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Equation 2 · Fitness Landscapes and the Limits of the Metaphor

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w(g0)<w(g1)<⋯<w(gL)w(g_0) < w(g_1) < \cdots < w(g_L)

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ww

Symbol w

w is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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g0g_0

Symbol g_0

g0g_0 is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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g1g_1

Symbol g_1

g1g_1 is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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gLg_L

Symbol g_L

gLg_L is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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subscript

subscript

The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.

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What the article says around this equation

The reason is an ordering constraint. Under the standard assumption that a population moves by fixing one beneficial mutation at a time, a mutational path is accessible only if fitness increases at every single step: w(g0)<w(g1)<⋯<w(gL)w(g_0) < w(g_1) < \cdots < w(g_L). A single step that violates this inequality closes the entire path, regardless of how high the endpoint is. Sign epistasis is exactly the mechanism that produces such steps, and reciprocal sign epistasis — where each of two mutations is deleterious without the other and beneficial with it — is what creates genuinely separated local optima.

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