Peter Ross

Evolution of constraint satisfaction strategies in examination timetabling

Evolutionary Computation in Combinatorial Optimization

Lecture Notes in Computer Science, 2007

A New Local Search Algorithm for the DNA Fragment Assembly Problem.- A Hybrid Immune-Based System... more A New Local Search Algorithm for the DNA Fragment Assembly Problem.- A Hybrid Immune-Based System for the Protein Folding Problem.- A Genetic Algorithm for the Resource Renting Problem with Minimum and Maximum Time Lags.- A Probabilistic Beam Search Approach to the Shortest Common Supersequence Problem.- Genetic Algorithms for Word Problems in Partially Commutative Groups.- A GRASP and Branch-and-Bound Metaheuristic for the Job-Shop Scheduling.- Reducing the Size of Traveling Salesman Problem Instances by Fixing Edges.- Iterated k-Opt Local Search for the Maximum Clique Problem.- Accelerating Local Search in a Memetic Algorithm for the Capacitated Vehicle Routing Problem.- Evolutionary Algorithms for Real-World Instances of the Automatic Frequency Planning Problem in GSM Networks.- A New Metaheuristic for the Vehicle Routing Problem with Split Demands.- Generation of Tree Decompositions by Iterated Local Search.- Edge Assembly Crossover for the Capacitated Vehicle Routing Problem.- Tackling the Container Loading Problem: A Hybrid Approach Based on Integer Linear Programming and Genetic Algorithms.- A Population-Based Local Search for Solving a Bi-objective Vehicle Routing Problem.- Combining Lagrangian Decomposition with an Evolutionary Algorithm for the Knapsack Constrained Maximum Spanning Tree Problem.- Exact/Heuristic Hybrids Using rVNS and Hyperheuristics for Workforce Scheduling.- An Analysis of Problem Difficulty for a Class of Optimisation Heuristics.- A New Grouping Genetic Algorithm for the Quadratic Multiple Knapsack Problem.- A Hybrid Method for Solving Large-Scale Supply Chain Problems.- Crossover Operators for the Car Sequencing Problem.

Hyper-Heuristics: An Emerging Direction in Modern Search Technology

Handbook of Metaheuristics

This chapter introduces and overviews an emerging methodology in search and optimisation. One of ... more This chapter introduces and overviews an emerging methodology in search and optimisation. One of the key aims of these new approaches, which have been termed hyper-heuristics, is to raise the level of generality at which optimisation systems can operate. An objective is that hyper-heuristics will lead to more general systems that are able to handle a wide range of problem domains rather than current meta-heuristic technology which tends to be customised to a particular problem or a narrow class of problems. Hyperheuristics are broadly concerned with intelligently choosing the right heuristic or algorithm in a given situation. Of course, a hyper-heuristic can be (often is) a (meta-)heuristic and it can operate on (meta-)heuristics. In a certain sense, a hyper-heuristic works at a higher level when compared with the typical application of meta-heuristics to optimisation problems i.e. a hyper-heuristic could be thought of as a (meta)-heuristic which operates on lower level (meta-)heuristics. In this chapter we will introduce the idea and give a brief history of this emerging area. In addition, we will review some of the latest work to be published in the field.

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Solving a Real-World Problem Using an Evolving Heuristically Driven Schedule Builder

Evolutionary Computation, 1998

This work addresses the real-life scheduling problem of a Scottish company that must produce dail... more This work addresses the real-life scheduling problem of a Scottish company that must produce daily schedules for the catching and transportation of large numbers of live chickens. The problem is complex and highly constrained. We show that it can be successfully solved by division into two subproblems and solving each using a separate genetic algorithm (GA). We address the problem of whether this produces locally optimal solutions and how to overcome this. We extend the traditional approach of evolving a “permutation + schedule builder” by concentrating on evolving the schedule builder itself. This results in a unique schedule builder being built for each daily scheduling problem, each individually tailored to deal with the particular features of that problem. This results in a robust, fast, and flexible system that can cope with most of the circumstances imaginable at the factory. We also compare the performance of a GA approach to several other evolutionary methods and show that p...

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Solving a Real-World Problem Using an Evolving Heuristically Driven Schedule Builder

Evolutionary Computation, 1998

This work addresses the real-life scheduling problem of a Scottish company that must produce dail... more This work addresses the real-life scheduling problem of a Scottish company that must produce daily schedules for the catching and transportation of large numbers of live chickens. The problem is complex and highly constrained. We show that it can be successfully solved by division into two subproblems and solving each using a separate genetic algorithm (GA). We address the problem of whether this produces locally optimal solutions and how to overcome this. We extend the traditional approach of evolving a “permutation + schedule builder” by concentrating on evolving the schedule builder itself. This results in a unique schedule builder being built for each daily scheduling problem, each individually tailored to deal with the particular features of that problem. This results in a robust, fast, and flexible system that can cope with most of the circumstances imaginable at the factory. We also compare the performance of a GA approach to several other evolutionary methods and show that p...

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Solving a Real-World Problem Using an Evolving Heuristically Driven Schedule Builder

Evolutionary Computation, 1998

This work addresses the real-life scheduling problem of a Scottish company that must produce dail... more This work addresses the real-life scheduling problem of a Scottish company that must produce daily schedules for the catching and transportation of large numbers of live chickens. The problem is complex and highly constrained. We show that it can be successfully solved by division into two subproblems and solving each using a separate genetic algorithm (GA). We address the problem of whether this produces locally optimal solutions and how to overcome this. We extend the traditional approach of evolving a “permutation + schedule builder” by concentrating on evolving the schedule builder itself. This results in a unique schedule builder being built for each daily scheduling problem, each individually tailored to deal with the particular features of that problem. This results in a robust, fast, and flexible system that can cope with most of the circumstances imaginable at the factory. We also compare the performance of a GA approach to several other evolutionary methods and show that p...

