Solving dynamic combinatorial problems poses a particular challenge to optimisation algorithms. Optimising a problem that does not notify the solver when a change has been made is very difficult for most well-known algorithms. Extremal Optimisation is a recent addition to the group of biologically inspired optimisation algorithms. Due to its extremely simple functionality, it is likely that the algorithm can be applied successfully in such a dynamic environment. This document examines the capabilities of Extremal Optimisation to solve a dynamic problem with a large variety of different changes that are not explicitly announced to the solver
Real-world optimisation problems are often dynamic. Previously good solutions must be updated or rep...
AbstractWe propose a general-purpose method for finding high-quality solutions to hard optimization ...
Abstract: Biological systems are, by their very nature, adaptive. However, the meta-heuristic search...
Abstract. Solving dynamic combinatorial problems poses a particular challenge to optimisation algori...
Solving dynamic combinatorial problems poses a particular challenge to optimisation algorithms. Opti...
Extremal Optimisation is a recently conceived addition to the range of stochastic solvers. Due to it...
Extremal Optimisation is a recently conceived addition to the range of stochastic solvers. Due to it...
Abstract. It is only relatively recently that extremal optimisation (EO) has been applied to combina...
The authors explore a new general-purpose heuristic for finding high-quality solutions to hard optim...
We explore a new general-purpose heuristic for finding high-quality solutions to hard optimization p...
Extremal optimisation is an emerging nature inspired meta-heuristic search technique that allows a p...
Dynamic function optimisation is an important research area because many real-world problems are inh...
We propose a general-purpose method for finding high-quality solutions to hard optimization problems...
Abstract If the optimization problem is dynamic, the goal is no longer to find the extrema, but to t...
This is a repository containing the code and results of the work "On the Elusivity of Dynamic Combin...
Real-world optimisation problems are often dynamic. Previously good solutions must be updated or rep...
AbstractWe propose a general-purpose method for finding high-quality solutions to hard optimization ...
Abstract: Biological systems are, by their very nature, adaptive. However, the meta-heuristic search...
Abstract. Solving dynamic combinatorial problems poses a particular challenge to optimisation algori...
Solving dynamic combinatorial problems poses a particular challenge to optimisation algorithms. Opti...
Extremal Optimisation is a recently conceived addition to the range of stochastic solvers. Due to it...
Extremal Optimisation is a recently conceived addition to the range of stochastic solvers. Due to it...
Abstract. It is only relatively recently that extremal optimisation (EO) has been applied to combina...
The authors explore a new general-purpose heuristic for finding high-quality solutions to hard optim...
We explore a new general-purpose heuristic for finding high-quality solutions to hard optimization p...
Extremal optimisation is an emerging nature inspired meta-heuristic search technique that allows a p...
Dynamic function optimisation is an important research area because many real-world problems are inh...
We propose a general-purpose method for finding high-quality solutions to hard optimization problems...
Abstract If the optimization problem is dynamic, the goal is no longer to find the extrema, but to t...
This is a repository containing the code and results of the work "On the Elusivity of Dynamic Combin...
Real-world optimisation problems are often dynamic. Previously good solutions must be updated or rep...
AbstractWe propose a general-purpose method for finding high-quality solutions to hard optimization ...
Abstract: Biological systems are, by their very nature, adaptive. However, the meta-heuristic search...