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Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search
Denoising autoencoder genetic programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming approach that uses...
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A stable data-augmented reinforcement learning method with ensemble exploration and exploitation
Learning from visual observations is a significant yet challenging problem in Reinforcement Learning (RL). Two respective problems, representation...
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Active Learning by Extreme Learning Machine with Considering Exploration and Exploitation Simultaneously
As an important machine learning paradigm, active learning has been widely applied to scenarios in which it is easy to acquire a large number of...
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CSDSE: Apply Cooperative Search to Solve the Exploration-Exploitation Dilemma of Design Space Exploration
The design and optimization of deep neural network accelerators should sufficiently consider numerous design parameters and physical constraints that...
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An adaptive human learning optimization with enhanced exploration–exploitation balance
Human Learning Optimization (HLO) is a simple yet efficient binary meta-heuristic, in which three learning operators, i.e. the random learning...
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Exploration and Exploitation of Unlabeled Data for Open-Set Semi-supervised Learning
In this paper, we address a complex but practical scenario in semi-supervised learning (SSL) named open-set SSL, where unlabeled data contain both...
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Adjusting Exploitation and Exploration Rates of Differential Evolution: A Novel Mutation Strategy
Differential evolution (DE) has attracted significant attention in recent years owing to its high performance in solving continuous problems. Up to...
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Reinforced exploitation and exploration grey wolf optimizer for numerical and real-world optimization problems
Grey Wolf Optimizer (GWO) has been proposed recently. As GWO has superior performance, it has been employed to solve various numerical and...
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Exploration and exploitation analysis for the sonar inspired optimization algorithm
In the recent years, extensive discussion takes place in literature, on the effectiveness of meta-heuristics, and especially Nature Inspired...
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Improved discrete salp swarm algorithm using exploration and exploitation techniques for feature selection in intrusion detection systems
The salp swarm algorithm (SSA) is a well-known optimization algorithm that is increasingly being utilized to solve many sorts of optimization...
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Collaboration exploitation and exploration: does a proactive search strategy matter?
Although one school of thought in the university-industry interactive literature is that universities learn from prior collaboration, we posit that...
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Deep Reinforcement Learning for Smart Restarts in Exploration-Only Exploitation-Only Hybrid Metaheuristics
Metaheuristic hybrids equipped with multiple restarts have shown promise in complex optimization problems. A critical challenge in this domain,...
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Disentangling Exploration and Exploitation in Deep Reinforcement Learning Using Contingency Awareness
This article investigates the efficiency of modelling contingency awareness in sparse reward environments for better exploration. We investigate this...
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Using Denoising Autoencoder Genetic Programming to Control Exploration and Exploitation in Search
Denoising Autoencoder Genetic Programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming (EDA-GP) algorithm...
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Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics
Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this...
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Optimizing Exploration-Exploitation Trade-off in Continuous Action Spaces via Q-ensemble
Ensemble-based reinforcement learning methods that combine multiple models of Q-function (i.e., value function) or policy have recently achieved...
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Incorporating Explanations to Balance the Exploration and Exploitation of Deep Reinforcement Learning
Discovering efficient exploration strategies is a central challenge in reinforcement learning (RL). Deep reinforcement learning (DRL) methods...
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Multimodal Labor Exploitation Detections for Taiwan Distant Water Fishing Industry
Taiwan plays a significant role in global seafood supply chains, accounting for approximately 10% of global tuna catches. The country is a...
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Improved Exploration Strategy for Q-Learning Based Multipath Routing in SDN Networks
Software-Defined Networking (SDN) is characterized by a high level of programmability and offers a rich set of capabilities for network management...
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Deep intrinsically motivated exploration in continuous control
In continuous control, exploration is often performed through undirected strategies in which parameters of the networks or selected actions are...
