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Life sciences · Preprint
arXiv · September 10, 2026
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This preprint proposes Iterative Sequential Transfer (IST), a novel method for few-shot multiobjective multitask optimization that sequences transfers across tasks to circumvent elite solution distribution bottlenecks. The work is algorithmic, unreviewed, and validation is limited to benchmarks and unspecified real-world problems without comparative baselines or effect sizes reported in the abstract.
Algorithm development with empirical validation on benchmarks and real-world problems. Intervention: Iterative Sequential Transfer (IST) algorithm with likelihood-informed task prioritization mechanism for multiobjective multitask optimization.
IST models multitask optimization as sequential transfer problems focused on single targets per iteration A likelihood-informed task prioritization mechanism is proposed to identify tasks ready for knowledge integration Method reported as effective under tight evaluation budgets on benchmark and real-world problems (no quantitative results in abstract)
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This is a preprint reporting a novel algorithmic approach to multiobjective multitask optimization with empirical validation on benchmarks, but lacks peer review, clinical or real-world outcome data, and comparative efficacy assessment against established methods.
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Applying knowledge transfer across multiple optimization tasks, multitask optimization (MTO) emerges as a promising approach to solving synergistic optimization tasks simultaneously. However, the development of effective knowledge transfer mechanisms in MTO fundamentally relies on aligning elite solution distributions across tasks. This dependency creates a critical bottleneck in few-shot optimization regimes, as restricted evaluation budgets impede the identification of elite solution distributions required for beneficial transfer. This challenge is exacerbated in multiobjective multitask problems, where each optimizer must approximate a continuous Pareto manifold rather than a single optimal point. This paper introduces Iterative Sequential Transfer (IST) to circumvent this bottleneck. We model MTO as a sequence of sequential transfer optimization problems, concentrating evaluations on a single target per iteration. We propose a likelihood-informed task prioritization mechanism to maximize transfer utility by identifying the task most likely ready for knowledge integration. Empirical results on benchmark and real-world problems verify the effectiveness of the proposed method under tight budgets.
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