296 research outputs found
Case-Based Reasoning as a Heuristic Selector in a Hyper-Heuristic for Course Timetabling Problems
This paper studies Knowledge Discovery (KD) using Tabu Search and Hill Climbing within Case-Based Reasoning (CBR) as a hyper-heuristic method for course timetabling problems. The aim of the hyper-heuristic is to choose the best heuristic(s) for given timetabling problems according to the knowledge stored in the case base. KD in CBR is a 2-stage iterative process on both case representation and the case base. Experimental results are analysed and related research issues for future work are discussed
Knowledge discovery in hyper-heuristic using case-based reasoning on course timetabling
This paper presents a new hyper-heuristic method using Case-Based Reasoning (CBR) for solving course timetabling problems. The term Hyper-heuristics has recently been employed to refer to 'heuristics that choose heuristics' rather than heuristics that operate directly on given problems. One of the overriding motivations of hyper-heuristic methods is the attempt to develop techniques that can operate with greater generality than is currently possible. The basic idea behind this is that we maintain a case base of information about the most successful heuristics for a range of previous timetabling problems to predict the best heuristic for the new problem in hand using the previous knowledge. Knowledge discovery techniques are used to carry out the training on the CBR system to improve the system performance on the prediction. Initial results presented in this paper are good and we conclude by discussing the con-siderable promise for future work in this area
Case Based Heuristic Selection for Timetabling Problems
This paper presents a case-based heuristic selection approach for automated university course and exam timetabling. The method described in this paper is motivated by the goal of developing timetabling systems that are fundamentally more general than the current state of the art. Heuristics that worked well in previous similar situations are memorized in a case base and are retrieved for solving the problem in hand. Knowledge discovery techniques are employed in two distinct scenarios. Firstly, we model the problem and the problem solving situations along with specific heuristics for those problems. Secondly, we refine the case base and discard cases which prove to be non-useful in solving new problems. Experimental results are presented and analyzed. It is shown that case based reasoning can act effectively as an intelligent approach to learn which heuristics work well for particular timetabling situations. We conclude by outlining and discussing potential research issues in this critical area of knowledge discovery for different difficult timetabling problems
Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems
This paper is concerned with the hybridization of two graph coloring heuristics (Saturation Degree and Largest Degree), and their application within a hyperheuristic for exam timetabling problems. Hyper-heuristics can be seen as algorithms which intelligently select appropriate algorithms/heuristics for solving a problem. We developed a Tabu Search based hyper-heuristic to search for heuristic lists (of graph heuristics) for solving problems and investigated the heuristic lists found by employing knowledge discovery techniques. Two hybrid approaches (involving Saturation Degree and Largest Degree) including one which employs Case Based Reasoning are presented and discussed. Both the Tabu Search based hyper-heuristic and the hybrid approaches are tested on random and real-world exam timetabling problems. Experimental results are comparable with the best state-of-the-art approaches (as measured against established benchmark problems). The results also demonstrate an increased level of generality in our approach
Case-based reasoning for course timetabling problems
The research in this thesis investigates Case-Based Reasoning (CBR), a Knowledge-Based Reasoning technique that proved to be capable of providing good solutions in educational course timetabling problems. Following the basic idea behind CBR, experiences in solving previous similar timetabling problems are employed to find the solutions for new problems.
A basic CBR system that is hierarchically organized with structured knowledge representations by attribute graphs is proposed in Chapter Four. The system is then further improved to solve a wider range of problems, which is described in Chapter Five. Evaluations on a large number of experiments indicate that this approach could provide a significant step forward in timetabling and scheduling research.
This basic system works well on relatively small problems. To deal with this drawback a multiple-retrieval approach that partitions large timetabling problems into small solvable sub-problems is presented in Chapter Six. Good results are obtained from a wide range of experiments.
In Chapter Seven, a new idea is introduced in CBR for solving timetabling problems by investigating the approach to select the most appropriate heuristic method rather than to employ it directly on the problem, in the attempt to raise the level of generality at which we can operate. All the evidence obtained from the first stage experiments indicates that there is a range of promising future directions.
