AFL-RL: A Reinforcement Learning Based Mutation Scheduling Optimization Method for Fuzzing.
Menglin LiHaoran ZhuHaochen ZhangJingtian LiuPublished in: HP3C (2023)
Keyphrases
- optimization method
- reinforcement learning
- differential evolution
- evolutionary algorithm
- genetic algorithm
- optimization algorithm
- scheduling problem
- optimization process
- optimization methods
- simulated annealing
- global optimum
- reinforcement learning algorithms
- particle swarm
- model free
- function approximation
- fitness function
- particle swarm optimization
- mutation operator
- state space
- convergence speed
- multi objective
- nonlinear optimization
- optimization procedure
- rl algorithms
- hybrid algorithm
- metaheuristic
- evolutionary computation
- particle swarm optimization pso
- markov decision processes
- optimal policy
- temporal difference
- optimization problems
- quasi newton
- learning algorithm
- partially observable domains
- nelder mead simplex
- optimal control
- transfer learning
- multi agent
- machine learning
- learning problems
- markov decision process
- action space
- state and action spaces
- direct policy search
- learning classifier systems
- crossover operator
- learning agent
- harmony search
- dynamic programming
- biogeography based optimization
- neural network