The Experts below are selected from a list of 184359 Experts worldwide ranked by ideXlab platform
Hans Ziegler - One of the best experts on this subject based on the ideXlab platform.
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scheduling to minimize the sum of weighted flowtime and weighted tardiness of jobs in a flowshop with sequence dependent setup times
European Journal of Operational Research, 2003Co-Authors: Chandrasekharan Rajendran, Hans ZieglerAbstract:Abstract Efficient Heuristics for scheduling jobs in a static flowshop with sequence-dependent setup times of jobs are presented in this paper. The objective is to minimize the sum of weighted flowtime and weighted tardiness of jobs. Two heuristic preference relations are used to construct a good heuristic permutation sequence of jobs. Thereafter, an improvement scheme is implemented, once and twice, on the heuristic sequence to enhance the quality of the solution. An existing heuristic, a random search procedure and a greedy local search are used as benchmark methods for relatively evaluating the proposed Heuristics. An extensive performance analysis has shown that the proposed Heuristics are computationally faster and more effective in yielding solutions of better quality than the benchmark procedures.
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an efficient heuristic for scheduling in a flowshop to minimize total weighted flowtime of jobs
European Journal of Operational Research, 1997Co-Authors: Chandrasekharan Rajendran, Hans ZieglerAbstract:The problem of scheduling in a flowshop is considered with the objective of minimizing the total weighted flowtime of jobs. A heuristic algorithm is developed by the introduction of lower bounds on the completion times of jobs and the development of heuristic preference relations for the scheduling problem under study. An improvement scheme is incorporated in the heuristic to enhance the quality of its solution. The proposed heuristic, with and without the improvement scheme, and the existing Heuristics are evaluated by a large number of randomly generated problems. The results of an extensive computational investigation for various problem sizes are presented. It has been observed that both versions of the proposed heuristic perform better than the existing Heuristics in giving a superior solution quality and that the proposed heuristic without the improvement scheme yields a good solution by requiring a negligible CPU time. In addition, an experimental investigation is carried out to evaluate the effectiveness of the improvement scheme when implemented in the proposed heuristic and the existing Heuristics, as well as the effectiveness of a variant of the scheme. The results are also discussed.
Chandrasekharan Rajendran - One of the best experts on this subject based on the ideXlab platform.
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scheduling to minimize the sum of weighted flowtime and weighted tardiness of jobs in a flowshop with sequence dependent setup times
European Journal of Operational Research, 2003Co-Authors: Chandrasekharan Rajendran, Hans ZieglerAbstract:Abstract Efficient Heuristics for scheduling jobs in a static flowshop with sequence-dependent setup times of jobs are presented in this paper. The objective is to minimize the sum of weighted flowtime and weighted tardiness of jobs. Two heuristic preference relations are used to construct a good heuristic permutation sequence of jobs. Thereafter, an improvement scheme is implemented, once and twice, on the heuristic sequence to enhance the quality of the solution. An existing heuristic, a random search procedure and a greedy local search are used as benchmark methods for relatively evaluating the proposed Heuristics. An extensive performance analysis has shown that the proposed Heuristics are computationally faster and more effective in yielding solutions of better quality than the benchmark procedures.
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an efficient heuristic for scheduling in a flowshop to minimize total weighted flowtime of jobs
European Journal of Operational Research, 1997Co-Authors: Chandrasekharan Rajendran, Hans ZieglerAbstract:The problem of scheduling in a flowshop is considered with the objective of minimizing the total weighted flowtime of jobs. A heuristic algorithm is developed by the introduction of lower bounds on the completion times of jobs and the development of heuristic preference relations for the scheduling problem under study. An improvement scheme is incorporated in the heuristic to enhance the quality of its solution. The proposed heuristic, with and without the improvement scheme, and the existing Heuristics are evaluated by a large number of randomly generated problems. The results of an extensive computational investigation for various problem sizes are presented. It has been observed that both versions of the proposed heuristic perform better than the existing Heuristics in giving a superior solution quality and that the proposed heuristic without the improvement scheme yields a good solution by requiring a negligible CPU time. In addition, an experimental investigation is carried out to evaluate the effectiveness of the improvement scheme when implemented in the proposed heuristic and the existing Heuristics, as well as the effectiveness of a variant of the scheme. The results are also discussed.
