Optimizing through Co-evolutionary Avalanches

Abstract. We explore a new general-purpose heuristic for nding highquality solutions to hard optimization problems. The method, called extremal optimization, is inspired by self-organized criticality," a concept introduced to describe emergent complexity in many physical systems. In contrast to Genetic Algorithms which operate on an entire genepool" of possible solutions, extremal optimization successively replaces extremely undesirable elements of a sub-optimal solution with new, random ones. Large uctuations, called avalanches," ensue that e ciently explore many local optima. Drawing upon models used to simulate farfrom-equilibrium dynamics, extremal optimization complements approximation methods inspired by equilibrium statistical physics, such as simulated annealing. With only one adjustable parameter, its performance has proved competitive with more elaborate methods, especially near phase transitions. Those phase transitions are found in the parameter space of most...

• Type: Conference
• Year: 2000
• Where: PPSN
• Authors: Stefan Boettcher, Allon G. Percus, Michelangelo Grigni
• Keywords: Combinatorial Optimization Problems, Distributed And Parallel Computing, Extremal Optimization, Optimization Problems, PPSN 2000