Papers Evolutionary Algorithms
“Evolutionary Algorithms” 태그가 달린 논문 1,107편 · 필터 해제
An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary Algorithms
Hyperparameter optimization is a crucial problem in Evolutionary Computation. In fact, the values of the hyperparameters directly impact the trajectory taken by the optimization process, and their choice requires extensi…
Evolutionary AlgorithmsHyperparameter OptimizationHow quantum and evolutionary algorithms can help each other: two examples
We investigate the potential of bio-inspired evolutionary algorithms for designing quantum circuits with specific goals, focusing on two particular tasks. The first one is motivated by the ideas of Artificial Life that a…
Artificial LifeEvolutionary AlgorithmsQuantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey
The clever hybridization of quantum computing concepts and evolutionary algorithms (EAs) resulted in a new field called quantum-inspired evolutionary algorithms (QIEAs). Unlike traditional EAs, QIEAs employ quantum bits …
DiversityEvolutionary AlgorithmsOvercoming Binary Adversarial Optimisation with Competitive Coevolution
Co-evolutionary algorithms (CoEAs), which pair candidate designs with test cases, are frequently used in adversarial optimisation, particularly for binary test-based problems where designs and tests yield binary outcomes…
Evolutionary AlgorithmsGrammar-based Game Description Generation using Large Language Models
Game Description Language (GDL) provides a standardized way to express diverse games in a machine-readable format, enabling automated game simulation, and evaluation. While previous research has explored game description…
Evolutionary AlgorithmsGame DesignIn-Context LearningvalidEnhancing Mass Customization Manufacturing: Multiobjective Metaheuristic Algorithms for flow shop Production in Smart Industry
The current landscape of massive production industries is undergoing significant transformations driven by emerging customer trends and new smart manufacturing technologies. One such change is the imperative to implement…
Evolutionary AlgorithmsOptimization of breeding program design through stochastic simulation with evolutionary algorithms
The effective planning and allocation of resources in modern breeding programs is a complex task. Breeding program design and operational management have a major impact on the success of a breeding program and changing p…
Distributed ComputingEvolutionary AlgorithmsregressionHyper-Heuristics Can Profit From Global Variation Operators
In recent work, Lissovoi, Oliveto, and Warwicker (Artificial Intelligence (2023)) proved that the Move Acceptance Hyper-Heuristic (MAHH) leaves the local optimum of the multimodal CLIFF benchmark with remarkable efficien…
Evolutionary AlgorithmsCCSRP: Robust Pruning of Spiking Neural Networks through Cooperative Coevolution
Spiking neural networks (SNNs) have shown promise in various dynamic visual tasks, yet those ready for practical deployment often lack the compactness and robustness essential in resource-limited and safety-critical sett…
Evolutionary AlgorithmsNetwork PruningPareto local search for a multi-objective demand response problem in residential areas with heat pumps and electric vehicles
In future energy systems characterized by significant shares of fluctuating renewable energy sources, there is a need for a fundamental change in electricity consumption. The energy system requires the ability to adapt t…
Evolutionary AlgorithmsHeuristic Searchreinforcement-learningReinforcement LearningDeep Learning-Based Operators for Evolutionary Algorithms
We present two novel domain-independent genetic operators that harness the capabilities of deep learning: a crossover operator for genetic algorithms and a mutation operator for genetic programming. Deep Neural Crossover…
DecoderDeep LearningDeep Reinforcement LearningEvolutionary Algorithms+1Sliding Window Bi-Objective Evolutionary Algorithms for Optimizing Chance-Constrained Monotone Submodular Functions
Variants of the GSEMO algorithm using multi-objective formulations have been successfully analyzed and applied to optimize chance-constrained submodular functions. However, due to the effect of the increasing population …
Evolutionary AlgorithmsLocal Optima in Diversity Optimization: Non-trivial Offspring Population is Essential
The main goal of diversity optimization is to find a diverse set of solutions which satisfy some lower bound on their fitness. Evolutionary algorithms (EAs) are often used for such tasks, since they are naturally designe…
DiversityEvolutionary AlgorithmsRoboMorph: Evolving Robot Morphology using Large Language Models
We introduce RoboMorph, an automated approach for generating and optimizing modular robot designs using large language models (LLMs) and evolutionary algorithms. In this framework, we represent each robot design as a gra…
Evolutionary AlgorithmsNavigateTail Bounds on the Runtime of Categorical Compact Genetic Algorithm
The majority of theoretical analyses of evolutionary algorithms in the discrete domain focus on binary optimization algorithms, even though black-box optimization on the categorical domain has a lot of practical applicat…
Evolutionary AlgorithmsA Two-stage Evolutionary Framework For Multi-objective Optimization
In the field of evolutionary multi-objective optimization, the approximation of the Pareto front (PF) is achieved by utilizing a collection of representative candidate solutions that exhibit desirable convergence and div…
DiversityEvolutionary AlgorithmsDecomposition of Difficulties in Complex Optimization Problems Using a Bilevel Approach
Practical optimization problems may contain different kinds of difficulties that are often not tractable if one relies on a particular optimization method. Different optimization approaches offer different strengths that…
Bilevel OptimizationEvolutionary AlgorithmsLeveraging Latent Evolutionary Optimization for Targeted Molecule Generation
Lead optimization is a pivotal task in the drug design phase within the drug discovery lifecycle. The primary objective is to refine the lead compound to meet specific molecular properties for progression to the subseque…
Drug DesignDrug DiscoveryEvolutionary AlgorithmsMulti-task multi-constraint differential evolution with elite-guided knowledge transfer for coal mine integrated energy system dispatching
The dispatch optimization of coal mine integrated energy system is challenging due to high dimensionality, strong coupling constraints, and multiobjective. Existing constrained multiobjective evolutionary algorithms stru…
DiversityEvolutionary AlgorithmsMultiobjective OptimizationTransfer LearningSurrogate-assisted evolutionary framework with an ensemble of teaching-learning and differential evolution for expensive optimization
Surrogate-Assisted Evolutionary Algorithms (SAEAs) integrate Evolutionary Algorithms (EAs) with surrogate models to reduce the actual number of expensive function evaluations and have been widely used in solving Expensiv…
DiversityEvolutionary Algorithms