Title :
Qualitative selection strategies in genetic-based evolutionary economic models
Author :
Loia, Vincenzo ; Scandizzo, Sergio
Author_Institution :
Dipartimento di Inf. ed Applicazioni, Salerno Univ., Italy
Abstract :
We use an evolutionary economic model based on a “genetic” representation of firm´s behaviour which can naturally be implemented by means of a genetic algorithm. We suggest that both optimisation performance and simulation power may be enhanced incorporating in genetic algorithms a sophisticated fuzzy clustering algorithm. Such a system would be worth using in all those cases where uncertainty and imprecision occur in the evaluation of the fitness function, as well as in assessing similarities and differences among individuals
Keywords :
commerce; corporate modelling; economic cybernetics; economics; fuzzy set theory; genetic algorithms; fitness function; genetic-based evolutionary economic models; imprecision; optimisation performance; qualitative selection strategies; simulation power; sophisticated fuzzy clustering algorithm; uncertainty; Artificial neural networks; Clustering algorithms; Environmental economics; Evolution (biology); Genetic algorithms; Laboratories; Power generation economics; Power system economics; Power system modeling; Uncertainty;
Conference_Titel :
Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society. Proceedings of ISUMA - NAFIPS '95., Third International Symposium on
Conference_Location :
College Park, MD
Print_ISBN :
0-8186-7126-2
DOI :
10.1109/ISUMA.1995.527717
