Papers Evolutionary Algorithms
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Warm Starting of CMA-ES for Contextual Optimization Problems
Several practical applications of evolutionary computation possess objective functions that receive the design variables and externally given parameters. Such problems are termed contextual optimization problems. These p…
Evolutionary AlgorithmsAI-driven materials design: a mini-review
Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial-and-error and can be inefficient. Computational techniques, enhanced by modern artificial inte…
Evolutionary AlgorithmsReinforcement Learning (RL)Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization
Quality-Diversity has emerged as a powerful family of evolutionary algorithms that generate diverse populations of high-performing solutions by implementing local competition principles inspired by biological evolution. …
DiversityEvolutionary AlgorithmsMeta-LearningDominated Novelty Search: Rethinking Local Competition in Quality-Diversity
Quality-Diversity is a family of evolutionary algorithms that generate diverse, high-performing solutions through local competition principles inspired by natural evolution. While research has focused on improving specif…
DiversityEvolutionary AlgorithmsDegree is Important: On Evolving Homogeneous Boolean Functions
Boolean functions with good cryptographic properties like high nonlinearity and algebraic degree play an important in the security of stream and block ciphers. Such functions may be designed, for instance, by algebraic c…
Evolutionary AlgorithmsScaling Policy Gradient Quality-Diversity with Massive Parallelization via Behavioral Variations
Quality-Diversity optimization comprises a family of evolutionary algorithms aimed at generating a collection of diverse and high-performing solutions. MAP-Elites (ME), a notable example, is used effectively in fields li…
DiversityEvolutionary AlgorithmsGPUImproving Genetic Programming for Symbolic Regression with Equality Graphs
The search for symbolic regression models with genetic programming (GP) has a tendency of revisiting expressions in their original or equivalent forms. Repeatedly evaluating equivalent expressions is inefficient, as it d…
Evolutionary AlgorithmsregressionSymbolic RegressionStochastic Population Update Provably Needs An Archive in Evolutionary Multi-objective Optimization
Evolutionary algorithms (EAs) have been widely applied to multi-objective optimization, due to their nature of population-based search. Population update, a key component in multi-objective EAs (MOEAs), is usually perfor…
Evolutionary AlgorithmsA Benchmarking Environment for Worker Flexibility in Flexible Job Shop Scheduling Problems
In Production Scheduling, the Flexible Job Shop Scheduling Problem (FJSSP) aims to optimize a sequence of operations and assign each to an eligible machine with varying processing times. For integration of the workforce,…
BenchmarkingEvolutionary AlgorithmsJob Shop SchedulingSchedulingWhat if Eye...? Computationally Recreating Vision Evolution
Vision systems in nature show remarkable diversity, from simple light-sensitive patches to complex camera eyes with lenses. While natural selection has produced these eyes through countless mutations over millions of yea…
Evolutionary AlgorithmsEvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning
Evolutionary Reinforcement Learning (EvoRL) has emerged as a promising approach to overcoming the limitations of traditional reinforcement learning (RL) by integrating the Evolutionary Computation (EC) paradigm with RL. …
BenchmarkingEvolutionary AlgorithmsGPUreinforcement-learning+2Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization
Differential evolution (DE) algorithm is recognized as one of the most effective evolutionary algorithms, demonstrating remarkable efficacy in black-box optimization due to its derivative-free nature. Numerous enhancemen…
Evolutionary AlgorithmsMeta-LearningReinforcement Learning (RL)Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing
Coupling Large Language Models (LLMs) with Evolutionary Algorithms has recently shown significant promise as a technique to design new heuristics that outperform existing methods, particularly in the field of combinatori…
BenchmarkingEvolutionary AlgorithmsEvaluation and Efficiency Comparison of Evolutionary Algorithms for Service Placement Optimization in Fog Architectures
This study compares three evolutionary algorithms for the problem of fog service placement: weighted sum genetic algorithm (WSGA), non-dominated sorting genetic algorithm II (NSGA-II), and multiobjective evolutionary alg…
DiversityEvolutionary AlgorithmsEfficient Deep Learning-based Forward Solvers for Brain Tumor Growth Models
Glioblastoma, a highly aggressive brain tumor, poses major challenges due to its poor prognosis and high morbidity rates. Partial differential equation-based models offer promising potential to enhance therapeutic outcom…
Deep LearningEvolutionary AlgorithmsPrognosisA RankNet-Inspired Surrogate-Assisted Hybrid Metaheuristic for Expensive Coverage Optimization
Coverage optimization generally involves deploying a set of facilities to best satisfy the demands of specified points, with broad applications in fields such as location science and sensor networks. Recent applications …
Evolutionary AlgorithmsMultiple-gain Estimation for Running Time of Evolutionary Combinatorial Optimization
The running-time analysis of evolutionary combinatorial optimization is a fundamental topic in evolutionary computation. Its current research mainly focuses on specific algorithms for simplified problems due to the chall…
Combinatorial OptimizationEvolutionary AlgorithmsPareto Optimization with Robust Evaluation for Noisy Subset Selection
Subset selection is a fundamental problem in combinatorial optimization, which has a wide range of applications such as influence maximization and sparse regression. The goal is to select a subset of limited size from a …
Combinatorial OptimizationEvolutionary AlgorithmsregressionEvolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities
Deep learning models trained on finite data lack a complete understanding of the physical world. On the other hand, physics-informed neural networks (PINNs) are infused with such knowledge through the incorporation of ma…
Evolutionary AlgorithmsModel OptimizationSurveyRuntime Analysis of Evolutionary Algorithms for Multiparty Multiobjective Optimization
In scenarios where multiple decision-makers operate within a common decision space, each focusing on their own multi-objective optimization problem (e.g., bargaining games), the problem can be modeled as a multi-party mu…
Evolutionary AlgorithmsMultiobjective Optimization