Graham Kendall

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The concept of a hyperheuristic is introduced as an approach that operates at a higher lever of abstraction than current metaheuristic approaches. The hyperheuristic manages the choice of which lowerlevel heuristic method should be applied at any given time, depending upon the characteristics of the region of the solution space currently under exploration.(More)
Hyperheuristics can be defined to be heuristics which choose between heuristics in order to solve a given optimisation problem. The main motivation behind the development of such approaches is the goal of developing automated scheduling methods which are not restricted to one problem. In this paper we report the investigation of a hyperheuristic approach(More)
This paper presents a new best-fit heuristic for the two-dimensional rectangular stock-cutting problem and demonstrates its effectiveness by comparing it against other published approaches. A placement algorithm usually takes a list of shapes, sorted by some property such as increasing height or decreasing area, and then applies a placement rule to each of(More)
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 hyperheuristic is relatively new; it was first used in(More)
1. INTRODUCTION The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self-cells or non-self cells. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global(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(More)
Hyper-heuristics comprise a set of approaches that share the common goal of automating the design and tuning of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. The term hyper-heuristic was coined in the early 2000’s to refer to the idea of ‘heuristics to choose(More)
This paper examines measures of diversity in genetic programming. The goal is to understand the importance of such measures and their relationship with fitness. Diversity methods and measures from the literature are surveyed and a selected set of measures are applied to common standard problem instances in an experimental study. Results show the varying(More)
Automating the design of heuristic search methods is an active research field within computer science, artificial intelligence and operational research. In order to make these methods more generally applicable, it is important to eliminate or reduce the role of the human expert in the process of designing an effective methodology to solve a given(More)
In this paper we introduce a Monte Carlo based hyper-heuristic. The Monte Carlo hyper-heuristic manages a set of low level heuristics (in this case just simple 2-opt 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(More)