This paper extends a recently proposed model for combinatorial landscapes: Local Optima Networks (LON), to incorporate a first-improvement (greedyascent) hill-climbing algorithm, instead of a best-improvement (steepest-ascent) one, for the definition and extraction of the basins of attraction of the landscape optima. A statistical analysis comparing best and first improvement network models for...

The Disjunctive Temporal Problem with Uncertainty (DTPU) is a fundamental problem that expresses temporal reasoning with both disjunctive constraints and contingency. A recent work (Peintner et al, 2007) develops a complete algorithm for determining Strong Controllability of a DTPU. Such a notion that guarantees 100% confidence of execution may be too conservative in practice. In this paper, fo...

Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring search space possible graphs and moving towards graph that maximises given objective function. Some algorithms offer exact guarantee to return highest score, while others approximate in exchange for reduced computational complexity. This paper describes an BN learning algorithm, which we call Model A...

Automated software module clustering is important for maintenance of legacy systems written in a ‘monolithic format’ with inadequate module boundaries. Even where systems were originally designed with suitable module boundaries, structure tends to degrade as the system evolves, making re-modularization worthwhile. This paper focuses upon search-based approaches to the automated module clusterin...

This work studies the vulnerabilities of i-vector based speaker verification systems against indirect attacks. Particularly, we exploit the one-to-one representation of speakers via their corresponding i-vectors to perform Hill-Climbing based attacks; under the hypothesis that the inherent low-dimensionality of ivectors might represent a potential security breach to fraudulently access the syst...

The current evaluation functions for heuristic planning are expensive to compute. In numerous domains these functions give good guidance on the solution, so it worths the computation effort. On the contrary, where this is not true, heuristics planners compute loads of useless node evaluations that make them scale-up poorly. In this paper we present a novel approach for boosting the scalability ...

Even though hill climbing search (HCS) control is the simplest MPPT algorithm that does not require any prior knowledge of the system, it has the disadvantage of being slow in its response. This slowness in the response is due to the number of perturbations involved in climbing the hill and the settling time of the each perturbation. This paper proposes an improved HCS control, in which the nat...

When using Simple Temporal Networks, it may be that not all constraints must be met, rather that they should be met if possible with some degree of preference. To express this, a utility value is added to represent the importance of some temporal constraints. Various search procedures are looked at to try to maximise the total utility whilst still maintaining consistency. It is found that a gre...

In this paper we introduce two novel methods for performing Bayesian network structure search that make use of Gaussian Process regression. Using a relatively small number of samples from the posterior distribution of Bayesian networks, we are able to find an accurate function approximator based on Gaussian Processes. This allows us to remove our dependency on the data during the search and lea...

This paper presents a new algorithm for enhancing the efficiency of simulation-based optimisation using local search and neural network metamodels. The local search strategy is based on steepest ascent Hill Climbing. In contrast to many other approaches that use a metamodel for simulation optimisation, this algorithm alternates between the metamodel and its underlying simulation model, rather t...