Extremal Optimization: Methods derived from Co-Evolution

Details Extremal Optimization: Methods derived from Co-Evolution Extremal Optimization: Methods derived from Co-Evolution

1999-04-13

arxiv.org/abs/math/9904056v1

Mathematics

Optimization and Control

8 pages, Latex, 5 ps-figures included. To appear in ``GECCO-99: Proceedings of the Genetic and Evolutionary Computation Confer

Scientific paper

We describe a general-purpose method for finding high-quality solutions to hard optimization problems, inspired by self-organized critical models of co-evolution such as the Bak-Sneppen model. The method, called Extremal Optimization, successively eliminates extremely undesirable components of sub-optimal solutions, rather than ``breeding'' better components. In contrast to Genetic Algorithms which operate on an entire ``gene-pool'' of possible solutions, Extremal Optimization improves on a single candidate solution by treating each of its components as species co-evolving according to Darwinian principles. Unlike Simulated Annealing, its non-equilibrium approach effects an algorithm requiring few parameters to tune. With only one adjustable parameter, its performance proves competitive with, and often superior to, more elaborate stochastic optimization procedures. We demonstrate it here on two classic hard optimization problems: graph partitioning and the traveling salesman problem.

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