# Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results

arXiv: https://arxiv.org/abs/1706.02766 · HTML: https://mlsanity.com/paper/1706.02766/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2017.

## Built on by (most cited first, 4 of 4)

- [Prompt Evolution for Generative AI: A Classifier-Guided Approach](https://mlsanity.com/paper/2305.16347/) (2023)
- [Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions](https://mlsanity.com/paper/2102.02558/) (2021)
- [(θl, θu)-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spaces](https://mlsanity.com/paper/2503.08394/) (2025)
- [Solving Few-Shot Multiobjective Multitask Optimization via Iterative Sequential Transfer](https://mlsanity.com/paper/2609.11228/) (2026)

## Abstract (arXiv)

In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously. The relationship between tasks varies between different test problems, which would be helpful to have a comprehensive evaluation of the MO-MFO algorithms. It is expected that the proposed test problems will germinate progress the field of the MTMOO research.