  In this paper, we explore a general-purpose heuristic algorithm for finding
high-quality solutions to continuous optimization problems. The method, called
continuous extremal optimization(CEO), can be considered as an extension of
extremal optimization(EO) and is consisted of two components, one is with
responsibility for global searching and the other is with responsibility for
local searching. With only one adjustable parameter, the CEO's performance
proves competitive with more elaborate stochastic optimization procedures. We
demonstrate it on a well known continuous optimization problem: the
Lennerd-Jones clusters optimization problem.
