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Linear Proposal Operators and Stochastic Search Geometry in SOMA and Differential Evolution

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arXiv:2607.29228v1 Announce Type: new Abstract: Swarm and evolutionary algorithms are usually analyzed as complete procedural systems in which nonlinear selection, replacement, and adaptation obscure simpler structure within candidate generation. This paper introduces an operator--selection factorization that separates objective-independent variation from boundary repair and fitness-dependent selection, and uses it to study the proposal geometry of the Self-Organizing Migrating Algorithm (SOMA) and Differential Evolution (DE). The canonical SOMA proposal is shown to be affine in the search space and exactly linear in an augmented migrant--leader state. In leader-relative coordinates, the resulting operator provides a direct interpretation of interpolation, projection, overshooting, and coordinate masking. Under Bernoulli perturbation masks, we derive closed-form expressions for the proposal mean, covariance, expected squared step length, expected squared distance from the leader, activ...

arXiv Neural/NEabout 10 hours ago
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Linear Proposal Operators and Stochastic Search Geometry in SOMA and Differential Evolution | Steek AI Signal | Steek