Our goal is to automatically generate heuristics to guide state space search. Our heuristics are deened by distances in an abstract space that is automatically derived from the deenition of the original space. Such abstractions provide monotone heuristics for search algorithms such as A* and IDA*. To facilitate the automatic generation of heuristics we introduce a notation for describing states...

Hyperheuristics give us the appealing possibility of abstracting the solution method from the problem, since our hyperheuristic, at each decision point, chooses between different low level heuristics rather than different solutions as is usually the case for metaheuristics. By assembling low level heuristics from parameterised components we may create hundreds or thousands of low level heuristi...

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 heuristics. On each iteration, the Monte Carlo h...

Extensive research has been devoted to economic production quantity (EPQ) problem. However, little atten-tion has been paid to problems where depreciation cost and process quality cost must be considered, simulta-neously. In this paper, we consider the economic production quantity model of minimizing the annual total cost subject to depreciation cost and process quality cost, where depreciation...

Malware authors evade the signature based detection by packing the original malware using custom packers. In this paper, we present a static heuristics based approach for the detection of packed executables. We present 1) the PE heuristics considered for analysis and taxonomy of heuristics; 2) a method for computing the score using power distance based on weights and risks assigned to the defin...

An effective approach for learning Bayesian network structures is to perform a local search on the space of topological orderings, followed by a systematic search of compatible parent sets. Typically, the local search is initialized with an ordering generated uniformly at random. This can lead to poor local optima, slow down convergence and hurt the performance of the method. In this work we de...

Along several years, Nokia has utilized user experience heuristics for assessing design prototypes with a panel of internal experts. In this paper, we describe how we have tailored the traditional heuristic evaluation method to suit the needs of a large multinational corporate. HEURISTIC EVALUATION Heuristic evaluation method [3] has traditionally been used for evaluating usability against a se...

In this paper we propose a new tabu search hyperheuristic which makes individual low level heuristics tabu dynamically using an analogy with the Binary Exponential Back Off (BEBO) method used in network communication. We compare this method to a reduced Variable Neighbourhood Search (rVNS), greedy and random hyperheuristic approaches and other tabu search based heuristics for a complex real wor...