Multi-strategy multi-objective differential evolutionary algorithm with reinforcement learning
Multiobjective evolutionary algorithms (MOEAs) have gained much attention due to their
high effectiveness and efficiency in solving multiobjective optimization problems (MOPs) …
high effectiveness and efficiency in solving multiobjective optimization problems (MOPs) …
Reinforcement learning-based particle swarm optimization with neighborhood differential mutation strategy
The particle swarm optimization (PSO) algorithm has been one of the most effective methods
for solving various engineering optimization problems. Most existing PSO variants frequently …
for solving various engineering optimization problems. Most existing PSO variants frequently …
A survey on learnable evolutionary algorithms for scalable multiobjective optimization
Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
AS-NAS: Adaptive scalable neural architecture search with reinforced evolutionary algorithm for deep learning
Neural architecture search (NAS) is a challenging problem in the design of deep learning
due to its nonconvexity. To address this problem, an adaptive scalable NAS method (AS …
due to its nonconvexity. To address this problem, an adaptive scalable NAS method (AS …
A survey of fitness landscape analysis for optimization
F Zou, D Chen, H Liu, S Cao, X Ji, Y Zhang - Neurocomputing, 2022 - Elsevier
Over past few decades, as a powerful analytical tool to characterize the fitness landscape of
a problem, fitness landscape analysis (FLA) has been widely concerned and utilized for all …
a problem, fitness landscape analysis (FLA) has been widely concerned and utilized for all …
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 …
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey
Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
Differential evolution with hybrid parameters and mutation strategies based on reinforcement learning
Z Tan, Y Tang, K Li, H Huang, S Luo - Swarm and Evolutionary …, 2022 - Elsevier
Differential evolution (DE) has recently attracted a lot of attention as a simple and powerful
numerical optimization approach for solving various real-world applications. However, the …
numerical optimization approach for solving various real-world applications. However, the …
Heuristic smoothing ant colony optimization with differential information for the traveling salesman problem
The traveling salesman problem (TSP) is an NP complete problem with potential
applications. Thus far, numerous approaches have been proposed to solve the TSP …
applications. Thus far, numerous approaches have been proposed to solve the TSP …
Evolutionary sampling agent for expensive problems
Data-driven evolutionary algorithms are widely studied for their ability to solve expensive
optimization problems in engineering and science. This article introduces a novel …
optimization problems in engineering and science. This article introduces a novel …