Papers Evolutionary Algorithms
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Information retrieval system using evolutionary algorithms
When it comes to web search, information retrieval (IR) represents a critical technique as web pages have been increasingly growing. However, web users face major problems; unrelated user query retrieved documents (i.e.,…
Evolutionary AlgorithmsInformation RetrievalRetrievalERL-Re$^2$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation
Deep Reinforcement Learning (Deep RL) and Evolutionary Algorithms (EA) are two major paradigms of policy optimization with distinct learning principles, i.e., gradient-based v.s. gradient-free. An appealing research dire…
continuous-controlContinuous ControlDeep Reinforcement LearningEvolutionary Algorithms+2Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture Search
Population-based search has recently emerged as a possible alternative to Reinforcement Learning (RL) for black-box neural architecture search (NAS). It performs well in practice even though it is not theoretically well …
Evolutionary AlgorithmsNeural Architecture SearchReinforcement Learning (RL)Sub-network Multi-objective Evolutionary Algorithm for Filter Pruning
Filter pruning is a common method to achieve model compression and acceleration in deep neural networks (DNNs).Some research regarded filter pruning as a combinatorial optimization problem and thus used evolutionary algo…
Combinatorial OptimizationEvolutionary AlgorithmsModel CompressionA Secure Federated Data-Driven Evolutionary Multi-objective Optimization Algorithm
Data-driven evolutionary algorithms usually aim to exploit the information behind a limited amount of data to perform optimization, which have proved to be successful in solving many complex real-world optimization probl…
Evolutionary AlgorithmsE2R: a Hierarchical-Learning inspired Novelty-Search method to generate diverse repertoires of grasping trajectories
Robotics grasping refers to the task of making a robotic system pick an object by applying forces and torques on its surface. Despite the recent advances in data-driven approaches, grasping remains an unsolved problem. M…
DiversityEvolutionary AlgorithmsAccelerating the Evolutionary Algorithms by Gaussian Process Regression with $ε$-greedy acquisition function
In this paper, we propose a novel method to estimate the elite individual to accelerate the convergence of optimization. Inspired by the Bayesian Optimization Algorithm (BOA), the Gaussian Process Regression (GPR) is app…
Bayesian OptimizationEvolutionary AlgorithmsGPRregressionMulti-objective optimization via equivariant deep hypervolume approximation
Optimizing multiple competing objectives is a common problem across science and industry. The inherent inextricable trade-off between those objectives leads one to the task of exploring their Pareto front. A meaningful q…
Bayesian OptimizationEvolutionary AlgorithmsEvolution is Still Good: Theoretical Analysis of Evolutionary Algorithms on General Cover Problems
Theoretical studies on evolutionary algorithms have developed vigorously in recent years. Many such algorithms have theoretical guarantees in both running time and approximation ratio. Some approximation mechanism seems …
Evolutionary AlgorithmsNeuroEvo: A Cloud-based Platform for Automated Design and Training of Neural Networks using Evolutionary and Particle Swarm Algorithms
Evolutionary algorithms (EAs) provide unique advantages for optimizing neural networks in complex search spaces. This paper introduces a new web platform, NeuroEvo (neuroevo.io), that allows users to interactively design…
Evolutionary AlgorithmsGPUComparative Study of Q-Learning and NeuroEvolution of Augmenting Topologies for Self Driving Agents
Autonomous driving vehicles have been of keen interest ever since automation of various tasks started. Humans are prone to exhaustion and have a slow response time on the road, and on top of that driving is already quite…
Autonomous DrivingEvolutionary AlgorithmsQ-LearningEvolutionary Deep Reinforcement Learning Using Elite Buffer: A Novel Approach Towards DRL Combined with EA in Continuous Control Tasks
Despite the numerous applications and success of deep reinforcement learning in many control tasks, it still suffers from many crucial problems and limitations, including temporal credit assignment with sparse reward, ab…
continuous-controlContinuous ControlDeep Reinforcement LearningEvolutionary Algorithms+3Diversity and Novelty MasterPrints: Generating Multiple DeepMasterPrints for Increased User Coverage
This work expands on previous advancements in genetic fingerprint spoofing via the DeepMasterPrints and introduces Diversity and Novelty MasterPrints. This system uses quality diversity evolutionary algorithms to generat…
DiversityEvolutionary AlgorithmsFast Re-Optimization of LeadingOnes with Frequent Changes
In real-world optimization scenarios, the problem instance that we are asked to solve may change during the optimization process, e.g., when new information becomes available or when the environmental conditions change. …
Evolutionary AlgorithmsWhat to Prune and What Not to Prune at Initialization
Post-training dropout based approaches achieve high sparsity and are well established means of deciphering problems relating to computational cost and overfitting in Neural Network architectures. Contrastingly, pruning a…
Evolutionary AlgorithmsUnderstandable Controller Extraction from Video Observations of Swarms
Swarm behavior emerges from the local interaction of agents and their environment often encoded as simple rules. Extracting the rules by watching a video of the overall swarm behavior could help us study and control swar…
Evolutionary AlgorithmsAccelerating differential evolution algorithm with Gaussian sampling based on estimating the convergence points
In this paper, we propose a simple strategy for estimating the convergence point approximately by averaging the elite sub-population. Based on this idea, we derive two methods, which are ordinary averaging strategy, and …
Evolutionary AlgorithmsEvolving the MCTS Upper Confidence Bounds for Trees Using a Semantic-inspired Evolutionary Algorithm in the Game of Carcassonne
Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The success of MCTS depends heavily on how the tree is built and the selection process plays a fundamental role in this. One…
Evolutionary AlgorithmsSemantic SimilaritySemantic Textual SimilarityDifferential evolution variants for Searching D- and A-optimal designs
Optimal experimental design is an essential subfield of statistics that maximizes the chances of experimental success. The D- and A-optimal design is a very challenging problem in the field of optimal design, namely mini…
Evolutionary AlgorithmsExperimental DesignEnhanced Opposition Differential Evolution Algorithm for Multimodal Optimization
Most of the real-world problems are multimodal in nature that consists of multiple optimum values. Multimodal optimization is defined as the process of finding multiple global and local optima (as opposed to a single sol…
Evolutionary Algorithms