Practical Genetic Algorithms

Haupt, Randy L.; Haupt, Sue Ellen

doi:10.1002/0471671746

Cited by 2,647 publications

(2,586 citation statements)

References 68 publications

(57 reference statements)

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“…A set of 15 random WDAG graphs consists of 100 to 200 tasks and 4 to 9 machines are generated randomly as shown in table (3). The matrix ECT is generated by random and all elements of matrix R are set to one.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…Therefore, they appear to be appropriate for solving problems [3]. There are more heuristic methods for solving the static task scheduling, some of which are: Opportunistic Load Balancing (OLB) [4], Minimum Execution Time (MET) [4], Minimum Completion Time (MCT) [4], Genetic Algorithms (GAs) [5][6][7][8], Simulated Annealing [9], Tabu Search [7].…”

Section: Introductionmentioning

confidence: 99%

“…There are many researches under the topics of solving the static task scheduling using GAs in the multiprocessor systems [3,8,10,11,12] and the distributed systems [3,5,6,13,14]. In this paper, a novel GA is presented which has a good ability to solve the above problem using the simulated annealing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Genetic Algorithm for Static Task Scheduling in Distributed Systems

Rahmani¹,

Rezvani²

2009

IJCTE

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Abstract-The static task scheduling problem in distributed systems is very important because of optimal usage of available machines and accepted computation time for scheduling algorithm. Solving this problem using the dynamic programming and the back tracking needs much more time. Therefore, there are more attempts to solve it using the heuristic methods. In this paper, a new genetic algorithm, named TDGASA, is presented which its running time depends on the number of tasks in the scheduling problem. Then, the computation time of TDGASA to find a sub-optimal schedule is improved by Simulated Annealing (SA). The results show that the computation time of the proposed algorithm decreases compared to an existing GA-based algorithm, although, the completion time of the final scheduled task in the system decreases a little.

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Section: Simulations and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Genetic Algorithm for Static Task Scheduling in Distributed Systems

Rahmani¹,

Rezvani²

2009

IJCTE

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“…This probability is calculated by a problem-independent approach called 'rank weighting' (Haupt and Haupt, 2004), originally used in the genetic algorithm (Holland, 1992). First, the solutions must be ranked by fitness, as we did in the 'Initialisation Step'.…”

Section: Ifmentioning

confidence: 99%

A new Metropolis optimisation method, the cross-section particle collision algorithm: some preliminary results

Sacco

Rios-Coelho

2016

IJNEST

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Abstract:In this work, we introduce a new Metropolis algorithm, which is an enhancement of the recent Particle Collision Algorithm (PCA), loosely inspired by the neutron interactions in a reactor. This novel method is called the CrossSection Particle Collision Algorithm (CSPCA), as it incorporates the concept of cross-section from Reactor Physics, in the sense that points in the search space and their respective fitness-function values are analogous to the neutron cross-sections which are used to express the likelihood of interaction between an incident neutron and a target nucleus. CSPCA is compared against the original PCA and two state-of-the-art metaheuristics, differential evolution and big bang-big crunch. These methods are applied to the turbine balancing problem, which is an NP-hard (i.e. non-deterministic polynomial-time hard) combinatorial optimisation problem that can be used to assess the potential of an algorithm to be applied to Fuel Management Optimisation (FMO). CSPCA performs better than its opponents, showing potential to be used not only in FMO, but also in other nuclear science and engineering optimisation problems.

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“…The Genetic Algorithm (GA), based on principles of genetics and natural selection, is an optimization and search method [10].Genetic algorithm starts working randomly or an existing, initial population. Each element of the population is called chromosome.…”

Section: B Genetic Algorithmmentioning

confidence: 99%

Rules Optimization Based Fuzzy Model for Predicting Distance Education Students’ Grades

Yıldız¹,

Bal²,

Gülseçen³

et al. 2014

IJIET

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Abstract-Distance education has been rapidly becoming widespread all around the world. This rapid growth brings along both success and failure. One of the causes of failure in distance education is the lack of student observation, which is a part of traditional education. We can overcome this deficit through an analysis of logs kept in the learning management system. These logs make it possible to estimate academic performance of a student attending to distance education. The instructor having such information can take precautions in order to prevent failure. In this study, a mathematical model has been formed by using Fuzzy Logic. In order to enhance the success of model, fuzzy logic rules have been optimized by using genetic algorithm. Three different variableshave been introduced to the proposed model. These are recency, stating the number of days that pass from the date of registering course contents until the date of student's admission to the system; frequency, stating the frequency of logging on to the system and monetary, stating the period of time spent online on the system. End of term scores of students have been estimated on the basis of the first 6-week data and then the results have been verified by the real scores.

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Practical Genetic Algorithms

Cited by 2,647 publications

References 68 publications

A Novel Genetic Algorithm for Static Task Scheduling in Distributed Systems

A Novel Genetic Algorithm for Static Task Scheduling in Distributed Systems

A new Metropolis optimisation method, the cross-section particle collision algorithm: some preliminary results

Rules Optimization Based Fuzzy Model for Predicting Distance Education Students’ Grades

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