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...

Both coarse-to-fine and A∗ parsing use simple grammars to guide search in complex ones. We compare the two approaches in a common, agenda-based framework, demonstrating the tradeoffs and relative strengths of each method. Overall, coarse-to-fine is much faster for moderate levels of search errors, but below a certain threshold A∗ is superior. In addition, we present the first experiments on hie...

Abstract: Probabilistic inference models (e.g. Bayesian models) are often cast as being rational and at odds with simple heuristic approaches. We show that prominent decision heuristics, take-the-best and tallying, are special cases of Bayesian inference. We developed two Bayesian learning models by extending two popular regularized regression approaches, lasso and ridge regression. The priors ...

This paper presents an overview of recent advances for the Nurse Rostering Problem (NRP) based on methodological papers published between 2012 to 2021. It provides a comprehensive review latest solution methodologies, particularly computational intelligence (CI) approaches, utilized in benchmark and real-world nurse rostering. The methodologies are systematically categorised (Heuristics, Meta-h...

Parallel simulation techniques have been investigated in a number of studies as a way to decrease long execution times of sequential simulations. In this work, we propose diierent parallel approaches for a sequential discrete event simulator developed previously to compare diierent strategies for task allocation when processors are subject to failures. The failure-repair mechanism is modeled by...

This workshop paper addresses Heuristic approaches to solving Sudoku puzzles, with particular focus on Particle Swarm Optimisation(PSO). Sudoku problems and their constraints will be discussed. Heuristics used to solve Sudoku will be identified. We will then propose a component framework for supporting PSO and other Heuristics. Doing so allows us to separately detail aspects of PSO such as init...

This paper presents extensive computational experiments to compare 12 heuristics and 2 meta-heuristics for the problem of minimizing straight-line crossings in a 2-layer graph. These experiments show that the performance of the heuristics (largely based on simple ordering rules) drastically deteriorates as the graphs become sparser. A tabu search metaheuristic yields the best results for relati...

Hyper-heuristics are easy-to-implement emerging search and optimisation strategies which have been used instead of meta-heuristics for providing higher level generalized structures to solve mainly combinatorial optimisation problems. The motivation behind them is generally expressed as raising the level of generality. That is, they are the answers to the following question: How can we create or...

Abstract A general enhancement of the Benders’ decomposition (BD) algorithm can be achieved through improved use large neighbourhood search heuristics within mixed-integer programming solvers. While solvers are endowed with an array heuristics, few, if any, have been designed for BD. Further, typically is limited to finding solutions BD master problem. Given lack frameworks BD, only ad hoc appr...