators, two-stage genetic algorithm and resource borders updating function