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arXiv · 2604.21991

Multi-Task Optimization over Networks of Tasks

Julian Hatzky·Thomas Bartz-Beielstein·A. E. Eiben·Anil Yaman
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Abstract

Multi-task optimization is a powerful approach for solving a large number of tasks in parallel. However, existing algorithms face distinct limitations: Population-based methods scale poorly and remain underexplored for large task sets. Approaches that do scale beyond a thousand tasks are mostly MAP-Elites variants and rely on a fixed, discretized archive that disregards the topology of the task space. We introduce MONET (Multi-Task Optimization over Networks of Tasks), a multi-task optimization algorithm that models the task space as a graph: tasks are nodes, and edges connect tasks in the task parameter space. This representation enables knowledge transfer between tasks and remains tractable for high-dimensional problems while exploiting the topology of the task space. MONET combines social learning, which generates candidates from neighboring nodes via crossover, with individual learning, which refines a node's own solution independently via mutation. We evaluate MONET on four domains (archery, arm, and cartpole with 5,000 tasks each; hexapod with 2,000 tasks) and show that it matches or exceeds the performance of existing MAP-Elites-based baselines across all four domains.

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Julian Hatzky, Thomas Bartz-Beielstein, A. E. Eiben, Anil Yaman. 2026-06-24. Multi-Task Optimization over Networks of Tasks. https://arxiv.org/abs/2604.21991

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