A star gadget with 2n triangles on one central variable in a binary VCSP produces an exponential ascent of length 10*2^n - 9 by intertwining two linear sublandscapes.
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In isotropic Gaussian random field fitness landscapes the expected number of local optima is determined by the correlation of fitness effects, with modularity increasing and locus heterogeneity decreasing the count.
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Binary constraints on one additional variable can create exponential ascents
A star gadget with 2n triangles on one central variable in a binary VCSP produces an exponential ascent of length 10*2^n - 9 by intertwining two linear sublandscapes.
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Epistatic strength, modularity, and locus heterogeneity shape the number of local optima in fitness landscapes
In isotropic Gaussian random field fitness landscapes the expected number of local optima is determined by the correlation of fitness effects, with modularity increasing and locus heterogeneity decreasing the count.