DocumentCode :
1403922
Title :
Extremal optimization: heuristics via coevolutionary avalanches
Author :
Boettcher, Stefan
Author_Institution :
Dept. of Phys., Emory Univ., Atlanta, GA, USA
Volume :
2
Issue :
6
fYear :
2000
Firstpage :
75
Lastpage :
82
Abstract :
The extremal dynamics of the Bak-Sneppen model can be converted into an optimization algorithm called extremal optimization. Attractive features of the model include the following: it is straightforward to relate the sum of all fitnesses to the cost function of the system; in the self-organized critical state to which the system inevitably evolves, almost all species have a much better than random fitness; most species preserve a good fitness for long times unless they are connected to poorly adapted species, providing the system with a long memory; the system retains a potential for large, hill-climbing fluctuations at any stage; and the model accomplishes these features without any control parameters
Keywords :
evolutionary computation; optimisation; Bak-Sneppen model; coevolutionary avalanches; cost function; extremal dynamics; extremal optimization; heuristics; hill-climbing fluctuations; self-organized critical state; Circuit simulation; Computational modeling; Costs; Design optimization; Genetic algorithms; Intelligent networks; Process design; Simulated annealing; Space exploration; Stochastic processes;
fLanguage :
English
Journal_Title :
Computing in Science & Engineering
Publisher :
ieee
ISSN :
1521-9615
Type :
jour
DOI :
10.1109/5992.881710
Filename :
881710
Link To Document :
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