Multi-Tasking Genetic Algorithm (MTGA) for Fuzzy System Optimization

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. Based on the MTGA, a simultaneous optimization strategy for fuzzy system design is also proposed. Experiments on simultaneous optimization of type-1 and interval type-2 fuzzy logic controllers for couple-tank water level control demonstrated that the MTGA can find better fuzzy logic controllers than other approaches.