A multilevel algorithm for partitioning graphs
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Artificial Intelligence
Memetic Algorithms and the Fitness Landscape of the Graph Bi-Partitioning Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Optimization with extremal dynamics
Complexity - Complex Adaptive systems: Part I
Robust Point Correspondence for Image Registration Using Optimization with Extremal Dynamics
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Optimization with extremal dynamics
Complexity - Complex Adaptive systems: Part I
A self-organizing random immigrants genetic algorithm for dynamic optimization problems
Genetic Programming and Evolvable Machines
A self-organized criticality mutation operator for dynamic optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Hybrid Scatter Search with Extremal Optimization for Solving the Capacitated Vehicle Routing Problem
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Extremal optimization dynamics in neutral landscapes: the royal road case
EA'09 Proceedings of the 9th international conference on Artificial evolution
Self-organized combinatorial optimization
Expert Systems with Applications: An International Journal
Self-organizing approach to graph vertex colouring in the applications of ad hoc networks
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Fast detection of size-constrained communities in large networks
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Development of hybrid evolutionary algorithms for production scheduling of hot strip mill
Computers and Operations Research
A study on the mutation rates of a genetic algorithm interacting with a sandpile
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
The sandpile mutation operator for genetic algorithms
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
A population-based hybrid extremal optimization algorithm
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Toward understanding the optimization of complex systems
Artificial Intelligence Review
Inferring human mobility patterns from anonymized mobile communication usage
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
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A local-search heuristic for finding high-quality solutions for many hard optimization problems is explored. The method is inspired by recent progress in understanding far-from-equilibrium phenomena in terms of self-organized criticality, a concept introduced to describe emergent complexity in physical systems. This method, called extremal optimization, successively replaces the value of extremely undesirable variables in a sub-optimal solution with new, random ones. Large, avalanche-like fluctuations in the cost function emerge dynamically. These enable the search to effectively scaling barriers to explore local optima in distant neighborhoods of the configuration space while eliminating the need to tune parameters. Drawing upon models used to simulate the dynamics of granular media, evolution, or geology, extremal optimization complements approximation methods inspired by equilibrium statistical physics, such as simulated annealing. This method is very general and so far has proved competitive with--and even superior to--more elaborate general-purpose heuristics on testbeds of constrained optimization problems with up to 105 variables, such as bipartitioning, coloring, and spin glasses. Analysis of a model problem predicts the only free parameter of the method in accordance with all experimental results.