Local Budget Benefits, Population Commitment, and Solver Performance: A DGO Case Study with Controlled DE Transfer

Abstract

An evaluated candidate can improve a local search step, alter the population used by later steps, and affect a solver's final result. We distinguish these three questions using Da Gou Gun Optimization (DGO), a population optimizer designed for this study. Its JQ100 configuration evaluates an interpolation candidate during a two-sided probe. We define local budget value, population commitment effects and complete-policy performance, and give an exact example in which a locally better commitment leads to a worse continuation. Short-horizon matched comparisons favor interpolation on eligible quadratic states. Paired continuations yield mixed effect estimates, with primary group intervals spanning zero. Complete-policy comparisons favor JQ100 over the tested classical references in aggregate; a matched-start comparison has favorable means but intervals spanning parity. On 24 boxed BBOB functions, target-truncated geometric-mean error ratios favor JQ100 over the tested classical policies and favor L-SHADE and CMA-ES over JQ100 under the stated initialization and stopping rules. Local evaluation gains, commitment effects and solver competitiveness therefore require separate comparisons.