Edmund K Burke - One of the best experts on this subject based on the ideXlab platform.
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A methodology for determining an effective subset of Heuristics in selection hyper-Heuristics
European Journal of Operational Research, 2017Co-Authors: Jorge A. Soria-alcaraz, Marco A. Sotelo-figeroa, Gabriela Ochoa, Edmund K BurkeAbstract:We address the important step of determining an effective subset of Heuristics in selection hyper-Heuristics. Little attention has been devoted to this in the literature, and the decision is left at the discretion of the investigator. The performance of a hyper-heuristic depends on the quality and size of the heuristic pool. Using more than one heuristic is generally advantageous, however, an unnecessary large pool can decrease the performance of adaptive approaches. Our goal is to bring methodological rigour to this step. The proposed methodology uses non-parametric statistics and fitness landscape measurements from an available set of Heuristics and benchmark instances, in order to produce a compact subset of effective Heuristics for the underlying problem. We also propose a new iterated local search hyper-heuristic using multi-armed bandits coupled with a change detection mechanism. The methodology is tested on two real-world optimization problems: course timetabling and vehicle routing. The proposed hyper-heuristic with a compact heuristic pool, outperforms state-of-the-art hyper-Heuristics and competes with problem-specific methods in course timetabling, even producing new best-known solutions in 5 out of the 24 studied instances.
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a case study of controlling crossover in a selection hyper heuristic framework using the multidimensional knapsack problem
Evolutionary Computation, 2016Co-Authors: John H Drake, Ender Özcan, Edmund K BurkeAbstract:Hyper-Heuristics are high-level methodologies for solving complex problems that operate on a search space of Heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level Heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level Heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-Heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain.
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Hyper-Heuristics: a survey of the state of the art
Journal of the Operational Research Society, 2013Co-Authors: Edmund K Burke, Ender Özcan, Gabriela Ochoa, Michel Gendreau, Matthew Hyde, Graham Kendall, Rong QuAbstract:Hyper-Heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe Heuristics to choose Heuristics in the context of combinatorial optimisation. However, the idea of automating the design of Heuristics is not new; it can be traced back to the 1960s. The definition of hyper-Heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating Heuristics to solve computational search problems . Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation . The distinguishing feature of hyper-Heuristics is that they operate on a search space of Heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-Heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.
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an improved choice function heuristic selection for cross domain heuristic search
Parallel Problem Solving from Nature, 2012Co-Authors: John H Drake, Ender Özcan, Edmund K BurkeAbstract:Hyper-Heuristics are a class of high-level search technologies to solve computationally difficult problems which operate on a search space of low-level Heuristics rather than solutions directly. A iterative selection hyper-heuristic framework based on single-point search relies on two key components, a heuristic selection method and a move acceptance criteria. The Choice Function is an elegant heuristic selection method which scores Heuristics based on a combination of three different measures and applies the heuristic with the highest rank at each given step. Each measure is weighted appropriately to provide balance between intensification and diversification during the heuristic search process. Choosing the right parameter values to weight these measures is not a trivial process and a small number of methods have been proposed in the literature. In this study we describe a new method, inspired by reinforcement learning, which controls these parameters automatically. The proposed method is tested and compared to previous approaches over a standard benchmark across six problem domains.
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dispatching rules for production scheduling a hyper heuristic landscape analysis
Congress on Evolutionary Computation, 2009Co-Authors: Gabriela Ochoa, Jose Antonio Vazquezrodriguez, Sanja Petrovic, Edmund K BurkeAbstract:Hyper-Heuristics or “Heuristics to chose Heuristics” are an emergent search methodology that seeks to automate the process of selecting or combining simpler Heuristics in order to solve hard computational search problems. The distinguishing feature of hyper-Heuristics, as compared to other heuristic search algorithms, is that they operate on a search space of Heuristics rather than directly on the search space of solutions to the underlying problem. Therefore, a detailed understanding of the properties of these heuristic search spaces is of utmost importance for understanding the behaviour and improving the design of hyper-heuristic methods. Heuristics search spaces can be studied using the metaphor of fitness landscapes. This paper formalises the notion of hyper-heuristic landscapes and performs a landscape analysis of the heuristic search space induced by a dispatching-rule-based hyper-heuristic for production scheduling. The studied hyper-heuristic spaces are found to be “easy” to search. They also exhibit some special features such as positional bias and neutrality. It is argued that search methods that exploit these features may enhance the performance of hyper-Heuristics.
