Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling
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 e...
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| Format: | Article |
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Elsevier
2014
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| Online Access: | https://eprints.nottingham.ac.uk/28269/ |
| _version_ | 1848793538965274624 |
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| author | Li, Jingpeng Bai, Ruibin Shen, Yindong Qu, Rong |
| author_facet | Li, Jingpeng Bai, Ruibin Shen, Yindong Qu, Rong |
| author_sort | Li, Jingpeng |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:01:54Z |
| format | Article |
| id | nottingham-28269 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:01:54Z |
| publishDate | 2014 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-282692020-05-04T16:57:30Z https://eprints.nottingham.ac.uk/28269/ Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling Li, Jingpeng Bai, Ruibin Shen, Yindong Qu, Rong 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. Elsevier 2014-11-13 Article PeerReviewed Li, Jingpeng, Bai, Ruibin, Shen, Yindong and Qu, Rong (2014) 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. ISSN 0377-2217 Metaheuristics Evolutionary algorithm stochastic process combinatorial optimisation exam timetabling http://www.sciencedirect.com/science/article/pii/S0377221714009060 doi:10.1016/j.ejor.2014.11.002 doi:10.1016/j.ejor.2014.11.002 |
| spellingShingle | Metaheuristics Evolutionary algorithm stochastic process combinatorial optimisation exam timetabling Li, Jingpeng Bai, Ruibin Shen, Yindong Qu, Rong Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling |
| title | Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling |
| title_full | Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling |
| title_fullStr | Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling |
| title_full_unstemmed | Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling |
| title_short | Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling |
| title_sort | search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling |
| topic | Metaheuristics Evolutionary algorithm stochastic process combinatorial optimisation exam timetabling |
| url | https://eprints.nottingham.ac.uk/28269/ https://eprints.nottingham.ac.uk/28269/ https://eprints.nottingham.ac.uk/28269/ |