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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Search with Evolutionary Ruin and Stochastic Rebuild: a Theoretic Framework and a Case Study on Exam Timetabling
Authors: Li, Jingpeng
Bai, Ruibin
Shen, Yindong
Qu, Rong
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Keywords: Metaheuristics
Evolutionary algorithm
Stochastic process
Combinatorial optimisation
Exam timetabling
Issue Date: May-2015
Publisher: Elsevier
Citation: Li J, Bai R, Shen Y & Qu R (2015) Search with Evolutionary Ruin and Stochastic Rebuild: a Theoretic Framework and a Case Study on Exam Timetabling, European Journal of Operational Research, 242 (3), pp. 798-806.
Abstract: This paper presents a state transition based formal framework for a new search method, called Evolutionary Ruin and Stochastic Recreate, which tries to learn and adapt to the changing environments during the search process. It improves the performance of the original Ruin and Recreate principle by embedding an additional phase of Evolutionary Ruin to mimic the survival-of-the-fittest mechanism within single solutions. This method executes a cycle of Solution Decomposition, Evolutionary Ruin, Stochastic Recreate and Solution Acceptance until a certain stopping condition is met. The Solution Decomposition phase first uses some problem-specific knowledge to decompose a complete solution into its components and assigns a score to each component. The Evolutionary Ruin phase then employs two evolutionary operators (namely Selection and Mutation) to destroy a certain fraction of the solution, and the next Stochastic Recreate phase repairs the “broken” solution. Last, the Solution Acceptance phase selects a specific strategy to determine the probability of accepting the newly generated solution. Hence, optimisation is achieved by an iterative process of component evaluation, solution disruption and stochastic constructive repair. From the state transitions point of view, this paper presents a probabilistic model and implements a Markov chain analysis on some theoretical properties of the approach. Unlike the theoretical work on genetic algorithm and simulated annealing which are based on state transitions within the space of complete assignments, our model is based on state transitions within the space of partial assignments. The exam timetabling problems are used to test the performance in solving real-world hard problems.
Type: Journal Article
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Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: Computing Science - CSM Dept
University of Nottingham Ningbo China
Huazhong University of Science and Technology
University of Nottingham

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