Neural Network approaches to time series prediction are briefly discussed, and the need to find the appropriate sample rate and an appropriately sized input window identified. Relevant theoretical results from dynamic systems theory are briefly introduced, and heuristics for finding the appropriate sampling rate and embedding dimension, and thence window size, are discussed. The method is appli...

AIGSolve is a rewriting-based solver based on And-Inverter Graphs (AIGs). In this approach, quantifiers are eliminated, starting with the inner-most quantifiers. Intermediate results are represented symbolically using AIGs [22, 23]. The basic method consists of cofactorbased quantifier elimination which is combined with a multitude of optimization approaches including a SATand BDD-based compact...

Cost partitioning is a general and principled approach for constructing additive admissible heuristics for state-space search. Cost partitioning approaches for optimal classical planning include optimal cost partitioning, uniform cost partitioning, zero-one cost partitioning, saturated cost partitioning, post-hoc optimization and the canonical heuristic for pattern databases. We compare these a...

In many types of planning algorithms distance heuristics play an important role. Most of the earlier works restrict to STRIPS operators, and their application to a more general language with disjunctivity and conditional effects first requires an exponential size reduction to STRIPS operators. I present direct formalizations of a number of distance heuristics for a general operator description ...

Many real-world problems can be expressed in terms of states and actions that modify the world to reach a certain goal. Such problems can be solved by automated planning. Numeric planning supports numeric quantities such as resources or physical properties in addition to the propositional variables from classical planning. We approach numeric planning with heuristic search and introduce adaptat...

This paper presents a genetic programming (GP) hyper-heuristic approach that optimizes a search space of functions to assess the difficulty of allocating an examination during the timetable construction process. Each function is a heuristic combination of lowlevel construction heuristics combined by logical operators. The approach is tested on a set of five benchmark problems of varying difficu...

This paper presents some improvements that have been made to UNLSumm, an automatic summarizer of UNL Texts. These are texts encoded in the Universal Networking Language (UNL), an artificial language intended to convey conceptualizations of natural language texts. In UNLSumm, a set of heuristics is responsible for, based in the UNL characteristics, prune sentences of a UNL Text, generating its U...

The first Cross-domain Heuristic Search Challenge (CHeSC 2011) is an international research competition aimed at measuring hyperheuristics performance over several problem domains. Hyper-heuristics are new approaches which aim at raising the level of abstraction when solving combinatorial optimisation problems. During this competition, we have applied the Ant-Q hyper-heuristic approach, propose...

Three modifications to the framework within which hyper-heuristic approaches operate are presented. The first modification automates a self learning mechanism for updating the values of parameters in the choice function used by the controller. Second, a procedure for dynamically configuring a range of lowlevel heuristics is described. Third, in order to effectively use this range of low-level h...

The existence of several solution strategies for different search and optimisation problems motivates the use of these strategies in a collaborative way. These collaborative approaches are considered in the context of hybridisation. Hyper-heuristics provide hybridisation by performing search on multiple low-level heuristics for finding efficient solutions in a problem-independent manner. In thi...