Partial reinforcement optimizer: an evolutionary optimization algorithm
In this paper, a novel evolutionary optimization algorithm, named Partial Reinforcement
Optimizer (PRO), is introduced. The major idea behind the PRO comes from a psychological …
Optimizer (PRO), is introduced. The major idea behind the PRO comes from a psychological …
A survey on evolutionary reinforcement learning algorithms
Reinforcement Learning (RL) has proven to be highly effective in various real-world
applications. However, in certain scenarios, Evolutionary Algorithms (EAs) have been …
applications. However, in certain scenarios, Evolutionary Algorithms (EAs) have been …
Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities
Evolutionary algorithms (EA), a class of stochastic search methods based on the principles
of natural evolution, have received widespread acclaim for their exceptional performance in …
of natural evolution, have received widespread acclaim for their exceptional performance in …
Reinforcement learning algorithms: An overview and classification
F AlMahamid, K Grolinger - 2021 IEEE Canadian Conference …, 2021 - ieeexplore.ieee.org
The desire to make applications and machines more intelligent and the aspiration to enable
their operation without human interaction have been driving innovations in neural networks …
their operation without human interaction have been driving innovations in neural networks …
[PDF][PDF] Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution.
Electrocardiogram (ECG) signal is a measure of the heart's electrical activity. Recently, ECG
detection and classification have benefited from the use of computer-aided systems by …
detection and classification have benefited from the use of computer-aided systems by …
EMORL: Effective multi-objective reinforcement learning method for hyperparameter optimization
SP Chen, J Wu, XY Liu - Engineering Applications of Artificial Intelligence, 2021 - Elsevier
Hyperparameter optimization is critical for the performance of machine learning algorithms.
Significant efforts have been dedicated to improve the final accuracy of algorithm by …
Significant efforts have been dedicated to improve the final accuracy of algorithm by …
Reinforcement learning versus evolutionary computation: A survey on hybrid algorithms
MM Drugan - Swarm and evolutionary computation, 2019 - Elsevier
A variety of Reinforcement Learning (RL) techniques blends with one or more techniques
from Evolutionary Computation (EC) resulting in hybrid methods classified according to their …
from Evolutionary Computation (EC) resulting in hybrid methods classified according to their …
A new reinforcement learning-based memetic particle swarm optimizer
Developing an effective memetic algorithm that integrates the Particle Swarm Optimization
(PSO) algorithm and a local search method is a difficult task. The challenging issues include …
(PSO) algorithm and a local search method is a difficult task. The challenging issues include …
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective
at solving reinforcement learning (RL) problems. However, since few rigorous empirical …
at solving reinforcement learning (RL) problems. However, since few rigorous empirical …
Automated design of metaheuristics using reinforcement learning within a novel general search framework
Metaheuristic algorithms have been investigated intensively to address highly complex
combinatorial optimization problems. However, most metaheuristic algorithms have been …
combinatorial optimization problems. However, most metaheuristic algorithms have been …