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 2000 to describe heuristics to choose heu...

A hyperheuristic is a high level procedure which searches over a space of low level heuristics rather than directly over the space of problem solutions. The sequence of low level heuristics, applied in an order which is intelligently determined by the hyperheuristic, form a solution method for the problem. In this paper, we consider a hyperheuristicbased methodology where a large set of low lev...

Achieving high performance on modern processors heavily relies on the compiler optimizations to exploit the microprocessor architecture. The efficiency of optimization directly depends on the compiler heuristics. These heuristics must be target-specific and each new processor generation requires heuristics reengineering. In this paper, we address the automatic generation of optimization heurist...

The use of heuristics for the evaluation of interfaces is a well studied area. Currently there appear to be two main research areas in relation to heuristics: the analysis of methods to improve the effectiveness of heuristic evaluations; and the development of new heuristic sets for novel and specialised domains. This paper proposes an evidence based design approach to the development of domain...

We present an Evolutionary Programming Hyper-heuristic (EPH) implemented for the Cross-Domain Heuristic Search Challenge 2011. The proposed method combines an evolutionary programming approach and co-evolution. The solving process of EPH consists in evolving a population of solutions by applying heuristics sequences. The heuristics sequences are also in a population and evolve according to thei...

Problem solvers, both human and machine, have at their disposal many heuristics that may support effective search. The efficacy of these heuristics, however, varies with the problem class, and their mutual interactions may not be well understood. The long-term goal of our work is to learn how to select appropriately from among a large body of heuristics, and how to combine them into a mixture t...

Content based image retrieval (CBIR) refers to image content that is retrieved directly, by which the images with features or containing certain contents will be searched in an image database. The main idea of CBIR is to analyze image information by low level features of an image, which includes color, texture, shape and space relationship of objects etc., and to set up feature vectors of an im...

In this paper, we propose and evaluate heuristics to improve the performance of BMW and its variants. The proposed changes maintain the property of preserving the order of the top ranking results, while reduce query processing times and the amount of memory required for processing queries.

Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyper-heuristics 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 do...