An ACO-based Hyper-heuristic for Sequencing Many-objective Evolutionary Algorithms that Consider Different Ways to Incorporate the DM's Preferences.
Gilberto RiveraLaura Cruz ReyesEduardo Fernández-GonzálezClaudia Gómez SantillánNelson Rangel-ValdezCarlos A. Coello CoelloPublished in: Swarm Evol. Comput. (2023)
Keyphrases
- hyper heuristics
- evolutionary algorithm
- multi objective
- multiple objectives
- genetic programming
- examination timetabling
- graph coloring
- optimization problems
- evolutionary computation
- multi objective optimization
- fitness function
- cutting stock problems
- ant colony optimization
- difficult problems
- metaheuristic
- simulated annealing
- differential evolution
- constraint satisfaction problems
- evolution strategy
- heuristic search
- timetabling problem
- search procedure
- differential evolution algorithm
- decision making
- search strategies
- heuristic methods
- crossover operator
- decision makers
- genetic algorithm
- multiobjective optimization
- user preferences
- nsga ii
- neural network
- multi objective evolutionary algorithms
- ant colony optimisation
- dynamic programming