NSGA-II implementation details may influence quality of solutions for the job-shop scheduling problem.
Maxim BuzdalovIrina PetrovaArina BuzdalovaPublished in: GECCO (Companion) (2014)
Keyphrases
- implementation details
- job shop scheduling problem
- nsga ii
- benchmark problems
- test problems
- multi objective
- pareto optimal
- job shop scheduling
- pareto optimal solutions
- multi objective evolutionary algorithms
- benchmark instances
- tabu search algorithm
- multi objective optimization problems
- multi objective optimization
- evolutionary algorithm
- tabu search
- genetic algorithm
- optimization problems
- optimal solution
- genetic operators
- simulated annealing
- critical path
- bi objective
- multiobjective optimization
- uniform design
- pareto frontier
- multiobjective evolutionary algorithm
- multi objective differential evolution
- scheduling problem
- multi objective problems
- evolutionary multiobjective optimization
- scatter search
- memetic algorithm
- artificial immune system
- pareto optimal set
- metaheuristic
- evolutionary multiobjective
- knapsack problem
- strength pareto evolutionary algorithm
- optimization algorithm
- constrained multi objective optimization problems
- combinatorial optimization problems
- particle swarm optimization
- fitness function
- feasible solution
- vehicle routing problem
- efficient solutions
- fuzzy logic
- evolutionary computation
- cost function
- solution quality
- multiple objectives
- computational complexity