Search Results for author:
Found 9 papers, 2 papers with code
Leveraging Large Language Model to Generate a Novel Metaheuristic Algorithm with CRISPE Framework
In this paper, we borrow the large language model (LLM) ChatGPT-3. 5 to automatically and quickly design a new metaheuristic algorithm (MA) with only a small amount of input.
Efficient Multiplayer Battle Game Optimizer for Adversarial Robust Neural Architecture Search
This paper introduces a novel metaheuristic algorithm, known as the efficient multiplayer battle game optimizer (EMBGO), specifically designed for addressing complex numerical optimization tasks.
Multiplayer Battle Game-Inspired Optimizer for Complex Optimization Problems
Various popular multiplayer battle royale games share a lot of common elements.
Accelerating the Evolutionary Algorithms by Gaussian Process Regression with $ε$-greedy acquisition function
Based on this hypothesis, in each generation of optimization, we replace the worst individual in Evolutionary Algorithms (EAs) with the elite individual to participate in the evolution process.
Accelerating the Genetic Algorithm for Large-scale Traveling Salesman Problems by Cooperative Coevolutionary Pointer Network with Reinforcement Learning
In this paper, we propose a two-stage optimization strategy for solving the Large-scale Traveling Salesman Problems (LSTSPs) named CCPNRL-GA. First, we hypothesize that the participation of a well-performed individual as an elite can accelerate the convergence of optimization.
Cooperative coevolutionary Modified Differential Evolution with Distance-based Selection for Large-Scale Optimization Problems in noisy environments through an automatic Random Grouping
Simulation experiments and mathematical analysis show that aRG can detect the interactions between variables without the fitness landscape knowledge, and the sub-problems decomposed by aRG have smaller scales, which is easier for EAs to optimize.
Accelerating 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.
Cooperative coevolutionary hybrid NSGA-II with Linkage Measurement Minimization for Large-scale Multi-objective optimization
In the variable grouping stage, according to our previous research, we treat the variable grouping problem as a combinatorial optimization problem, and the linkage measurement function is designed based on linkage identification by the nonlinearity check on real code (LINC-R).
Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction
Rich user behavior information is of great importance for capturing and understanding user interest in click-through rate (CTR) prediction.
