Multi-Tasking Genetic Algorithm (MTGA) for Fuzzy System Optimization
Dongrui Wu, Xianfeng Tan

TL;DR
This paper introduces a novel multi-tasking genetic algorithm (MTGA) that effectively handles diverse optimization tasks by estimating task bias, outperforming existing methods in benchmarks and fuzzy system design.
Contribution
The paper presents a new easy-to-implement MTGA that manages significantly different tasks and demonstrates its superiority over state-of-the-art approaches in benchmarks and fuzzy system optimization.
Findings
MTGA outperforms eight state-of-the-art methods on benchmarks.
MTGA has lower computational cost than six compared approaches.
MTGA finds better fuzzy controllers in water level control experiments.
Abstract
Multi-task learning uses auxiliary data or knowledge from relevant tasks to facilitate the learning in a new task. Multi-task optimization applies multi-task learning to optimization to study how to effectively and efficiently tackle multiple optimization problems simultaneously. Evolutionary multi-tasking, or multi-factorial optimization, is an emerging subfield of multi-task optimization, which integrates evolutionary computation and multi-task learning. This paper proposes a novel and easy-to-implement multi-tasking genetic algorithm (MTGA), which copes well with significantly different optimization tasks by estimating and using the bias among them. Comparative studies with eight state-of-the-art single- and multi-task approaches in the literature on nine benchmarks demonstrated that on average the MTGA outperformed all of them, and had lower computational cost than six of them.…
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Taxonomy
TopicsFuzzy Logic and Control Systems · Machine Learning and ELM · Neural Networks and Applications