Ben Paechter - One of the best experts on this subject based on the ideXlab platform.
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on the synthesis of perturbative Heuristics for multiple combinatorial optimisation domains
Parallel Problem Solving from Nature, 2018Co-Authors: Christopher Stone, Emma Hart, Ben PaechterAbstract:Hyper-heuristic frameworks, although intended to be cross-domain at the highest level, rely on a set of domain-specific low-level Heuristics at lower levels. For some domains, there is a lack of available Heuristics, while for novel problems, no Heuristics might exist. We address this issue by introducing a novel method, applicable in multiple domains, that constructs new low-level Heuristics for a domain. The method uses grammatical evolution to construct iterated local search Heuristics: it can be considered cross-domain in that the same grammar can evolve Heuristics in multiple domains without requiring any modification, assuming that solutions are represented in the same form. We evaluate the method using benchmarks from the travelling-salesman (TSP) and multi-dimensional knapsack (MKP) domain. Comparison to existing methods demonstrates that the approach generates low-level Heuristics that outperform heuristic methods for TSP and are competitive for MKP.
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automatic generation of constructive Heuristics for multiple types of combinatorial optimisation problems with grammatical evolution and geometric graphs
International Conference on the Applications of Evolutionary Computation, 2018Co-Authors: Christopher Stone, Emma Hart, Ben PaechterAbstract:In many industrial problem domains, when faced with a combinatorial optimisation problem, a “good enough, quick enough” solution to a problem is often required. Simple Heuristics often suffice in this case. However, for many domains, a simple heuristic may not be available, and designing one can require considerable expertise. Noting that a wide variety of problems can be represented as graphs, we describe a system for the automatic generation of constructive Heuristics in the form of Python programs by mean of grammatical evolution. The system can be applied seamlessly to different graph-based problem domains, only requiring modification of the fitness function. We demonstrate its effectiveness by generating Heuristics for the Travelling Salesman and Multi-Dimensional Knapsack problems. The system is shown to be better or comparable to human-designed Heuristics in each domain. The generated Heuristics can be used ‘out-of-the-box’ to provide a solution, or to augment existing hyper-heuristic algorithms with new low-level Heuristics.
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EvoApplications - Automatic Generation of Constructive Heuristics for Multiple Types of Combinatorial Optimisation Problems with Grammatical Evolution and Geometric Graphs
Applications of Evolutionary Computation, 2018Co-Authors: Christopher Stone, Emma Hart, Ben PaechterAbstract:In many industrial problem domains, when faced with a combinatorial optimisation problem, a “good enough, quick enough” solution to a problem is often required. Simple Heuristics often suffice in this case. However, for many domains, a simple heuristic may not be available, and designing one can require considerable expertise. Noting that a wide variety of problems can be represented as graphs, we describe a system for the automatic generation of constructive Heuristics in the form of Python programs by mean of grammatical evolution. The system can be applied seamlessly to different graph-based problem domains, only requiring modification of the fitness function. We demonstrate its effectiveness by generating Heuristics for the Travelling Salesman and Multi-Dimensional Knapsack problems. The system is shown to be better or comparable to human-designed Heuristics in each domain. The generated Heuristics can be used ‘out-of-the-box’ to provide a solution, or to augment existing hyper-heuristic algorithms with new low-level Heuristics.
Gabriela Ochoa - One of the best experts on this subject based on the ideXlab platform.
