Timothy Rawlins

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In existing work, Artificial Neural Networks (ANNs) are often used to model objective functions for Multi-Objective Particle Swarm Optimisation (MOPSO) or MOPSO is used to aid in ANN-training. We instead use an ANN to guide the optimisation algorithm by deciding if a trial solution is worthy of full evaluation. This should be particularly helpful for(More)
A potential area of difficulty for Multi-Objective Optimisation of industrial problems is a class of problems where the majority of the objective space violates blackbox constraints. The difficult arises because potential solutions that violate blackbox constraints provide no information beyond their infeasibility. They provide neither meaningful(More)
Different evolutionary algorithms, by their very nature, will have different search trajectory characteristics. Understanding these particularly for real world problems gives researchers and practitioners valuable insights into potential problem domains for the various algorithms, as well as an understanding for potential hybridisation. In this study, we(More)
Artificial Neural Networks (ANNs) have often been used to model objective functions for Multi-Objective Particle Swarm Optimisation (MOPSO); alternatively MOPSO has been used to aid in training ANNs. In previous work we instead used an ANN to guide optimisation by deciding if a trial solution was worthy of full evaluation. In this work we introduce Active(More)
—Population-based, multi-objective optimisation algorithms are increasingly making use of distributed, parallel computing environments. In these cases it is a commonsense precaution to consider the possibility of a variety of failures. In particular, errors caused by response failures are more prone to arise than in homogeneous parallel computers. While(More)
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