Results 21 to 30 of about 332,730 (283)
Combinatorial Dominance Guarantees for Heuristic Algorithms [PDF]
Discrete Mathematics & Theoretical Computer Science, 2007 An $f(n)$ $\textit{dominance bound}$ on a heuristic for some problem is a guarantee that the heuristic always returns a solution not worse than at least $f(n)$ solutions.
Daniel Berend +2 more
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A Hybrid Optimization Framework with Dynamic Transition Scheme for Large-Scale Portfolio Management
Algorithms, 2022 Meta-heuristic algorithms have successfully solved many real-world problems in recent years. Inspired by different natural phenomena, the algorithms with special search mechanisms can be good at tackling certain problems.
Zhenglong Li, Vincent Tam
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Cost-Aware and Energy-Efficient Task Scheduling Based on Grey Wolf Optimizer [PDF]
Journal of Mahani Mathematical Research, 2023 One of the principal challenges in the cloud is the task scheduling problem. Appropriate task scheduling algorithms are needed to achieve goals such as load balancing, minimum cost, minimum energy consumption, etc.
Reyhane Ghafari, Najme Mansouri
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Iterated-greedy-based algorithms with beam search initialization for the permutation flowshop to minimize total tardiness [PDF]
, 2018 The permutation flow shop scheduling problem is one of the most studied operations research related problems. Literally, hundreds of exact and approximate algorithms have been proposed to optimise several objective functions. In this paper we address the
Fernández-Viagas Escudero, Víctor +2 more
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Review of Quantum-inspired Metaheuristic Algorithms and Its Applications [PDF]
Jisuanji kexueThe quantum meta heuristic algorithm is developed by applying quantum computing to the meta-heuristic algorithm.This kind of algorithm is good at solving combinatorial and numerical optimization problems,and has the characteristics of acce-lerated ...
RUAN Ning, LI Chun, MA Haoyue, JIA Yi, LI Tao
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IEEE Access, 2023 Heuristic search algorithms are informed search strategies that use heuristics to estimate the minimal cost of the path from the current state to the goal. Using this additional knowledge, this type of algorithms can distinguish non-goal states, and then
Ouardi Amine, Mestari Mohammed
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Population Dynamics in Genetic Programming for Dynamic Symbolic Regression
Applied SciencesThis paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications.
Philipp Fleck +2 more
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Bias and Variance Analysis of Contemporary Symbolic Regression Methods
Applied SciencesSymbolic regression is commonly used in domains where both high accuracy and interpretability of models is required. While symbolic regression is capable to produce highly accurate models, small changes in the training data might cause highly dissimilar ...
Lukas Kammerer +2 more
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Scalable Kernelization for Maximum Independent Sets [PDF]
, 2019 The most efficient algorithms for finding maximum independent sets in both theory and practice use reduction rules to obtain a much smaller problem instance called a kernel.
Hespe, Demian +2 more
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A Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem
Entropy, 2021 In this paper, we present a hybrid genetic-hierarchical algorithm for the solution of the quadratic assignment problem. The main distinguishing aspect of the proposed algorithm is that this is an innovative hybrid genetic algorithm with the original ...
Alfonsas Misevičius, Dovilė Verenė
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