From Consistency to Collaborative Discovery: MFEA-CoD for Multitask Novelty Search
2026-07-01 • Neural and Evolutionary Computing
Neural and Evolutionary Computing
AI summaryⓘ
The authors introduce a new method called MFEA-CoD that helps solve multiple tasks by encouraging exploration of different novel solutions rather than just speeding up finding known answers. Their approach uses a mechanism to make tasks avoid overlapping discoveries and shares useful information when tasks find valuable new areas. They also combine searching for new behaviors with traditional objectives to avoid getting stuck in tricky problems. Tests on various problem types show their method finds diverse new solutions more efficiently and handles difficult problem landscapes better.
Evolutionary multitaskingNovelty searchMultifactorial evolutionary algorithmBehavioral diversityObjective optimizationPremature convergenceDeceptive objectivesAdaptive knowledge transferMulti-task learningMuJoCo
Authors
Jiao Liu, Yanchi Li, Hua Yu, Abhishek Gupta, Yew-Soon Ong
Abstract
Evolutionary multitasking (EMT) has shown strong capability in solving multiple optimization problems simultaneously by exploiting latent inter-task consistency, such as similarities in promising solutions or search directions. However, most existing EMT studies remain focused on objective-driven optimization, where such consistency is mainly used to accelerate convergence toward predefined optima. In this paper, we move EMT from consistency to collaborative discovery and propose a multifactorial evolutionary algorithm with collaborative discovery (MFEA-CoD) for multitask novelty search. Unlike conventional EMT, MFEA-CoD coordinates multiple novelty search tasks to collaboratively discover behaviorally novel solutions rather than merely transferring consistent search information for faster convergence. Specifically, a multitask repulsion operator encourages different tasks to explore distinct regions of the unified search space, thereby reducing redundant behavioral discoveries. Meanwhile, an adaptive inter-task transfer mechanism exploits shared discovery opportunities in overlapping novelty-improving regions by adjusting the transfer probability according to the online contribution of transferred information. Furthermore, MFEA-CoD is extended to multitask novelty-augmented optimization, where behavioral novelty is jointly considered with objective information to alleviate premature convergence caused by deceptive objectives. Experiments on synthetic basin-type problems, deceptive maze navigation problems, MuJoCo policy optimization problems, and generative novelty search problems demonstrate that MFEA-CoD improves the efficiency of discovering diverse novel solutions and shows clear advantages in deceptive objective landscapes.