An Introduction to Genetic Algorithms for Scientists and Engineers

Coley, David

doi:10.1142/3904

Cited by 757 publications

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“…They also perform a robust and parametric search, i.e., the convergence is guaranteed, provided that the choice of algorithm parameters is appropriate [31 ]. Since the GA's are efficient in cases where the search is long and complex, and when the solution domain is extensive, they are particularly suitable for obtaining the coefficients of the relativistic hydrogenic screening model.…”

Section: The Fit Of the Screening Constants Using A Genetic Algorithmmentioning

confidence: 99%

A new set of relativistic screening constants for the screened hydrogenic model

Mendoza¹,

Rubiano²,

Gil³

et al. 2011

High Energy Density Physics

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Keywords:Screening constants Screened hydrogenic model A new Relativistic Screened Hydrogenic Model has been developed to calculate atomic data needed to compute the optical and thermodynamic properties of high energy density plasmas. The model is based on a new set of universal screening constants, including nij-splitting that has been obtained by fitting to a large database of ionization potentials and excitation energies. This database was built with energies compiled from the National Institute of Standards and Technology (NIST) database of experimental atomic energy levels, and energies calculated with the Flexible Atomic Code (FAC). The screening constants have been computed up to the 5p¡/2 subshell using a Genetic Algorithm technique with an objective function designed to minimize both the relative error and the maximum error. To select the best set of screening constants some additional physical criteria has been applied, which are based on the reproduction of the filling order of the shells and on obtaining the best ground state configuration. A statistical error analysis has been performed to test the model, which indicated that approximately 88% of the data lie within a ±10% error interval. We validate the model by comparing the results with ionization energies, transition energies, and wave functions computed using sophisticated self-consistent codes and experimental data.

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Section: The Fit Of the Screening Constants Using A Genetic Algorithmmentioning

confidence: 99%

A new set of relativistic screening constants for the screened hydrogenic model

Mendoza¹,

Rubiano²,

Gil³

et al. 2011

High Energy Density Physics

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“…1.  Fuzzification Module-It maps the system inputs [6], which are crisp numbers, into fuzzy sets. Then, it determines the degree to which these inputs belong to each of the appropriate fuzzy sets.…”

Section: Fuzzy Controlmentioning

confidence: 99%

“…WSNs have gained an increasing interest in many fields such as medicine, agriculture, environmental monitoring, tracking, etc [4,5,6]. They are composed of limited-power sensors that have limited transmission range, processing and storage capabilities, called sensor nodes [7,8].…”

Section: Introductionmentioning

confidence: 99%

MUCH: Multi-Criteria Cluster Head Delegation Based on Fuzzy Logic

Ghosn¹,

Ghaddar²

2017

International Journal of Advanced Research in Computer and Comm

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WSNs are composed of limited-power sensors. Replacing or recharging sensors, especially when deployed in places where human intervention is risky, is an important challenge. Therefore, and since communication is the major energy consuming operation [1], ensuring an energy efficient usage is a major concern. Clustering is one of the techniques used for this purpose [2]. It decreases the number of sensors which take part in transmission thereby assist in power consumption [3]. The sensors are organized into clusters. Each Cluster has a Cluster Head (CH) to which only other cluster members send data. CHs gather information and send it to a base station for processing. We propose an method for CH selection based on multiple criteria (distance to base station, distance to cluster members, energy cost and number of rounds) using fuzzy logic. Simulation results show that MUCH decreases the energy consumption comparing to LEACH thus increases the network lifetime.

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“…The value of each demand is then generated in the appropriate range according to a discrete uniform distribution. Twenty instances are generated for each 10, 20 and 50 customers with demand range (1,5), (6,10), (11,15).…”

Section: Datamentioning

confidence: 99%

“…To improve pure GA performance, some algorithms are combined with the simple GA, yielding a hybrid algorithm. The statement about GA hybridization is noted by [6] that hybrid algorithms, which combine a GA with more traditional algorithms, have been hinted as a highly powerful combination for solving practical problem, also by [11] that it is well known that a standard GA must be hybridized with another search procedure to be able to compete with metaheuristics like TS. The approach is also inspired by the emerging interest in hybrid metaheuristics that has risen considerably among researchers in combinatorial optimization.…”

Section: Introductionmentioning

confidence: 99%

Solving the Vehicle Routing Problem with Stochastic Demands via Hybrid Genetic Algorithm-Tabu Search

Ismail¹,

Irhamah²

2008

J. of Mathematics and Statistics

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This study considers a version of the stochastic vehicle routing problem where customer demands are random variables with known probability distribution. A new scheme based on a hybrid GA and Tabu Search heuristic is proposed for this problem under a priori approach with preventive restocking. The relative performance of the proposed HGATS is compared to each GA and TS alone, on a set of randomly generated problems following some discrete probability distributions. The problem data are inspired by real case of VRPSD in waste collection. Results from the experiment show the advantages of the proposed algorithm that are its robustness and better solution qualities resulted.

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An Introduction to Genetic Algorithms for Scientists and Engineers

Abstract: , ekctronic or mechanical, including phofocopying, recording or any information storage an& retrieval system now known or to be invented, without writfen ~e~i s s i o n~o m the Publisher.

Cited by 757 publications

References 0 publications

A new set of relativistic screening constants for the screened hydrogenic model

A new set of relativistic screening constants for the screened hydrogenic model

MUCH: Multi-Criteria Cluster Head Delegation Based on Fuzzy Logic

Solving the Vehicle Routing Problem with Stochastic Demands via Hybrid Genetic Algorithm-Tabu Search

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