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Examination Timetabling Using Late Acceptance Hyperheuristics
"... Abstract — A hyperheuristic is a high level problem solving methodology that performs a search over the space generated by a set of low level heuristics. One of the hyperheuristic frameworks is based on a single point search containing two main stages: heuristic selection and move acceptance. Most o ..."
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Abstract — A hyperheuristic is a high level problem solving methodology that performs a search over the space generated by a set of low level heuristics. One of the hyperheuristic frameworks is based on a single point search containing two main stages: heuristic selection and move acceptance. Most of the existing move acceptance methods compare a new solution, generated after applying a heuristic, against a current solution in order to decide whether to reject it or replace the current one. Late Acceptance Strategy is presented as a promising local search methodology based on a novel move acceptance mechanism. This method performs a comparison between the new candidate solution and a previous solution that is generated L steps earlier. In this study, the performance of a set of hyperheuristics utilising different heuristic selection methods combined with the Late Acceptance Strategy are investigated over an examination timetabling problem. The results illustrate the potential of this approach as a hyperheuristic component. The hyperheuristic formed by combining a random heuristic selection with Late Acceptance Strategy improves on the best results obtained in a previous study. I.
Heuristic selection for stochastic search optimization: Modeling solution quality by extreme value theory
 In Proceedings of the 10th International Conference on Principles and Practice of Constraint Programming
, 2004
"... Abstract. The success of stochastic algorithms is often due to their ability to effectively amplify the performance of search heuristics. This is certainly the case with stochastic sampling algorithms such as heuristicbiased stochastic sampling (HBSS) and valuebiased stochastic sampling (VBSS), wh ..."
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Abstract. The success of stochastic algorithms is often due to their ability to effectively amplify the performance of search heuristics. This is certainly the case with stochastic sampling algorithms such as heuristicbiased stochastic sampling (HBSS) and valuebiased stochastic sampling (VBSS), wherein a heuristic is used to bias a stochastic policy for choosing among alternative branches in the search tree. One complication in getting the most out of algorithms like HBSS and VBSS in a given problem domain is the need to identify the most effective search heuristic. In many domains, the relative performance of various heuristics tends to vary across different problem instances and no single heuristic dominates. In such cases, the choice of any given heuristic will be limiting and it would be advantageous to gain the collective power of several heuristics. Toward this goal, this paper describes a framework for integrating multiple heuristics within a stochastic sampling search algorithm. In its essence, the framework uses onlinegenerated statistical models of the search performance of different base heuristics to select which to employ on each subsequent iteration of the search. To estimate the solution quality distribution resulting from repeated application of a strong heuristic within a stochastic search, we propose the use of models from extreme value theory (EVT). Our EVTmotivated approach is validated on the NPHard problem of resourceconstrained project scheduling with time windows (RCPSP/max). Using VBSS as a base stochastic sampling algorithm, the integrated use of a set of project scheduling heuristics is shown to be competitive with the current best known heuristic algorithm for RCPSP/max and in some cases even improves upon best known solutions to difficult benchmark instances. 1
Local search with very largescale neighborhoods for optimal permutations in machine translation
 In Proc. of the Workshop on Computationally Hard Problems and Joint Inference
, 2006
"... We introduce a novel decoding procedure for statistical machine translation and other ordering tasks based on a family of Very LargeScale Neighborhoods, some of which have previously been applied to other NPhard permutation problems. We significantly generalize these problems by simultaneously con ..."
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We introduce a novel decoding procedure for statistical machine translation and other ordering tasks based on a family of Very LargeScale Neighborhoods, some of which have previously been applied to other NPhard permutation problems. We significantly generalize these problems by simultaneously considering three distinct sets of ordering costs. We discuss how these costs might apply to MT, and some possibilities for training them. We show how to search and sample from exponentially large neighborhoods using efficient dynamic programming algorithms that resemble statistical parsing. We also incorporate techniques from statistical parsing to improve the runtime of our search. Finally, we report results of preliminary experiments indicating that the approach holds promise. 1
A.J.: Mapping the performance of heuristics for constraint satisfaction
 In: IEEE Congress on Evolutionary Computation (CEC
, 2010
"... Abstract — Hyperheuristics are high level search methodologies that operate over a set of heuristics which operate directly on the problem domain. In one of the hyperheuristic frameworks, the goal is automating the process of selecting a humandesigned low level heuristic at each step to construc ..."
