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A Monte Carlo HyperHeuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine
 PLACEMENT MACHINE, INTECH’03 THAILAND
, 2003
"... In this paper we introduce a Monte Carlo based hyperheuristic. The Monte Carlo hyperheuristic manages a set of low level heuristics (in this case just simple 2opt swaps but they could be any other heuristics). Each of the low level heuristics is responsible for creating a unique neighbour that ..."
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Cited by 35 (9 self)
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In this paper we introduce a Monte Carlo based hyperheuristic. The Monte Carlo hyperheuristic manages a set of low level heuristics (in this case just simple 2opt swaps but they could be any other heuristics). Each of the low level heuristics is responsible for creating a unique neighbour that may be impossible to create by the other low level heuristics. On each iteration, the Monte Carlo hyper heuristic randomly calls a low level heuristic. The new solution returned by the low level heuristic will be accepted based on the Monte Carlo acceptance criteria. The Monte Carlo acceptance criteria always accept an improved solution. Worse solutions will be accepted with a certain probability, which decreases with worse solutions, in order to escape local minima. We develop three hyperheuristics based on a Monte Carlo method, these being Linear Monte Carlo Exponential Monte Carlo and Exponential Monte Carlo with counter. We also investigate four other hyperheuristics to examine their performance and for comparative purposes. To demonstrate our approach we employ these hyperheuristics to optimise component placement sequencing in order to improve the efficiency of the multi head placement machine. Experimental results show that the Exponential Monte Carlo hyperheuristic is superior to the other hyperheuristics and is superior to a choice function hyperheuristic reported in earlier work.
Applications to timetabling
 Handbook of Graph Theory, chapter 5.6
, 2004
"... Abstract Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyperheuristics are search methodologies that not only aim to provide a general framework for solving problem instances at different difficulty levels in a given dom ..."
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Cited by 29 (17 self)
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Abstract Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyperheuristics are search methodologies that not only aim to provide a general framework for solving problem instances at different difficulty levels in a given domain, but a key goal is also to extend the level of generality so that different problems from different domains can also be solved. Indeed, a major challenge is to explore how the heuristic design process might be automated. Almost all existing iterative selection hyperheuristics performing single point search contain two successive stages; heuristic selection and move acceptance. Different operators can be used in either of the stages. Recent studies explore ways of introducing learning mechanisms into the search process for improving the performance of hyperheuristics. In this study, a broad empirical analysis is performed comparing Monte Carlo based hyperheuristics for solving capacitated examination timetabling problems. One of these hyperheuristics is an approach that overlaps two stages and presents them in a single algorithmic body. A learning heuristic selection method (L) operates in harmony with a simulated annealing move acceptance method using reheating (SA) based on some shared variables. Yet, the heuristic selection and move
Evolving Bin Packing Heuristics with Genetic Programming
 PARALLEL PROBLEM SOLVING FROM NATURE  PPSN IX SPRINGER LECTURE NOTES IN COMPUTER SCIENCE. VOLUME 4193 OF LNCS., REYKJAVIK, ICELAND, SPRINGERVERLAG (2006) 860–869
, 2006
"... The binpacking problem is a well known NPHard optimisation problem, and, over the years, many heuristics have been developed to generate good quality solutions. This paper outlines a genetic programming system which evolves a heuristic that decides whether to put a piece in a bin when presente ..."
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Cited by 27 (12 self)
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The binpacking problem is a well known NPHard optimisation problem, and, over the years, many heuristics have been developed to generate good quality solutions. This paper outlines a genetic programming system which evolves a heuristic that decides whether to put a piece in a bin when presented with the sum of the pieces already in the bin and the size of the piece that is about to be packed. This heuristic operates in a fixed framework that iterates through the open bins, applying the heuristic to each one, before deciding which bin to use. The best evolved programs emulate the functionality of the human designed `firstfit' heuristic. Thus, the contribution of this paper is to demonstrate that genetic programming can be employed to automatically evolve bin packing heuristics which are the same as high quality heuristics which have been designed by humans.
Exploring Hyperheuristic Methodologies with Genetic Programming
"... Hyperheuristics represent a novel search methodology that is motivated by the goal of automating the process of selecting or combining simpler heuristics in order to solve hard computational search problems. An extension of the original hyperheuristic idea is to generate new heuristics which are n ..."
