Adaptive Steady State Genetic Algorithm for scheduling university exams

Alsharafat, Wafa; AlSharafat, Mohammad Slaibi

doi:10.1109/icnit.2010.5508555

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…In 2013, Alsharafat devised an intelligent DSS for enrollment management in higher education institutions employing multi aggregator models and evolutionary computing such as fuzzy logic, genetic algorithms, neuro-networks, and probabilistic reasoning [34].…”

Section: Proposed Top-down Processmentioning

confidence: 99%

Goal Programming and Mathematical Modelling for Developing a Capacity Planning Decision Support System-Based Framework in Higher Education Institutions

et al. 2022

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Achieving the Saudi Kingdom’s vision 2030 in the higher education sector requires higher education institutions to make a significant simultaneous change in their current practices. This encompasses the transitioning of government-funded educational institutions to be financially independent. Therefore, a prompt, agile transition is required while maintaining a positive socioeconomic impact, entrepreneurship and innovation, and high-quality education. This necessitates the transition to lean processes and the review of current practices. One of the most vital processes in educational institutions is student admission/enrollment capacity planning. This study puts forward a capacity planning decision support system (DSS)-based framework for university student enrollment. The framework was applied to the case of KAU, where current practice and challenges are presented, and from which data were collected. A top-down/bottom-up approach was followed and applied using the goal programming technique and a developed mathematical model, respectively. Results show that the proposed framework effectively affects student admission/enrollment capacity planning on strategic and operational levels. Moreover, it can be used in other planning aspects of higher education in universities, such as human resources planning, teaching load planning, faculty-to-student ratios, accreditation, quality requirements, lab capacity planning, equipment/teaching aids procurement, and financial planning, to mention a few. The implications of this study include assisting decision-makers in higher education institutions in matching their admission/enrollment capacity of student numbers between the macro-strategic and the micro-operational level.

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Section: Proposed Top-down Processmentioning

confidence: 99%

Goal Programming and Mathematical Modelling for Developing a Capacity Planning Decision Support System-Based Framework in Higher Education Institutions

et al. 2022

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“…Visually enhanced presentation of the output facilitates its perception and interpretation. Recently, multiaggregator models for fuzzy queries and ranking based on an evolutionary computing approach to build a decision support system for admission student in university have been introduced by Alsharafat [ 25 ]. A unified approach based on a combination of four soft computing methodologies (Fuzzy Logic, Neuronetworks, Genetic Algorithms, and Probabilistic Reasoning) was used to build the proposed intelligent DSS.…”

Section: Literature Reviewmentioning

confidence: 99%

A Generalized National Planning Approach for Admission Capacity in Higher Education: A Nonlinear Integer Goal Programming Model with a Novel Differential Evolution Algorithm

El-Qulity

Mohamed

2016

Computational Intelligence and Neuroscience

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This paper proposes a nonlinear integer goal programming model (NIGPM) for solving the general problem of admission capacity planning in a country as a whole. The work aims to satisfy most of the required key objectives of a country related to the enrollment problem for higher education. The system general outlines are developed along with the solution methodology for application to the time horizon in a given plan. The up-to-date data for Saudi Arabia is used as a case study and a novel evolutionary algorithm based on modified differential evolution (DE) algorithm is used to solve the complexity of the NIGPM generated for different goal priorities. The experimental results presented in this paper show their effectiveness in solving the admission capacity for higher education in terms of final solution quality and robustness.

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“…In a single-deme (single population) GA, a similar scheme can be applied where random individuals are introduced into each generation from the global search space, thus introducing an alternative source of diversity. A typical GA will use either the incremental/steady state genetic algorithm (IGA) model [2], [41] or the generational genetic algorithm (GGA) [7], [40]. Here we use the GGA that batch replaces an entire population each generation, as opposed to the IGA which in typical applications only replaces one individual at a time.…”

Section: Random Migration Operatorsmentioning

confidence: 99%

A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

Gladwin

Stewart

2011

Engineering Applications of Artificial Intelligence

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Abstract-In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the number of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Algorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which represents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-theloop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits.

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Adaptive Steady State Genetic Algorithm for scheduling university exams

Cited by 6 publications

References 10 publications

Goal Programming and Mathematical Modelling for Developing a Capacity Planning Decision Support System-Based Framework in Higher Education Institutions

Goal Programming and Mathematical Modelling for Developing a Capacity Planning Decision Support System-Based Framework in Higher Education Institutions

A Generalized National Planning Approach for Admission Capacity in Higher Education: A Nonlinear Integer Goal Programming Model with a Novel Differential Evolution Algorithm

A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

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