Empirical Analysis of Using Weighted Sum Fitness Functions in NSGA-II for Many-Objective 0/1 Knapsack Problems.
Hisao IshibuchiNoritaka TsukamotoYusuke NojimaPublished in: UKSim (2009)
Keyphrases
- nsga ii
- weighted sum
- empirical analysis
- fitness function
- knapsack problem
- multi objective evolutionary algorithms
- multi objective
- test problems
- evolutionary algorithm
- pareto frontier
- pareto optimal solutions
- objective function
- optimization problems
- pareto optimal
- genetic programming
- genetic algorithm
- multiple objectives
- multi objective optimization
- multiobjective optimization
- multiobjective evolutionary algorithm
- optimal solution
- genetic algorithm ga
- empirical studies
- bi objective
- evolutionary computation
- combinatorial optimization problems
- theoretical analysis
- mutation operator
- evolutionary multiobjective optimization
- optimization algorithm
- evolutionary multiobjective
- linear combination
- search space
- differential evolution
- genetic operators
- completion times
- np hard
- dynamic programming
- crossover operator
- greedy algorithm
- linear program
- particle swarm optimization
- neural network
- computational complexity
- fuzzy logic
- initial population
- pixel values
- computational efficiency
- benchmark problems