Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solution goes through two successive processes heuristic selection and move acceptance until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems with different characteristics. Some previous studies show that different combinations of heuristic selection and move acceptance as hyper-heuristic components might yield different performances. This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.

/pdf/a-reinforcement-learning-great-deluge-hyper-heuristic-for-21rn9j47y5.pdf

A Reinforcement Learning-Great-Deluge Hyper-Heuristic for Examination Timetabling

A multi-objective approach for robust airline scheduling

Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem

Ontologies are recognized as a fundamental component for enabling interoperability across heterogeneous systems and applications. Indeed, they try to fit a common understanding of concepts in a particular domain of interest to support the exchange of information among people, artificial agents, and distributed applications. Unfortunately, because of human subjectivity, various ontologies related to the same application domain may use different terms for the same meaning or may use the same term to mean different things, raising the so-called heterogeneity problem. The ontology alignment process tries to solve this semantic gap by individuating a collection of similar entities belonging to different ontologies and enabling a full comprehension among different actors involved in a given knowledge exchanging. However, the complexity of the alignment task, especially for large ontologies, requires an automated and effective support for computing high-quality alignments. The aim of this paper is to propose a memetic algorithm to perform an efficient matching process capable of computing a suboptimal alignment between two ontologies. As shown by experiments, the memetic approach is more suitable for ontology alignment problem than a classical evolutionary technique such as genetic algorithms. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

A hybrid evolutionary approach for solving the ontology alignment problem

In this paper, we address the issue of ordering events by simultaneously con- sidering three separate heuristics using fuzzy methods. Combinations of the fol- lowing three heuristic orderings are em- ployed: largest degree, saturation degree and largest enrollment. The fuzzy weight of an event is used to represent how difficult it is to schedule. The decreasingly ordered events are sequentially chosen to be assigned to the last slot with least penalty cost value while the feasibility of the timetable is maintained throughout the process. Unscheduling and reschedul- ing events is performed until all events are scheduled. The proposed algorithm has been tested on 11 benchmark data sets of course timetabling problems and the re- sults show that this approach can produce good quality solutions with low require- ments for rescheduling. Moreover, there is signi?cant potential to extend the ap- proach further by including a larger range of criteria.

Fuzzy multiple heuristic orderings for course timetabling

http://www.cs.nott.ac.uk/~pszeo/docs/publications/ICMA.pdf

The Interleaved Constructive Memetic Algorithm and its application to timetabling

Timetabling problems are well known complicated constraint satisfaction problems A new real- world course timetabling problem is described in this study The difficulty in approximating to an optimal solution in a reasonable time increases for the large problem instances regardless of the algorithm used An incremental strategy that aims to relieve this difficulty to some extent com- bined with a memetic algorithm (MA) is investigated for solving the new timetabling problem The incremental MA is a multistage approach that enlarges the size of the candidate solutions by adding a selected subset of unscheduled course meetings at a stage A solution is then sought for all the course meetings at hand The initial results show that the incremental MA is promising The high-school graduates in Turkey enter a country-wide Student Selection Examination (OSS), in order to get admitted to a higher education program at a university The Student Selection and Placement Center (OSYM) has been providing a centralized system for fair access and placement of students to higher education programs since 1974 Currently, OSYM administers OSS The stu- dents are placed into the higher education programs according to their OSS score and set of choices OSS is arranged every year Less than 40% of the applicants can continue their education in an undergraduate degree program This situation causes a high-level competition among stu- dents For this reason, there are many private OSS preparation schools in Turkey established as a support mechanism for the candidates Such schools opening more branches started to deal with the difficult problem of scheduling the course meetings properly subject to a set of constraints The timetabling problems are NP complete (11) real-world problems A comprehensive survey on timetabling can be found in (7) particularly discussing the MAs Course timetabling problems (CTPs) are a subset of timetabling problems that require an optimal arrangement of resources for the employees (eg, teachers), employers (eg, school administration) and students at an educa- tional institution subject to a set of constraints There are two types of constraints in timetabling: hard and soft Hard constraint violations represent highly undesirable situations, while soft con- straints are less essential preferences The constraints can be categorized further for the practical timetabling In this paper, a new course timetabling problem is introduced, University Exam (OSS) Preparation School Timetabling Problem (PSTP) PSTP is analyzed initially by Alpay Alkan dur- ing his studies on timetabling Alkan and Ozcan introduced a violation directed hierarchical hill climbing method (VDHC) in (3) for solving the university course timetabling problem VDHC performs a local search over the constraint oriented neighborhoods as described in (29) based on the constraint types, their viola- tions and structure of the problem Ozcan extended the study and suggested a heuristic template for designing a set of operators in (22) A variety of genetic operators and self-adjusting hill-climbers based on this template have already been experimented within the Memetic Algorithm (MAs) for solving different timetabling problems in (21) and (23) The VDHC methods within the MAs that managed multiple constraint based hill climbers turned out to be successful in timetabling In this study, an incremental MA is proposed for solving PSTPs The performance of the incremental MA is compared to the conventional one both using the VDHC on real PSTP instances, each requiring a large set of course meetings to be scheduled

/pdf/a-memetic-algorithm-for-solving-a-timetabling-problem-an-5cwidwazhv.pdf

A Memetic Algorithm for Solving a Timetabling Problem: An Incremental Strategy

Course timetabling problems are real world constraint optimization problems that are often coped with educational institutions, such as universities or high schools. In this paper, we present a variety of new operators that can be also applied in evolutionary algorithms for other timetabling problems, such as, exam timetabling. Operators include violation directed mutations, crossovers, and a successful violation directed hierarchical hill climbing method. Tests are performed on a small portion of a real data and results are promising.

/pdf/memetic-algorithms-for-timetabling-28zba8umin.pdf

Memetic algorithms for timetabling

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The Interleaved Constructive Memetic Algorithm and its application to timetabling

A Memetic Algorithm for Solving a Timetabling Problem: An Incremental Strategy

Memetic algorithms for timetabling