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Abstract — Hyperheuristics are high level search methodologies that operate over a set of heuristics which operate directly on the problem domain. In one of the hyperheuristic frameworks, the goal is automating the process of selecting a humandesigned low level heuristic at each step to construct a solution for a given problem. Constraint Satisfaction Problems (CSP) are well know NP complete problems. In this study, behaviours of two variable ordering heuristics MaxConflicts (MXC) and Saturation Degree (SD) with respect to various combinations of constraint density and tightness values are investigated in depth over a set of random CSP instances. The empirical results show that the performance of these two heuristics are somewhat complementary and they vary for changing constraint density and tightness value pairs. The outcome is used to design three hyperheuristics using MXC and SD as low level heuristics to construct a solution for unseen CSP instances. It has been observed that these hyperheuristics improve the performance of individual low level heuristics even further in terms of mean consistency checks for some CSP instances. I.
MonteCarlo Tree Search in Production Management Problems
"... Classical search algorithms rely on the existence of a sufficiently powerful evaluation function for nonterminal states. In many task domains, the development of such an evaluation function requires substantial effort and domain knowledge, or is not even possible. As an alternative in recent years, ..."
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Classical search algorithms rely on the existence of a sufficiently powerful evaluation function for nonterminal states. In many task domains, the development of such an evaluation function requires substantial effort and domain knowledge, or is not even possible. As an alternative in recent years, MonteCarlo evaluation has been succesfully applied in such task domains. In this paper, we apply a search algorithm based on MonteCarlo evaluation, MonteCarlo Tree Search, in the task domain of production management problems. These can be defined as singleagent problems which consist of selecting a sequence of actions with side effects, leading to high quantities of one or more goal products. They are challenging and can be constructed with highly variable difficulty. Earlier research yielded an offline learning algorithm that leads to good solutions, but requires a long time to run. We show that MonteCarlo Tree Search leads to a solution in a shorter period of time than this algorithm, with improved solutions for large problems. Our findings can be generalized to other task domains. 1
Controlling crossover in a selection hyperheuristic framework
, 2011
"... Abstract. In evolutionary algorithms, crossover is used to recombine two candidate solutions to yield a new solution which hopefully inherits good material from both. Hyperheuristics are highlevel search methodologies which operate on a search space of heuristics. Hyperheuristics can be broadly s ..."
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Abstract. In evolutionary algorithms, crossover is used to recombine two candidate solutions to yield a new solution which hopefully inherits good material from both. Hyperheuristics are highlevel search methodologies which operate on a search space of heuristics. Hyperheuristics can be broadly split into two categories; heuristic selection and generation methodologies. Here we will investigate hyperheuristics from the former category. Selection hyperheuristics select a heuristic to apply from an existing set of lowlevel heuristics at a given point in the search. Crossover is increasingly being included in general purpose hyperheuristic frameworks such as HyFlex and Hyperion however little work has been done to assess how best to utilise it. Since a singlepoint search hyperheuristic operates on a single candidate solution and two candidate solutions are needed for crossover, a mechanism is required to control the choice of the other solution. We propose a framework which maintains a list of potential solutions for use in crossover. We investigate the control of such lists at two levels. Firstly, crossover is controlled at the hyperheuristic level where no problem speci c information is required. Secondly, it is controlled at the problem domain level where problem speci c information is used to produce good quality solutions to use for crossover. A number of selection hyperheuristics are tested over three wellknown benchmark libraries for an NPhard optimisation problem; the multidimensional 01 knapsack problem (MKP). Exact solvers such as CPLEX also use heuristics and have improved signi cantly since the last published application to some of the benchmark data. New results are presented using CPLEX 12.2 over the benchmark instances. *Corresponding author
Memory Length in Hyperheuristics: An Empirical Study
 CISCHED 2007
, 2007
"... Hyperheuristics are an emergent optimisation methodology which aims to give a higher level of flexibility and domainindependence than is currently possible. Hyperheuristics are able to adapt to the different problems or problem instances by dynamically choosing between heuristics during the sear ..."
