Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
On the issue of obtaining OWA operator weights
Fuzzy Sets and Systems
The functional equations of Frank and Alsina for uninorms and nullnorms
Fuzzy Sets and Systems
An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Evolving edge detectors with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Appropriate choice of aggregation operators in fuzzy decision support systems
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Statistical analysis of parametric t-norms
Information Sciences: an International Journal
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Aggregation operations play an important role in decision-making problems where a weighted combination of several criteria is used to select an alternative with the strongest support. In fuzzy set theory, aggregation operations are usually modeled as intersection, union, or as combination of both. The particular form and algebraic properties of these operations vary according to requirements for compensation among the criteria and other characteristics of the given decision-making situation. Traditionally, only algebraically well-behaved operations have been considered for this purpose. By relaxing some algebraic constraints, more realistic operations can be obtained that closely capture certain features of human decision-making, such as preferences and a limited level of detail. This paper proposes a method to generate fuzzy aggregation operations using genetic programming. It is shown that an evolutionary process, facilitated by genetic programming, has the capacity to generate new valid fuzzy aggregation operations and to reproduce existing ones. By varying process conditions, encoded in a fitness function, it is possible to obtain operations with different logical and algebraic properties. This approach, based solely on the axioms which define the desired class of operations, explores the space of possible functions and often leads to discovery of new operations. However, the proposed system can also be used to generate aggregation operations that fit a collected data set. This application is very important as it provides a powerful new tool for modeling and processing empirical data.