A Novel Greedy Genetic Algorithm to Solve Combinatorial Optimization Problem

Basmassi, Mohamed Amine; Benameur, Lamia; Chentoufi, Jihane Alami

doi:10.5194/isprs-archives-xliv-4-w3-2020-117-2020

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Greedy algorithm is a straightforward and fast method since it only selects solutions that satisfy greedy requirements. Numerous papers combined greedy with their hybrid algorithm like [13], [14] in the aim that the greedy solution will assist the hybrid algorithm in getting closer to the nearest solution. To address these concerns, this article offers a new greedy technique for array generating limitations based on the HC algorithm, named hybrid greedy hill climbing algorithm (HGHC).…”

Section: Introductionmentioning

confidence: 99%

Innovations in t-way test creation based on a hybrid hill climbing-greedy algorithm

Fadhil

Abdullah

Younis

2023

IJ-AI

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<p>In combinatorial testing development, the fabrication of covering arrays is the key challenge by the multiple aspects that influence it. A wide range of combinatorial problems can be solved using metaheuristic and greedy techniques. Combining the greedy technique utilizing a metaheuristic search technique like hill climbing (HC), can produce feasible results for combinatorial tests. Methods based on metaheuristics are used to deal with tuples that may be left after redundancy using greedy strategies; then the result utilization is assured to be near-optimal using a metaheuristic algorithm. As a result, the use of both greedy and HC algorithms in a single test generation system is a good candidate if constructed correctly. This study presents a hybrid greedy hill climbing algorithm (HGHC) that ensures both effectiveness and near-optimal results for generating a small number of test data. To make certain that the suggested HGHC outperforms the most used techniques in terms of test size. It is compared to others in order to determine its effectiveness. In contrast to recent practices utilized for the production of covering arrays (CAs) and mixed covering arrays (MCAs), this hybrid strategy is superior since allowing it to provide the utmost outcome while reducing the size and limit the loss of unique pairings in the CA/MCA generation.</p>

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Section: Introductionmentioning

confidence: 99%

Innovations in t-way test creation based on a hybrid hill climbing-greedy algorithm

Fadhil

Abdullah

Younis

2023

IJ-AI

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“…Thanks to the interpretable nature of symbolic regression based models, they have been widely used in industrial empirical modeling [14,34,27,31]. Due to the high dimensional space of mathematical expressions that can describe a specific dataset, symbolic regression is a complex combinatorial problem [5,3]. Therefore, the traditional symbolic regression approaches have been mostly based on genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Symbolic regression for scientific discovery: an application to wind speed forecasting

Abdellaoui¹,

Mehrkanoon²

2021

Preprint

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Symbolic regression corresponds to an ensemble of techniques that allow to uncover an analytical equation from data. Through a closed form formula, these techniques provide great advantages such as potential scientific discovery of new laws, as well as explainability, feature engineering as well as fast inference. Similarly, deep learning based techniques has shown an extraordinary ability of modeling complex patterns. The present paper aims at applying a recent end-to-end symbolic regression technique, i.e. the equation learner (EQL), to get an analytical equation for wind speed forecasting. We show that it is possible to derive an analytical equation that can achieve reasonable accuracy for short term horizons predictions only using few number of features.

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Solving Graph Coloring Problem Using Ant Colony Optimization, Simulated Annealing and Quantum Annealing—A Comparative Study

Kole¹,

Pal

2022

Intelligence Enabled Research

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A Novel Greedy Genetic Algorithm to Solve Combinatorial Optimization Problem

Cited by 5 publications

References 13 publications

Innovations in t-way test creation based on a hybrid hill climbing-greedy algorithm

Innovations in t-way test creation based on a hybrid hill climbing-greedy algorithm

Symbolic regression for scientific discovery: an application to wind speed forecasting

Solving Graph Coloring Problem Using Ant Colony Optimization, Simulated Annealing and Quantum Annealing—A Comparative Study

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