@inbook{0f1a5aa9ab004716883773be3a299c79,
title = "Simultaneous Ant Colony Optimization Algorithms for Learning Linguistic Fuzzy Rules",
abstract = "An approach based on Ant Colony Optimization for the induction of fuzzy rules is presented. Several Ant Colony Optimization algorithms are run simultaneously, with each focusing on finding descriptive rules for a specific class. The final outcome is a fuzzy rulebase that has been evolved so that individual rules complement each other during the classification process. This novel approach to fuzzy rule induction is compared against several other fuzzy rule induction algorithms, including a fuzzy genetic algorithm and a fuzzy decision tree. The initial findings indicate comparable or better classification accuracy, and superior comprehensibility. This is attributed to both the strategy of evolving fuzzy rules simultaneously, and to the individual rule discovery mechanism, the Ant Colony Optimization heuristic. The strengths and potential of the approach, and its current limitations, are discussed in detail.",
author = "Michelle Galea and Qiang Shen",
year = "2006",
doi = "10.1007/978-3-540-34956-3_4",
language = "English",
isbn = "978-3-540-34955-6",
series = "Studies in Computational Intelligence",
publisher = "Springer",
pages = "75--99",
editor = "Ajith Abraham and Crina Grosan and Vitorino Ramos",
booktitle = "Swarm Intelligence in Data Mining",
address = "United Kingdom",
}