Genetic fuzzy programs

author = "Isaac J. Sledge and James M. Keller",

title = "Genetic fuzzy programs",

booktitle = "Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2008",

year = "2008",

month = may,

pages = "1--6",

keywords = "genetic algorithms, genetic programming, decision making, decision-making behavior, evolutionary network learning approach, genetic fuzzy programs, weight-biased behavior, fuzzy set theory",

DOI = "

doi:10.1109/NAFIPS.2008.4531236",

abstract = "When aggregating multiple items together, for processes such as decision making, it is often beneficial to assign linguistically imprecise weights to each criterion. These weights, when coupled with the values of the criteria, enable us to prioritize certain responses over others. Through the advent of fuzzy connectives, computers can also emulate this weight-biased, decision-making behavior. However, when crafting an aggregation network for a complex problem, the solution may require not just a single fuzzy aggregator, but rather multiple layers of intimately entwined fuzzy connectives. To explore a suitable hierarchical relationship, we use genetic programming, to evolve both fuzzy aggregation networks, which take the form of multi arity expression trees, and the associated connective weights. We test our evolutionary network learning approach with synthetic and real-world data sets and discuss the results.",

notes = "Also known as \cite{4531236}",