Some Experiences with Hybrid Genetic Algorithms in Solving the Uncapacitated Examination Timetabling Problem

• URL: http://arxiv.org/abs/2306.00534v1
• Date: Thu, 1 Jun 2023 10:41:32 GMT
• Title: Some Experiences with Hybrid Genetic Algorithms in Solving the Uncapacitated Examination Timetabling Problem
• Authors: Ayse Aslan
• Abstract summary: The paper provides experimental experiences on two local search hybridized genetic algorithms in solving the uncapacitated examination timetabling problem. The proposed two hybrid algorithms use partition and priority based solution representations which are inspired from successful genetic algorithms proposed for graph coloring and project scheduling problems, respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper provides experimental experiences on two local search hybridized genetic algorithms in solving the uncapacitated examination timetabling problem. The proposed two hybrid algorithms use partition and priority based solution representations which are inspired from successful genetic algorithms proposed for graph coloring and project scheduling problems, respectively. The algorithms use a parametrized saturation degree heuristic hybridized crossover scheme. In the experiments, the algorithms firstly are calibrated with a Design of Experiments approach and then tested on the well-known Toronto benchmark instances. The calibration shows that the hybridization prefers an intensive local search method. The experiments indicate the vitality of local search in the proposed genetic algorithms, however, experiments also show that the hybridization benefits local search as well. Interestingly, although the structures of the two algorithms are not alike, their performances are quite similar to each other and also to other state-of-the-art genetic-type algorithms proposed in the literature.

Related papers

• Early years of Biased Random-Key Genetic Algorithms: A systematic review [2.249916681499244]
  This paper presents a systematic literature review and bibliometric analysis focusing on Biased Random-Key Genetic Algorithms (BRKGA) BRKGA is a metaheuristic framework that uses random-key-based chromosomes with biased, uniform, and elitist mating strategies alongside a genetic algorithm.
  arXiv  Detail & Related papers  (2024-05-02T22:22:41Z)
• Multi-agricultural Machinery Collaborative Task Assignment Based on Improved Genetic Hybrid Optimization Algorithm [0.0]
  This study proposes a method for multi-agricultural machinery collaborative task assignment based on an improved genetic hybrid optimisation algorithm. The developed hybrid algorithm can effectively reduce path costs, and the efficiency of the assignment outcomes surpasses that of the classical genetic algorithm.
  arXiv  Detail & Related papers  (2023-12-07T12:42:40Z)
• Batch Bayesian Optimization for Replicable Experimental Design [56.64902148159355]
  Many real-world design problems evaluate multiple experimental conditions in parallel and replicate each condition multiple times due to large and heteroscedastic observation noise. We propose the Batch Thompson Sampling for Replicable Experimental Design framework, which encompasses three algorithms. We show the effectiveness of our algorithms in two practical real-world applications: precision agriculture and AutoML.
  arXiv  Detail & Related papers  (2023-11-02T12:46:03Z)
• Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems [1.8434042562191815]
  Genetic Algorithms (GAs) are known for their efficiency in solving optimization problems. This paper proposes a new metaheuristic algorithm called the Genetic Engineering Algorithm (GEA) that draws inspiration from genetic engineering concepts.
  arXiv  Detail & Related papers  (2023-09-28T13:05:30Z)
• Amortized Implicit Differentiation for Stochastic Bilevel Optimization [53.12363770169761]
  We study a class of algorithms for solving bilevel optimization problems in both deterministic and deterministic settings. We exploit a warm-start strategy to amortize the estimation of the exact gradient. By using this framework, our analysis shows these algorithms to match the computational complexity of methods that have access to an unbiased estimate of the gradient.
  arXiv  Detail & Related papers  (2021-11-29T15:10:09Z)
• Hybrid Henry Gas Solubility Optimization Algorithm with Dynamic Cluster-to-Algorithm Mapping for Search-based Software Engineering Problems [1.0323063834827413]
  This paper discusses a new variant of the Henry Gas Solubility Optimization (HGSO) Algorithm, called Hybrid HGSO (HHGSO) Unlike its predecessor, HHGSO allows multiple clusters serving different individual meta-heuristic algorithms to coexist within the same population. Exploiting the dynamic cluster-to-algorithm mapping via penalized and reward model with adaptive switching factor, HHGSO offers a novel approach for meta-heuristic hybridization.
  arXiv  Detail & Related papers  (2021-05-31T12:42:15Z)
• A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems [54.61091936472494]
  We develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together. The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively. Results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems.
  arXiv  Detail & Related papers  (2021-03-10T03:16:12Z)
• Towards causal benchmarking of bias in face analysis algorithms [54.19499274513654]
  We develop an experimental method for measuring algorithmic bias of face analysis algorithms. Our proposed method is based on generating synthetic transects'' of matched sample images. We validate our method by comparing it to a study that employs the traditional observational method for analyzing bias in gender classification algorithms.
  arXiv  Detail & Related papers  (2020-07-13T17:10:34Z)
• Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising [51.97494906131859]
  Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc. Existing exact and approximate algorithms usually fail in such settings due to either requiring intolerable running time or too much computation resource. We propose textttNeuSearcher which leverages the knowledge learned from previously instances to solve new problem instances.
  arXiv  Detail & Related papers  (2020-05-09T02:48:23Z)
• Multi-User Remote lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm [1.0953917735844645]
  The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed algorithm. The proposed algorithm utilizes the Simplex algorithm in terms of exploration, and NSGA for sorting local optimum points.
  arXiv  Detail & Related papers  (2020-03-26T02:31:50Z)
• Optimal Randomized First-Order Methods for Least-Squares Problems [56.05635751529922]
  This class of algorithms encompasses several randomized methods among the fastest solvers for least-squares problems. We focus on two classical embeddings, namely, Gaussian projections and subsampled Hadamard transforms. Our resulting algorithm yields the best complexity known for solving least-squares problems with no condition number dependence.
  arXiv  Detail & Related papers  (2020-02-21T17:45:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.