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Cited by 20 (11 self)
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Hyperheuristics represent a novel search methodology that is motivated by the goal of automating the process of selecting or combining simpler heuristics in order to solve hard computational search problems. An extension of the original hyperheuristic idea is to generate new heuristics which are not currently known. These approaches operate on a search space of heuristics rather than directly on a search space of solutions to the underlying problem which is the case with most metaheuristics implementations. In the majority of hyperheuristic studies so far, a framework is provided with a set of human designed heuristics, taken from the literature, and with good measures of performance in practice. A less well studied approach aims to generate new heuristics from a set of potential heuristic components. The purpose of this chapter is to discuss this class of hyperheuristics, in which Genetic Programming is the most widely used methodology. A detailed discussion is presented including the steps needed to apply this technique, some representative case studies, a literature review of related work, and a discussion of relevant issues. Our aim is to convey the exciting potential of this innovative approach for automating the heuristic design process
Distributed Choice Function Hyperheuristics for Timetabling and Scheduling
 Practice and Theory of Automated Timetabling V, Springer Lecture notes in Computer Science. Volume 3616. (2005) 51–67
, 2004
"... This paper reports on ongoing research in the design of choice function hyperheuristics, the modelling of generalpurpose low level heuristics, and the exploitation of parallel computing platforms for hyperheuristics ..."
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Cited by 18 (1 self)
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This paper reports on ongoing research in the design of choice function hyperheuristics, the modelling of generalpurpose low level heuristics, and the exploitation of parallel computing platforms for hyperheuristics
A Classification of Hyperheuristic Approaches
"... The current state of the art in hyperheuristic 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 ..."
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Cited by 18 (13 self)
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The current state of the art in hyperheuristic 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 hyperheuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyperheuristic 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 hyperheuristic research.
CaseBased Heuristic Selection for Timetabling Problems
, 2003
"... This paper presents a casebased 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 wor ..."
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Cited by 17 (6 self)
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This paper presents a casebased 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 new 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 nonuseful 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 area of knowledge discovery for different difficult timetabling problems
A simulated annealing hyperheuristic methodology for flexible decision support
, 2007
"... One of the main motivations for investigating hyperheuristic methodologies is to provide a more general search framework than is currently available. Most of the current search techniques represent approaches that are largely adapted for specific search problems (and, in some cases, even specific ..."
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Cited by 14 (7 self)
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One of the main motivations for investigating hyperheuristic methodologies is to provide a more general search framework than is currently available. Most of the current search techniques represent approaches that are largely adapted for specific search problems (and, in some cases, even specific problem instances). There are many realworld scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are more generally applicable to more problems. One of our motivating goals is to underpin the development of more flexible search methodologies that can be easily and automatically employed on a broader range of problems than is currently possible. Almost all the heuristics that have appeared in the literature have been designed and selected by humans. In this paper, we investigate a simulated annealing hyperheuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a shortterm memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark data sets drawn from three very different and difficult (NPhard) problems: nurse rostering, university course timetabling and onedimensional bin packing. Experimental results show that the proposed hyperheuristic is able to achieve significant performance improvements over a recently proposed tabu search hyperheuristic without lowering the level of generality. We
To Max or not to Max: Online Learning for Speeding Up Optimal Planning
, 2010
"... It is well known that there cannot be a single “best ” heuristic for optimal planning in general. One way of overcoming this is by combining admissible heuristics (e.g. by using their maximum), which requires computing numerous heuristic estimates at each state. However, there is a tradeoff between ..."
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Cited by 12 (7 self)
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It is well known that there cannot be a single “best ” heuristic for optimal planning in general. One way of overcoming this is by combining admissible heuristics (e.g. by using their maximum), which requires computing numerous heuristic estimates at each state. However, there is a tradeoff between the time spent on computing these heuristic estimates for each state, and the time saved by reducing the number of expanded states. We present a novel method that reduces the cost of combining admissible heuristics for optimal search, while maintaining its benefits. Based on an idealized search space model, we formulate a decision rule for choosing the best heuristic to compute at each state. We then present an active online learning approach for that decision rule, and employ the learned model to decide which heuristic to compute at each state. We evaluate this technique empirically, and show that it substantially outperforms each of the individual heuristics that were used, as well as their regular maximum.
Channel Assignment in Cellular Communication Using a Great Deluge HyperHeuristic
 in the Proceedings of the 2004 IEEE International Conference on Network (ICON2004
, 2004
"... This paper proposes a methodology for the channel assignment problem in the cellular communication industry. The problem considers the assignment of a limited channel bandwidth to satisfy a growing channel demand without violating electromagnetic interference constraints. The initial solution is gen ..."
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Cited by 10 (2 self)
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This paper proposes a methodology for the channel assignment problem in the cellular communication industry. The problem considers the assignment of a limited channel bandwidth to satisfy a growing channel demand without violating electromagnetic interference constraints. The initial solution is generated using random constructive heuristic. This solution is then improved using a hyperheuristic technique based on the great deluge algorithm. Our experimental results, on benchmarks data sets, gives promising results.