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This paper proposes a novel optimization algorithm for image-space matching and three-dimensional space analysis, using an adapted scheme of evolutionary computation that employs the concept of symbiosis in a collective of homogeneous populations. It is applied to the automatic generation of disparity surfaces used for depth estimation in stereo vision. The global task of approximating the complete disparity surface is decomposed to a large number of smaller local problems, each solvable by a smaller processing unit. Coevolution is sustained in such a way as to counteract the arbitrary decomposition of the original super-problem, so that the local evolutions of all the subproblems become interlocked. This, in the long run, provides a consistent global solution, and it does so via an asynchronous and massively parallel architecture. The entire surface is partitioned to a set of adjoining patches represented by distinct species or populations, with phenotypes corresponding to different polynomial functionals. The credit assignment functions take into account both self and symbiotic terms in an adaptive and dynamic manner, in order to produce disparity patches that are fit within their own domain and at the same time fit in association with their symbionts. This persistent propagation of local interactions to a global scale throughout evolution generates a unified disparity surface composed of the many smaller patch surfaces.