Search Results for author:
Found 14 papers, 5 papers with code
Parallel Hyperparameter Optimization Of Spiking Neural Network
By defining an early stopping criterion detecting silent networks and by designing specific constraints, we were able to instantiate larger and more flexible search spaces.
SONATA: Self-adaptive Evolutionary Framework for Hardware-aware Neural Architecture Search
Our SONATA has seen up to sim$93. 6% Pareto dominance over the native NSGA-II, further stipulating the importance of self-adaptive evolution operators in HW-aware NAS.
Evolutionary Algorithms
Hardware Aware Neural Architecture Search
+1
Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework
To tackle such an issue, this paper introduces multi-objective reinforcement learning based on decomposition (MORL/D), a novel methodology bridging the literature of RL and MOO.
Multi-Objective Reinforcement Learning
reinforcement-learning
Hyperparameter Optimization for Multi-Objective Reinforcement Learning
Hence, prior research has explored hyperparameter optimization in RL to address this concern.
Hyperparameter Optimization
Multi-Objective Reinforcement Learning
+1
A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning algorithms (MORL) extend standard reinforcement learning (RL) to scenarios where agents must optimize multiple---potentially conflicting---objectives, each represented by a distinct reward function.
A Virtual-Force Based Swarm Algorithm for Balanced Circular Bin Packing Problems
Balanced circular bin packing problems consist in positioning a given number of weighted circles in order to minimize the radius of a circular container while satisfying equilibrium constraints.
An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters
Based on this search space we propose neighbourhood and evolution search operators to optimize both the architecture and hyper-parameters of our networks.
Hidden-Variables Genetic Algorithm for Variable-Size Design Space Optimal Layout Problems with Application to Aerospace Vehicles
In order to tackle this NP-hard problem, a genetic algorithm enhanced by an adapted hidden-variables mechanism is proposed.
MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments
We introduce MO-Gym, an extensible library containing a diverse set of multi-objective reinforcement learning environments.
Recovery-to-Efficiency: A New Robustness Concept for Multi-objective Optimization under Uncertainty
More precisely, in the paper the so-called recovery-to-efficiency robustness concept is proposed and investigated.
Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processes
Gaussian Processes (GPs) are one of the popular approaches to exhibit the correlations between these different fidelity levels.
Bayesian optimization of variable-size design space problems
This results in an optimization problem for which the search space varies dynamically (with respect to both number and type of variables) along the optimization process as a function of the values of specific discrete decision variables.
Bayesian Optimization using Deep Gaussian Processes
To overcome this issue, a new Bayesian Optimization approach is proposed.
Matheuristics to optimize refueling and maintenance planning of nuclear power plants
It is shown that the restriction of time windows, which was used to ease computations, induces large over-costs and that this restriction is not required anymore with the capabilities of matheuristics or local search to solve such size of instances.
