A bi-criteria hybrid Genetic Algorithm with robustness objective for the course timetabling problem

Akkan, Can; Gülcü, Ayla

doi:10.1016/j.cor.2017.09.007

Cited by 65 publications

(32 citation statements)

References 31 publications

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“…At this stage, the fitness value between the proposed GA method and other literature is performed [10]. The literature presents the results of the algorithm using a hybrid GA to solve course scheduling problems.…”

Section: Comparison Of Ga Methods From Other Literature With Proposed Gamentioning

confidence: 99%

“…Modifications of OX in recursive GA have also been conducted to produce decently scheduled timetabling courses [9]. PMX is modified using a feasibility check during gene exchange based on the period to obtain a good offspring [10]. The feasibility check carried out during the exchange process generates a much faster and better performance than conducting it after the exchange process.…”

Section: Introductionmentioning

confidence: 99%

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Multi-parent order crossover mechanism of genetic algorithm for minimizing violation of soft constraint on course timetabling problem

Fajrin

Fatichah

2020

register. jurnal. ilm. teknologi. sistem. inf.

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A crossover operator is one of the critical procedures in genetic algorithms. It creates a new chromosome from the mating result to an extensive search space. In the course timetabling problem, the quality of the solution is evaluated based on the hard and soft constraints. The hard constraints need to be satisfied without violation while the soft constraints allow violation. In this research, a multi-parent crossover mechanism is used to modify the classical crossover and minimize the violation of soft constraints, in order to produce the right solution. Multi-parent order crossover mechanism tends to produce better chromosome and also prevent the genetic algorithm from being trapped in a local optimum. The experiment with 21 datasets shows that the multi-parent order crossover mechanism provides a better performance and fitness value than the classical with a zero fitness value or no violation occurred. It is noteworthy that the proposed method is effective to produce available course timetabling.

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Section: Comparison Of Ga Methods From Other Literature With Proposed Gamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-parent order crossover mechanism of genetic algorithm for minimizing violation of soft constraint on course timetabling problem

Fajrin

Fatichah

2020

register. jurnal. ilm. teknologi. sistem. inf.

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“…Besides, genetic algorithms and particle swarm optimisation methods have received much attention in the recent years, and many commercial solvers permit simple implementation of such methods. Accordingly, few hybridisations of multiobjective simulated annealing with one of the aforementioned metaheuristics have been introduced in the literature [70,71] in addition to many applications in supply chain [72,73], distribution networks [74,75], facility layout design [76,77], design optimisation [66], and scheduling [69,[78][79][80].…”

Section: Domination-based Mosa Methodsmentioning

confidence: 99%

Multiobjective Simulated Annealing: Principles and Algorithm Variants

Amine

2019

Advances in Operations Research

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Simulated annealing is a stochastic local search method, initially introduced for global combinatorial mono-objective optimisation problems, allowing gradual convergence to a near-optimal solution. An extended version for multiobjective optimisation has been introduced to allow a construction of near-Pareto optimal solutions by means of an archive that catches nondominated solutions while exploring the feasible domain. Although simulated annealing provides a balance between the exploration and the exploitation, multiobjective optimisation problems require a special design to achieve this balance due to many factors including the number of objective functions. Accordingly, many variants of multiobjective simulated annealing have been introduced in the literature. This paper reviews the state of the art of simulated annealing algorithm with a focus upon multiobjective optimisation field.

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“…From the multi-objective point of view, there are many evaluation measures that compare the obtained Pareto fronts of competitors (Akkan & Gülcü, 2018).…”

Section: Assessment Metricsmentioning

confidence: 99%

An evolutionary decomposition-based multi-objective feature selection for multi-label classification

Bidgoli

Ebrahimpour-Komleh

Rahnamayan

2020

PeerJ Computer Science

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Data classification is a fundamental task in data mining. Within this field, the classification of multi-labeled data has been seriously considered in recent years. In such problems, each data entity can simultaneously belong to several categories. Multi-label classification is important because of many recent real-world applications in which each entity has more than one label. To improve the performance of multi-label classification, feature selection plays an important role. It involves identifying and removing irrelevant and redundant features that unnecessarily increase the dimensions of the search space for the classification problems. However, classification may fail with an extreme decrease in the number of relevant features. Thus, minimizing the number of features and maximizing the classification accuracy are two desirable but conflicting objectives in multi-label feature selection. In this article, we introduce a multi-objective optimization algorithm customized for selecting the features of multi-label data. The proposed algorithm is an enhanced variant of a decomposition-based multi-objective optimization approach, in which the multi-label feature selection problem is divided into single-objective subproblems that can be simultaneously solved using an evolutionary algorithm. This approach leads to accelerating the optimization process and finding more diverse feature subsets. The proposed method benefits from a local search operator to find better solutions for each subproblem. We also define a pool of genetic operators to generate new feature subsets based on old generation. To evaluate the performance of the proposed algorithm, we compare it with two other multi-objective feature selection approaches on eight real-world benchmark datasets that are commonly used for multi-label classification. The reported results of multi-objective method evaluation measures, such as hypervolume indicator and set coverage, illustrate an improvement in the results obtained by the proposed method. Moreover, the proposed method achieved better results in terms of classification accuracy with fewer features compared with state-of-the-art methods.

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A bi-criteria hybrid Genetic Algorithm with robustness objective for the course timetabling problem

Cited by 65 publications

References 31 publications

Multi-parent order crossover mechanism of genetic algorithm for minimizing violation of soft constraint on course timetabling problem

Multi-parent order crossover mechanism of genetic algorithm for minimizing violation of soft constraint on course timetabling problem

Multiobjective Simulated Annealing: Principles and Algorithm Variants

An evolutionary decomposition-based multi-objective feature selection for multi-label classification

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