Extremal optimization, a recently introduced meta-heuristic for hard optimization problems, is analysed on a simple model of jamming. The model is motivated first by the problem of finding lowest energy configurations for a disordered spin system on a fixed-valence graph. The numerical results for the spin system exhibit the same phenomenology found in all earlier studies of extremal optimization, and our analytical results for the model reproduce many of these features. PACS numbers: 02.60.Pn, 05.40.−a, 64.60.Cn, 75.10.Nr (Some figures in this article are in colour only in the electronic version) 1
In the present Ph.D. dissertation I present results concerning disordered and frustrated models of r...
Abstract: We adapt a combinatorial optimization algorithm, extremal optimization (EO), for the searc...
Simulated annealing and related Monte Carlo-type optimization algorithms are used to apply statistic...
The authors explore a new general-purpose heuristic for finding high-quality solutions to hard optim...
Abstract. Extremal Optimization (EO), a new local search heuristic, is used to approximate ground st...
Extremal Optimization (EO), a new local search heuristic, is used to approximate ground states of t...
We explore a new general-purpose heuristic for finding high-quality solutions to hard optimization p...
AbstractWe propose a general-purpose method for finding high-quality solutions to hard optimization ...
A version of the extremal optimization (EO) algorithm introduced by Boettcher and Percus is tested o...
Extremal optimisation is an emerging nature inspired meta-heuristic search technique that allows a p...
Several optimization problems can be stated as disordered systems problems. This fact encouraged a f...
We propose a general-purpose method for finding high-quality solutions to hard optimization problems...
Abstract. Solving dynamic combinatorial problems poses a particular challenge to optimisation algori...
A b s t r a c t. We explore a new general-purpose heuristic for finding high-quality solutions to ha...
Solving dynamic combinatorial problems poses a particular challenge to optimisation algorithms. Opti...
In the present Ph.D. dissertation I present results concerning disordered and frustrated models of r...
Abstract: We adapt a combinatorial optimization algorithm, extremal optimization (EO), for the searc...
Simulated annealing and related Monte Carlo-type optimization algorithms are used to apply statistic...
The authors explore a new general-purpose heuristic for finding high-quality solutions to hard optim...
Abstract. Extremal Optimization (EO), a new local search heuristic, is used to approximate ground st...
Extremal Optimization (EO), a new local search heuristic, is used to approximate ground states of t...
We explore a new general-purpose heuristic for finding high-quality solutions to hard optimization p...
AbstractWe propose a general-purpose method for finding high-quality solutions to hard optimization ...
A version of the extremal optimization (EO) algorithm introduced by Boettcher and Percus is tested o...
Extremal optimisation is an emerging nature inspired meta-heuristic search technique that allows a p...
Several optimization problems can be stated as disordered systems problems. This fact encouraged a f...
We propose a general-purpose method for finding high-quality solutions to hard optimization problems...
Abstract. Solving dynamic combinatorial problems poses a particular challenge to optimisation algori...
A b s t r a c t. We explore a new general-purpose heuristic for finding high-quality solutions to ha...
Solving dynamic combinatorial problems poses a particular challenge to optimisation algorithms. Opti...
In the present Ph.D. dissertation I present results concerning disordered and frustrated models of r...
Abstract: We adapt a combinatorial optimization algorithm, extremal optimization (EO), for the searc...
Simulated annealing and related Monte Carlo-type optimization algorithms are used to apply statistic...