A review on genetic algorithm: past, present, and future

Katoch, Sourabh; Chauhan, Sumit Singh; Kumar, Vijay

doi:10.1007/s11042-020-10139-6

Cited by 2,708 publications

(1,149 citation statements)

References 194 publications

(199 reference statements)

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“…Inspired by natural selection, it falls into the category of population based search algorithms. For a detailed discussion of the method and its several modifications we refer the interested reader to [42][43][44]. Here, we briefly present the simplest genetic algorithm, which is composed by three fundamental steps: selection, reproduction, and mutation.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

Demo

Tezzele

Mola

et al. 2021

JMSE

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In the field of parametric partial differential equations, shape optimization represents a challenging problem due to the required computational resources. In this contribution, a data-driven framework involving multiple reduction techniques is proposed to reduce such computational burden. Proper orthogonal decomposition (POD) and active subspace genetic algorithm (ASGA) are applied for a dimensional reduction of the original (high fidelity) model and for an efficient genetic optimization based on active subspace property. The parameterization of the shape is applied directly to the computational mesh, propagating the generic deformation map applied to the surface (of the object to optimize) to the mesh nodes using a radial basis function (RBF) interpolation. Thus, topology and quality of the original mesh are preserved, enabling application of POD-based reduced order modeling techniques, and avoiding the necessity of additional meshing steps. Model order reduction is performed coupling POD and Gaussian process regression (GPR) in a data-driven fashion. The framework is validated on a benchmark ship.

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Section: Genetic Algorithmmentioning

confidence: 99%

Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

Demo

Tezzele

Mola

et al. 2021

JMSE

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“…Often, many ML algorithms are applied to the same problem, allowing prediction accuracy of the same target trained on identical data to be compared across multiple algorithms, as seen by Han et al in Table 4 [ 130 ]. In addition to ANNs, other ML methods surveyed in this review include genetic algorithms (GA) [ 131 ], multiple linear regression (MLR), logistic regression (LR) [ 132 ], decision tree (DT) [ 133 ], random forest (RF) [ 134 ], k-nearest neighbors (kNN) [ 135 ], Naïve Bayes (NB) [ 136 ], and light gradient boosting machine (LGBM) [ 137 ].…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Molecular Simulation and Statistical Learning Methods toward Predicting Drug–Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design

Walden¹,

Bundey²,

Jagarapu³

et al. 2021

Molecules

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Amorphous solid dispersions (ASDs) have emerged as widespread formulations for drug delivery of poorly soluble active pharmaceutical ingredients (APIs). Predicting the API solubility with various carriers in the API–carrier mixture and the principal API–carrier non-bonding interactions are critical factors for rational drug development and formulation decisions. Experimental determination of these interactions, solubility, and dissolution mechanisms is time-consuming, costly, and reliant on trial and error. To that end, molecular modeling has been applied to simulate ASD properties and mechanisms. Quantum mechanical methods elucidate the strength of API–carrier non-bonding interactions, while molecular dynamics simulations model and predict ASD physical stability, solubility, and dissolution mechanisms. Statistical learning models have been recently applied to the prediction of a variety of drug formulation properties and show immense potential for continued application in the understanding and prediction of ASD solubility. Continued theoretical progress and computational applications will accelerate lead compound development before clinical trials. This article reviews in silico research for the rational formulation design of low-solubility drugs. Pertinent theoretical groundwork is presented, modeling applications and limitations are discussed, and the prospective clinical benefits of accelerated ASD formulation are envisioned.

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“…Many researchers proposed methods for solving the vehicle routing problem to find out the minimizing cost. Using the exact method (genetic algorithm (GA)) [Katoch et al, 2020], that has been widely used in various real-life applications, we used a trial-and-error experiment to compare the performances of the well-known in the literature and those of different kinds of optimization algorithms consisting of mixed intergenerational problems (MIP). The representation of chromosomes is closely associated with reallife problems.…”

Section: Trial and Error Of The Algorithm For Solving Relocation Bikementioning

confidence: 99%

Bike-sharing rebalancing problem

Boonjubut

Hasegawa

Kongwat

2021

JGTR

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The number of bike-sharing services has rapidly increased in many cities worldwide. One of the main challenges of the bike-sharing system operation costs is allocating enough bikes and parking space. This paper presents a model for solving the bike-sharing relocation problem. The artificial bee colony (ABC) algorithm is an efficient approach, but it is still insufficient for the selection strategy. ABC has been adopted in various problems to improve the performance of various systems. This research proposed a modified ABC algorithm in a neighbor solution to enhance the solution performance, namely guided local search (GLS), to apply to the design route for transportation while truck relocation bikes each station in the bike-sharing system. Computational experiments were performed to find out the best modeling solution in the case. The implementations were experimental for the same data instances, which made it possible to compare the performance algorithms so as to solve the bike-sharing relocation problem of the pickup and drop off. The results showed that the GLS-ABC method can be a better solution than the original one. The statistically significant p-value of the mean objective value of the different algorithms was smaller than 0.05. Thus, the impact of minimizing the route tour cost in solving the bike-sharing relocation problem.

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A review on genetic algorithm: past, present, and future

Cited by 2,708 publications

References 194 publications

Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

Molecular Simulation and Statistical Learning Methods toward Predicting Drug–Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design

Bike-sharing rebalancing problem

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