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A promising hybrid GA/heuristic approach for open-shop scheduling problems

Many problems in industry are a form of openshop scheduling problem (OSSP). We describe a hybrid ... more Many problems in industry are a form of openshop scheduling problem (OSSP). We describe a hybrid approach to this problem which combines a Genetic Algorithm (GA) with simple heuristic schedule building rules. Excellent performance is found on some benchmark OSS problems, including improvements on previous best-known results. We describe how our approach can be simply amended to deal with the more complex style of open shop scheduling problems which occur in industry, and discuss issues relating to further improvement of performance and integration of the approach into industrial job shop environments.

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Hyper-heuristics

Evolutionary Computation In Combinatorial Optimization

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Microcomputers in secondary education. Issues and techniques

International Journal of Educational Development

sort of detail that would enable the reader to unde;stand, for example, how the concrete operatio... more sort of detail that would enable the reader to unde;stand, for example, how the concrete operational/ formal operational distinction applies to sexual thinking); a reappraisal of Freud's Oedipal concept and the 'latency period' in children's sexuality; a general 'retardation' in children's biological and sexual thinking in all country samples except the Swedish; and the home as the major source of sex information. Cross-cultural differences in levels of sexual thinking were attributed post hoc to cultural and educational factors. The more advanced thinking of Swedish children is attributed to their longer and sustained sex education and more open attitudes to sexuality in Swedish society. Their findings on other socio-cultural factors affecting advanced levels of sexual thinking within cultures were several, including educated, or working mothers, at least one opposite sex sibling in a family of three or more children, living in a two parent family, and being exposed to a systematic school sex education programme in the primary school.

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Dynamic training subset selection for supervised learning in Genetic Programming

Lecture Notes in Computer Science, 1994

... The setup which generates the fittest classifier with respect to the training set which then ... more

Combining choices of heuristics

Useful artificial intelligence tools- a review of heuristic search methods

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Exploiting the analogy between the immune system and sparse distributed memory

The relationship between immunological memory and a class of associative memories known as sparse... more The relationship between immunological memory and a class of associative memories known as sparse distributed memories (SDM) is well known. This paper proposes a new model for clustering nonstationary data based on a combination of salient features from the two metaphors. The resulting system embodies the important principles of both types of memory; it is self-organising, robust, scalable, dynamic and can perform anomaly detection, and is shown to be a more faithful model of the biological system than a standard SDM. The model is first applied to clustering static benchmark data-sets, and is shown to outperform another system based on immunological principles. It is then applied to clustering nonstationary data-sets with promising results. The system is also shown to be scalable therefore is of potential for clustering real-world data-sets.

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Enhancing the performance of a GA through visualisation

This article describes a new tool for visualising genetic algorithms, (GAs) which i s d esigned i... more This article describes a new tool for visualising genetic algorithms, (GAs) which i s d esigned in order to allow the implicit mechanisms of the GA | i.e. crossover and mutation | to be thoroughly analysed. This allows the user to determine whether these mechanisms are essential to a GAs performance, and if so, to provide a principled means of setting the parameters associated with them, based on a sound understanding of their e ects. The use of the tool is illustrated by applying to the analysis of a jobshop scheduling problem, in order to choose e ective operators, and to determine appropriate settings for them. We show that by analysing two crossover operators and a mutation operator, we can re ne the choice and settings of these parameters in order to improve the performance of the GA on the particular problem chosen. When the new operators are applied to a wider range of problems of the same type, a similar improvement in performance is observed.

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A Promising Genetic Algorithm Approach to Job-Shop Scheduling, Rescheduling, and Open-Shop Scheduling Problems

... This performance enhancement method complements those discussed in, for example, (Booker 1987... more ... This performance enhancement method complements those discussed in, for example, (Booker 1987) and (Eshelman &amp;amp;amp; Scha er 1991), which present ways of improving performance by, eg, encouraging recombi-nation between adequately `di erent&amp;amp;#x27; genomes (incest prevention ...

Solving vehicle routing problems using different multiple ant colony systems

Gavel - a new tool for genetic algorithm visualisation

Dynamic Variable Ordering for Hard Binary Constraint Satisfaction Problems by Means of Hyper-Heuristics

GAVEL - a new tool for genetic algorithm visualization

Ieee Transactions on Evolutionary Computation, Aug 1, 2001

This paper surveys the state of the art in evolutionary algorithm visualization and describes a n... more This paper surveys the state of the art in evolutionary algorithm visualization and describes a new tool called GAVEL. It provides a means to examine in a generational genetic algorithm (GA) how crossover and mutation operations assembled the final result, where each of the alleles came from, and a way to trace the history of user-selected sets of alleles. A visualization tool of this kind can be very useful in choosing operators and parameters and in analyzing how and, indeed, whether or not a GA works. We describe the new tool and illustrate some of the benefits that can be gained from using it with reference to three different problems: a timetabling problem, a jobshop scheduling problem, and Goldberg and Horn's long-path problem. We also compare the tool to other available visualization tools, pointing out those features which are novel and identifying complementary features in other tools.

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