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A methodology for determining an effective subset of Heuristics in selection hyper-Heuristics
European Journal of Operational Research, 2017Co-Authors: Jorge A. Soria-alcaraz, Marco A. Sotelo-figeroa, Gabriela Ochoa, Edmund K BurkeAbstract:We address the important step of determining an effective subset of Heuristics in selection hyper-Heuristics. Little attention has been devoted to this in the literature, and the decision is left at the discretion of the investigator. The performance of a hyper-heuristic depends on the quality and size of the heuristic pool. Using more than one heuristic is generally advantageous, however, an unnecessary large pool can decrease the performance of adaptive approaches. Our goal is to bring methodological rigour to this step. The proposed methodology uses non-parametric statistics and fitness landscape measurements from an available set of Heuristics and benchmark instances, in order to produce a compact subset of effective Heuristics for the underlying problem. We also propose a new iterated local search hyper-heuristic using multi-armed bandits coupled with a change detection mechanism. The methodology is tested on two real-world optimization problems: course timetabling and vehicle routing. The proposed hyper-heuristic with a compact heuristic pool, outperforms state-of-the-art hyper-Heuristics and competes with problem-specific methods in course timetabling, even producing new best-known solutions in 5 out of the 24 studied instances.
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Grammar-based generation of variable-selection Heuristics for constraint satisfaction problems
Genetic Programming and Evolvable Machines, 2016Co-Authors: Alejandro Sosa-ascencio, Gabriela Ochoa, Hugo Terashima-marin, Santiago Enrique Conant-pablosAbstract:We propose a grammar-based genetic programming framework that generates variable-selection Heuristics for solving constraint satisfaction problems. This approach can be considered as a generation hyper-heuristic. A grammar to express Heuristics is extracted from successful human-designed variable-selection Heuristics. The search is performed on the derivation sequences of this grammar using a strongly typed genetic programming framework. The approach brings two innovations to grammar-based hyper-Heuristics in this domain: the incorporation of if-then-else rules to the function set, and the implementation of overloaded functions capable of handling different input dimensionality. Moreover, the heuristic search space is explored using not only evolutionary search, but also two alternative simpler strategies, namely, iterated local search and parallel hill climbing. We tested our approach on synthetic and real-world instances. The newly generated Heuristics have an improved performance when compared against human-designed Heuristics. Our results suggest that the constrained search space imposed by the proposed grammar is the main factor in the generation of good Heuristics. However, to generate more general Heuristics, the composition of the training set and the search methodology played an important role. We found that increasing the variability of the training set improved the generality of the evolved Heuristics, and the evolutionary search strategy produced slightly better results.
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Hyper-Heuristics: a survey of the state of the art
Journal of the Operational Research Society, 2013Co-Authors: Edmund K Burke, Ender Özcan, Gabriela Ochoa, Michel Gendreau, Matthew Hyde, Graham Kendall, Rong QuAbstract:Hyper-Heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe Heuristics to choose Heuristics in the context of combinatorial optimisation. However, the idea of automating the design of Heuristics is not new; it can be traced back to the 1960s. The definition of hyper-Heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating Heuristics to solve computational search problems . Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation . The distinguishing feature of hyper-Heuristics is that they operate on a search space of Heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-Heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.
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dispatching rules for production scheduling a hyper heuristic landscape analysis
Congress on Evolutionary Computation, 2009Co-Authors: Gabriela Ochoa, Jose Antonio Vazquezrodriguez, Sanja Petrovic, Edmund K BurkeAbstract:Hyper-Heuristics or “Heuristics to chose Heuristics” are an emergent search methodology that seeks to automate the process of selecting or combining simpler Heuristics in order to solve hard computational search problems. The distinguishing feature of hyper-Heuristics, as compared to other heuristic search algorithms, is that they operate on a search space of Heuristics rather than directly on the search space of solutions to the underlying problem. Therefore, a detailed understanding of the properties of these heuristic search spaces is of utmost importance for understanding the behaviour and improving the design of hyper-heuristic methods. Heuristics search spaces can be studied using the metaphor of fitness landscapes. This paper formalises the notion of hyper-heuristic landscapes and performs a landscape analysis of the heuristic search space induced by a dispatching-rule-based hyper-heuristic for production scheduling. The studied hyper-heuristic spaces are found to be “easy” to search. They also exhibit some special features such as positional bias and neutrality. It is argued that search methods that exploit these features may enhance the performance of hyper-Heuristics.
