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Adaptively Parameterised Hyperheuristics for Sales Summit Scheduling
, 2001
"... The concept of a hyperheuristic was recently proposed by the authors as an approach that operates at a higher level of abstraction than current metaheuristic approaches. The hyperheuristic chooses which lowlevel heuristic to apply depending upon the characteristics of the part of the search space b ..."
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Cited by 3 (2 self)
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The concept of a hyperheuristic was recently proposed by the authors as an approach that operates at a higher level of abstraction than current metaheuristic approaches. The hyperheuristic chooses which lowlevel heuristic to apply depending upon the characteristics of the part of the search space being explored and the performance history of each lowlevel heuristic. In this paper we present various hyperheuristic and heuristic methods and demonstrate their e ectiveness by way of a realworld casestudy problem of scheduling. In particular, we describe a selfadaptive hyperheuristic which uses a choice function to rank the lowlevel heuristics. Results produced by the choice function hyperheuristic appear to be not only superior to those produced by other heuristic methods, but also of much higher quality than those obtained from the greedy heuristic currently used by the problem owner.
Hyperheuristics with a Dynamic Heuristic Set for the Home Care Scheduling Problem
"... A hyperheuristic performs search over a set of other search mechanisms. During the search, it does not require any problemdependent data. This structure makes hyperheuristics problemindependent indirect search mechanisms. In this study, we propose a learning strategy to explore elite heuristic ..."
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A hyperheuristic performs search over a set of other search mechanisms. During the search, it does not require any problemdependent data. This structure makes hyperheuristics problemindependent indirect search mechanisms. In this study, we propose a learning strategy to explore elite heuristic subsets for different phases of a search. For that purpose, we apply a number of hyperheuristics with the proposed approach to a set of home care scheduling problem instances. The results show that the learning strategy increases the performance of the different hyperheuristics by excluding some heuristics from the heuristic set over the tested problem instances.
Toward Spoken Dialogue as Mutual Agreement
"... This paper reenvisions humanmachine dialogue as a set of mutual agreements between a person and a computer. The intention is to provide the person with a habitable experience that accomplishes her goals, and to provide the computer with sufficient flexibility and intuition to support them. The app ..."
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This paper reenvisions humanmachine dialogue as a set of mutual agreements between a person and a computer. The intention is to provide the person with a habitable experience that accomplishes her goals, and to provide the computer with sufficient flexibility and intuition to support them. The application domain is particularly challenging: for its vocabulary size, for the number and variety of its speakers, and for the complexity and number of the possible instantiations of the objects under discussion. The brittle performance of a traditional spoken dialogue system in such a domain motivates the design of a new, more robust social system, one where dialogue is necessarily represented on a variety of different levels.
Hyperheuristics for Performance Optimization of Simultaneous Multithreaded Processors
"... Abstract. In Simultaneous MultiThreaded (SMT) processor datapaths, there are many datapath resources that are shared by multiple threads. Currently, there are a few heuristics that distribute these resources among threads for better performance. A selection hyperheuristic is a search method which ..."
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Abstract. In Simultaneous MultiThreaded (SMT) processor datapaths, there are many datapath resources that are shared by multiple threads. Currently, there are a few heuristics that distribute these resources among threads for better performance. A selection hyperheuristic is a search method which mixes a fixed set of heuristics to exploit their strengths while solving a given problem. In this study, we propose learning selection hyperheuristics for predicting, choosing and running the best performing heuristic. Our initial test results show that hyperheuristics may improve the performance of the studied workloads by around 2%, on the average. The peak performance improvement is observed to be 41 % over the best performing heuristic, and more than 15 % over all heuristics that are studied. Our best hyperheuristic performs better than the stateofthe art heuristics on almost 60 % of the simulated workloads. 1
Monte Carlo hyperheuristics for . . .
, 2010
"... 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, bu ..."
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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
Integrating Probabilistic Reasoning with Constraint Satisfaction
, 2011
"... We hypothesize and confirm that probabilistic reasoning is closely related to constraint satisfaction at a formal level, and that this relationship yields effective algorithms for guiding constraint satisfaction and constraint optimization solvers. By taking a unified view of probabilistic inference ..."
