Adapt Genetic Algorithm to multi-objective tasks #76
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Timotshak
commented
Jan 25, 2024
•
Timotshak
commented
- Add NSGA2 selection in genetic algorithm to perform multi-objective optimization
- Change return type of fitness functions to tuple
- Add multiobjective fitness function TimeAndResourcesFitness
- Add is_multiobjective and fitness_weights parameters in Genetic Scheduler
- Add multiobjective scheduling methods to Genetic Scheduler
- Add Individual and IndividialFitness classes to change them dynamically what can't be done through the deap.creator
- Сhange the logic of genetic operators. Now they work with chromosomes, and individuals are then created in a genetic algorithm
- Replace HallOfFame by ParetoFront in genetic algorithm to perform multi-objective optimization. In the case of single-objective optimization, the behavior will not change
- Add make_offspring function to reduce code
- Add test of multi-objective genetic scheduling
- Add fitness_weights and use_pareto_domination parameters in genetic algorithm - Add Individual and IndividualFitness classes - Add NSGA-II selection in genetic algorithm
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