Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning.
Arina BuzdalovaMaxim BuzdalovPublished in: ICMLA (1) (2012)
Keyphrases
- evolutionary algorithm
- fitness function
- reinforcement learning
- evolutionary computation
- multi objective
- genetic programming
- optimization problems
- differential evolution
- genetic algorithm ga
- multi objective optimization
- genetic operators
- differential evolution algorithm
- simulated annealing
- genetic algorithm
- constrained optimization problems
- evolutionary process
- evolutionary search
- nsga ii
- mutation operator
- neural network
- fitness evaluation
- particle swarm
- initial population
- optimization algorithm
- state space
- artificial neural networks