Evolutionary Multitask Optimization

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9 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

First Mathematical Runtime Analyses of Evolutionary Multitask Multi-Objective Optimization
Oct 15, 2026Danyang Zhang, Weijie ZhengEvolutionary Multitask Optimization Multi-Objective Evolutionary Optimization
School of Computer Science and Technology, State Key Laboratory of Smart Farm Technologies and Systems, International Research Institute for Artificial Intelligence, Harbin Institute of Technology, Shenzhen, China · Pengcheng Laboratory, Shenzhen, China
In-Context Guidance: Learning Inter-Task Synergies via Numerical Foundational Models for Few-Shot Multitask Optimization
Sep 22, 2026Tingyang Wei, Haofeng Wu, Jiao Liu +3Evolutionary Multitask Optimization Bayesian Optimization
College of Computing and Data Science, Nanyang Technological University, Singapore · Institute of Advanced Intelligence and Computing, Agency for Science, Technology and Research, Singapore · Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research, Singapore +1
Insights from Multi-tasking the EAX Algorithm for the Travelling Salesperson Problem
Aug 13, 2026Liam Wigney, Aneta Neumann, Yew-Soon Ong +1Traveling Salesperson Problem Evolutionary Multitask Optimization
Optimisation and Logistics · School of Computer Science and Information Technology · College of Computing & Data Science
Multitask Pareto Optimization for Monotone Submodular Problems with Dynamic Constraints
Aug 11, 2026Liam Wigney, Frank NeumannSubmodular Optimization Evolutionary Multitask Optimization
Optimisation and Logistics School of Computer Science and Information Technology Adelaide University Adelaide, Australia
Novelty Search with Cross-Task Collaborative Discovery
Jul 1, 2026Jiao Liu, Yanchi Li, Hua Yu +2Novelty Search Evolutionary Optimization
College of Computing & Data Science, Nanyang Technological University, Singapore · School of Computer Science, China University of Geosciences · School of Intelligent Rail Transportation, Dalian Jiaotong University +1
Evolutionary Multi-Task Optimization for LLM-Guided Program Discovery
May 21, 2026Halil Alperen Gozeten, Xuechen Zhang, Emrullah Ildiz +3Evolutionary Optimization Evolutionary Multitask Optimization
University of Michigan - Ann Arbor · University of California San Diego
Multi-Task Optimization over Networks of Tasks
Apr 23, 2026Julian Hatzky, Thomas Bartz-Beielstein, A. E. Eiben +1Evolutionary Optimization Evolutionary Multitask Optimization
Vrije Universiteit Amsterdam, Amsterdam, The Netherlands · TH Köln, Cologne, Germany
Analysis of Multitasking Pareto Optimization for Monotone Submodular Problems
Apr 16, 2026Liam Wigney, Frank NeumannSubmodular Optimization Evolutionary Optimization
Optimisation and Logistics School of Computer Science and Information Technology Adelaide University Adelaide, Australia
Finding Sets of Pareto Sets in Real-World Scenarios -- A Multitask Multiobjective Perspective
Apr 4, 2026Jiao Liu, Yew Soon Ong, Melvin WongEvolutionary Multitask Optimization Multi-Objective Optimization
College of Computing & Data Science, Nanyang Technological University, Singapore

Oct 15, 2026J/K move · Enter open · S save

First Mathematical Runtime Analyses of Evolutionary Multitask Multi-Objective Optimization

Danyang Zhang, Weijie Zheng

School of Computer Science and Technology, State Key Laboratory of Smart Farm Technologies and Systems, International Research Institute for Artificial Intelligence, Harbin Institute of Technology, Shenzhen, China · Pengcheng Laboratory, Shenzhen, China

Evolutionary Multitask Optimization Multi-Objective Evolutionary Optimization Multi-Task Optimization Multi-Objective Optimization Runtime Analysis of Evolutionary Algorithms

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To facilitate the solving of complex problems, evolutionary multitask optimization is regarded as a promising technique due to its capacity for cross-task knowledge transfer. In contrast to empirical evidences, no mathematical runtime support exists for this technique within the domain of multi-objective optimization. In this work, we aim to fill this important gap. We analyze how the representative multiobjective multifactorial evolutionary algorithm (MO-MFEA) covers the Pareto fronts of two bi-objective problems simultaneously. For the two-task problem of maximizing the classic bi-objective OMM and the multimodal OJZJ benchmarks, the MO-MFEA requires only an expected runtime of

O(n^2 \log n)

---the same as optimizing the simple OMM alone---yet achieves a speedup by a factor of

Θ(n^{k-1}/\log n)

compared to optimizing OJZJ alone (with gap size

k

). Similarly, for the task pair comprising the classic LOTZ and the multimodal DLTB benchmarks, a speedup of

Θ(n)

is attained over optimizing DLTB alone, while the runtime matches that of optimizing LOTZ alone.