Refactor resource fitness functions and Lexicographic mode of GA #65
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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
- 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
- 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
# Conflicts: # pyproject.toml
- Add condition mating_amount > 1 in mate_scheduling_order to prevent errors on small instances - Fix mistake in contractors mutation masking in mutate_resources
# Conflicts: # sampo/scheduler/genetic/operators.py
Change logic of Lexicographic mode of GA to set optimization function after reaching deadline
Timotshak
added
enhancement
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fitness function for optimizization after reaching deadline