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Hyperheuristics are an emergent optimisation methodology which aims to give a higher level of flexibility and domainindependence than is currently possible. Hyperheuristics are able to adapt to the different problems or problem instances by dynamically choosing between heuristics during the search. This paper is concerned with the issues of memory length on the performance of hyperheuristics. We focus on a recently proposed simulated annealing hyperheuristic and choose a set of hard university course timetabling problems as the test bed for this empirical study. The experimental results show that the memory length can affect the performance of hyperheuristics and a good choice of memory length is able to improve solution quality. Finally, two dynamic approaches are investigated and one of the approaches is shown to be able to produce promising results without introducing extra sensitive algorithmic parameters.
Machine learning in hybrid hierarchical and partialorder planners for manufacturing domains
 Applied Artificial Intelligence
, 2005
"... systems is being widely deployed for all the tasks involved in the process, from product design to production planning and control. One of these problems is the automatic generation of control sequences for the entire manufacturing system in such a way that final plans can be directly used as the se ..."
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Cited by 5 (4 self)
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systems is being widely deployed for all the tasks involved in the process, from product design to production planning and control. One of these problems is the automatic generation of control sequences for the entire manufacturing system in such a way that final plans can be directly used as the sequential control programs which drive the operation of manufacturing systems. Hybis is a hierarchical and nonlinear planner whose goal is to obtain partially ordered plans at such a level of detail that they can be used as sequential control programs for manufacturing systems. Currently, those sequential control programs are being generated by hand using modelling tools. This document describes a work whose aim is to improve the efficiency of solving problems with Hybis by using machine learning techniques. It implements a deductive learning method that is able to automatically acquire control knowledge (heuristics) by generating bounded explanations of the problem solving episodes. The learning approach builds on Hamlet, a system that learns control knowledge in the form of control rules. 1
Evolving Algorithms for Constraint Satisfaction
, 2004
"... This paper proposes a framework for automatically evolving constraint satisfaction algorithms using genetic programming. The aim is to overcome the difficulties associated with matching algorithms to specific constraint satisfaction problems. A representation is introduced that is suitable for genet ..."
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Cited by 4 (0 self)
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This paper proposes a framework for automatically evolving constraint satisfaction algorithms using genetic programming. The aim is to overcome the difficulties associated with matching algorithms to specific constraint satisfaction problems. A representation is introduced that is suitable for genetic programming and that can handle both complete and local search heuristics. In addition, the representation is shown to have considerably more flexibility than existing alternatives, being able to discover entirely new heuristics and to exploit synergies between heuristics. In a preliminary empirical study, it is shown that the new framework is capable of evolving algorithms for solving the wellstudied problem of boolean satisfiability testing.
A.: Evolving VariableOrdering heuristics for constrained optimisation
 In: CP
, 2005
"... Abstract. In this paper we present and evaluate an evolutionary approach for learning new constraint satisfaction algorithms, specifically for MAXSAT optimisation problems. Our approach offers two significant advantages over existing methods: it allows the evolution of more complex combinations of ..."
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Abstract. In this paper we present and evaluate an evolutionary approach for learning new constraint satisfaction algorithms, specifically for MAXSAT optimisation problems. Our approach offers two significant advantages over existing methods: it allows the evolution of more complex combinations of heuristics, and; it can identify fruitful synergies among heuristics. Using four different classes of MAXSAT problems, we experimentally demonstrate that algorithms evolved with this method exhibit superior performance in comparison to general purpose methods. 1