AlgorithmsΒΆ
This section highlights the supported NEORL algorithms and how to setup and solve a problem. The algorithms are classified in three separate categories.
- Neural Algorithms (Reinforcement Learning)
- Evolutionary and Swarm Algorithms
- Evolution Strategies (\(\mu,\lambda\)) (ES)
- Particle Swarm Optimisation (PSO)
- Heterogeneous comprehensive learning particle swarm optimization (HCLPSO)
- Differential Evolution (DE)
- Exponential Natural Evolution Strategies (XNES)
- Grey Wolf Optimizer (GWO)
- Simulated Annealing (SA)
- Salp Swarm Algorithm (SSA)
- Whale Optimization Algorithm (WOA)
- Moth-flame Optimization (MFO)
- JAYA Algorithm
- Bat Algorithm (BAT)
- Harris Hawks Optimization (HHO)
- Ant Colony Optimization (ACO)
- Cuckoo Search (CS)
- Tabu Search (TS)
- Hybrid and Neuroevolution Algorithms
- Feedforward Neuroevolution of Augmenting Topologies (FNEAT)
- Recurrent Neuroevolution of Augmenting Topologies (RNEAT)
- Prioritized replay Evolutionary and Swarm Algorithm (PESA)
- Modern PESA (PESA2)
- RL-informed Evolution Strategies (PPO-ES)
- RL-informed Differential Evolution (ACKTR-DE)
- Neural Genetic Algorithms (NGA)
- Neural Harris Hawks Optimization (NHHO)
- Animorphic Ensemble Optimization (AEO)
- Ensemble of Differential Evolution Variants (EDEV)
- Ensemble Particle Swarm Optimization (EPSO)