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We hypothesize and confirm that probabilistic reasoning is closely related to constraint satisfaction at a formal level, and that this relationship yields effective algorithms for guiding constraint satisfaction and constraint optimization solvers. By taking a unified view of probabilistic inference and constraint reasoning in terms of graphical models, we first associate a number of formalisms and techniques between the two areas. For instance, we characterize search and inference in constraint reasoning as summation and multiplication (or disjunction and conjunction) in the probabilistic space; necessary but insufficient consistency conditions for solutions to constraint problems (like arcconsistency) mirror approximate objective functions over probability distributions (like the Bethe free energy); and the polytope of feasible points for marginal probabilities represents the linear relaxation of a particular constraint satisfaction problem. While such insights synthesize an assortment of existing formalisms from varied research communities, they also yield an entirely novel set of “bias estimation” techniques that contribute to a growing body of research on applying probabilistic methods to constraint problems. In practical terms, these techniques estimate the percentage of solutions to a constraint
Algorithm Selection for Search: A survey Algorithm Selection for Combinatorial Search Problems: A survey
"... Abstract The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a casebycase basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solv ..."
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Abstract The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a casebycase basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where Algorithm Selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine Algorithm Selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which Algorithm Selection has been approached. This paper contrasts and compares different methods for solving the problem as well as ways of using these solutions. It closes by identifying directions of current and future research.
An Antbased Selection Hyperheuristic for Dynamic Environments
"... Abstract. Dynamic environment problems require adaptive solution methodologies which can deal with the changes in the environment during the solution process for a given problem. A selection hyperheuristic manages a set of low level heuristics (operators) and decides which one to apply at each iter ..."
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Abstract. Dynamic environment problems require adaptive solution methodologies which can deal with the changes in the environment during the solution process for a given problem. A selection hyperheuristic manages a set of low level heuristics (operators) and decides which one to apply at each iterative step. Recent studies show that selection hyperheuristic methodologies are indeed suitable for solving dynamic environment problems with their ability of tracking the change dynamics in a given environment. The choice function based selection hyperheuristic is reported to be the best hyperheuristic on a set of benchmark problems. In this study, we investigate the performance of a new learning hyperheuristic and its variants which are inspired from the ant colony optimisation algorithm components. The proposed hyperheuristic maintains a matrix of pheromone intensities (utility values) between all pairs of low level heuristics. A heuristic is selected based on the utility values between the previously invoked heuristic and each heuristic from the set of low level heuristics. The antbased hyperheuristic performs better than the choice function and even its improved version across a variety of dynamic environments produced by the Moving Peaks Benchmark generator. 1
Software Module Clustering using a Fast Multiobjective Hyperheuristic Evolutionary Algorithm
"... Software evolution is a natural phenomenon in the software development life cycle. As the software evolves, the modular structure of software degrades, and at one point it becomes a challenging task to maintain the software further. Software module clustering is an important activity during software ..."
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Software evolution is a natural phenomenon in the software development life cycle. As the software evolves, the modular structure of software degrades, and at one point it becomes a challenging task to maintain the software further. Software module clustering is an important activity during software maintenance whose main goal is to obtain good modular structures. Software engineers greatly emphasize on good modular structures as it is easier to comprehend, develop and maintain such software systems. In recent times, the problem has been converted into a Searchbased Software Engineering Problem with multiple objectives. This problem is NP hard as it is an instance of graph partitioning and hence cannot be solved using traditional optimization techniques. The Multiobjective Hyperheuristic Evolutionary Algorithm (MHypEA) is a fast and effective metaheuristic search technique for suggesting software module clusters while maximizing cohesion and minimizing coupling of the software modules. It incorporates twelve lowlevel heuristics which are based on different methods of selection, crossover and mutation operations of Evolutionary Algorithms. The selection mechanism to select a lowlevel heuristic is based on reinforcement learning with adaptive weights. The effectiveness of the algorithm has been studied on six realworld module clustering problems reported in the literature and the comparison of the results prove the efficacy of the MHypEA in terms of quality of solutions and computational time.