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Characterising continuous optimisation problems for particle swarm optimisation performance prediction
Real-world optimisation problems are often very complex. Population-based metaheuristics, such as evolutionary algorithms and particle swarm optimisation (PSO) algorithms, have been successful inExpand
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Constraint Handling Guided by Landscape Analysis in Combinatorial and Continuous Search Spaces
The notion and characterisation of fitness landscapes has helped us understand the performance of heuristic algorithms on complex optimisation problems. Many practical problems, however, areExpand
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Saccade deviation indicators for automated eye tracking analysis
Eye tracking has been around for more than 100 years and the technology has improved at an incredible rate. With the advancement of technology, eye tracking can even be done from a mobile phone,Expand
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Analysis of error landscapes in multi-layered neural networks for classification
Artificial neural networks are inherently high-dimensional, which limits our ability to visualise and understand their inner workings. Neural network architecture and training algorithm parametersExpand
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Characterising neutrality in neural network error landscapes
The characterisation of topographical features of fitness landscapes can provide significant insight into the nature of underlying optimisation problems and the behaviour of metaheuristic searchExpand
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Insights into the Feature Selection Problem Using Local Optima Networks
The binary feature selection problem is investigated in this paper. Feature selection fitness landscape analysis is done, which allows for a better understanding of the behaviour of feature selectionExpand
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Landscape-Aware Constraint Handling Applied to Differential Evolution
In real-world contexts optimisation problems frequently have constraints. Evolutionary algorithms do not naturally handle constrained spaces, so require constraint handling techniques to modify theExpand
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Local optima networks for continuous fitness landscapes
Local Optima Networks (LONs) have been proposed as a coarsegrained model of discrete (combinatorial) fitness landscapes, where nodes are local optima and edges are search transitions based on anExpand
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  • Open Access
Essential Java for scientists and engineers
class FarmyardAnimal { protected int positionX, positionY; public abstract void makeNoise(); // no method body public void move(newX, newY) { positionX = newX; positionY = newY; } } class DuckExpand
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  • Open Access
Benchmarking in Optimization: Best Practice and Open Issues
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice in benchmarking isExpand
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  • Open Access