Finally in Chapter Eight the results of the work are evaluated and some directions for future work are present
A Classification of Hyper-heuristic Approaches
The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of previous categorisations of hyper-heuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research
Case-based reasoning for course timetabling problems
The research in this thesis investigates Case-Based Reasoning (CBR), a Knowledge-Based Reasoning technique that proved to be capable of providing good solutions in educational course timetabling problems. Following the basic idea behind CBR, experiences in solving previous similar timetabling problems are employed to find the solutions for new problems.
A basic CBR system that is hierarchically organized with structured knowledge representations by attribute graphs is proposed in Chapter Four. The system is then further improved to solve a wider range of problems, which is described in Chapter Five. Evaluations on a large number of experiments indicate that this approach could provide a significant step forward in timetabling and scheduling research.
This basic system works well on relatively small problems. To deal with this drawback a multiple-retrieval approach that partitions large timetabling problems into small solvable sub-problems is presented in Chapter Six. Good results are obtained from a wide range of experiments.
In Chapter Seven, a new idea is introduced in CBR for solving timetabling problems by investigating the approach to select the most appropriate heuristic method rather than to employ it directly on the problem, in the attempt to raise the level of generality at which we can operate. All the evidence obtained from the first stage experiments indicates that there is a range of promising future directions.
Finally in Chapter Eight the results of the work are evaluated and some directions for future work are present
Tailoring hyper-heuristics to specific instances of a scheduling problem using affinity and competence functions
Hyper-heuristics are high level heuristics which coordinate lower level ones to solve a given problem. Low level heuristics, however, are not all as competent/good as each other at solving the given problem and some do not work together as well as others. Hence the idea of measuring how good they are (competence) at solving the problem and how well they work together (their affinity). Models of the affinity and competence properties are suggested and evaluated using previous information on the performance of the simple low level heuristics. The resulting model values are used to improve the performance of the hyper-heuristic by tailoring it not only to the specific problem but the specific instance being solved. The test case is a hard combinatorial problem, namely the Hybrid Flow Shop scheduling problem. Numerical results on randomly generated as well as real-world instances are included
Hyper-heuristic decision tree induction
A hyper-heuristic is any algorithm that searches or operates in the space of
heuristics as opposed to the space of solutions. Hyper-heuristics are
increasingly used in function and combinatorial optimization. Rather than
attempt to solve a problem using a fixed heuristic, a hyper-heuristic
approach attempts to find a combination of heuristics that solve a problem
(and in turn may be directly suitable for a class of problem instances).
Hyper-heuristics have been little explored in data mining. This work presents
novel hyper-heuristic approaches to data mining, by searching a space of
attribute selection criteria for decision tree building algorithm. The search is
conducted by a genetic algorithm. The result of the hyper-heuristic search in
this case is a strategy for selecting attributes while building decision trees.
Most hyper-heuristics work by trying to adapt the heuristic to the state of
the problem being solved. Our hyper-heuristic is no different. It employs a
strategy for adapting the heuristic used to build decision tree nodes
according to some set of features of the training set it is working on. We
introduce, explore and evaluate five different ways in which this problem
state can be represented for a hyper-heuristic that operates within a decisiontree
building algorithm. In each case, the hyper-heuristic is guided by a rule
set that tries to map features of the data set to be split by the decision tree
building algorithm to a heuristic to be used for splitting the same data set.
We also explore and evaluate three different sets of low-level heuristics that
could be employed by such a hyper-heuristic.
This work also makes a distinction between specialist hyper-heuristics and
generalist hyper-heuristics. The main difference between these two hyperheuristcs
is the number of training sets used by the hyper-heuristic genetic
algorithm. Specialist hyper-heuristics are created using a single data set from
a particular domain for evolving the hyper-heurisic rule set. Such algorithms
are expected to outperform standard algorithms on the kind of data set used
by the hyper-heuristic genetic algorithm. Generalist hyper-heuristics are
trained on multiple data sets from different domains and are expected to
deliver a robust and competitive performance over these data sets when
compared to standard algorithms.
We evaluate both approaches for each kind of hyper-heuristic presented in
this thesis. We use both real data sets as well as synthetic data sets. Our
results suggest that none of the hyper-heuristics presented in this work are
suited for specialization – in most cases, the hyper-heuristic’s performance on
the data set it was specialized for was not significantly better than that of
the best performing standard algorithm. On the other hand, the generalist
hyper-heuristics delivered results that were very competitive to the best
standard methods. In some cases we even achieved a significantly better
overall performance than all of the standard methods
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