...Vasile Georgescu Purpose Type-2 fuzzy sets became attractive in practice because of their footprint of uncertainty that gives them more degrees of freedom. This paper aims to use genetic algorithms (GAs) to design an interval Type-2 fuzzy logic system (IT2FLS) for the purpose of predicting...

... performance of neural network (NN) combined with expert knowledge (EK) to obtain the objective function, and secondly searches the optimal influential parameters with genetic algorithm (GA). Findings Peaks function inside Matlab and the acural application of comprehensive performance optimization...

..., and explore the inner mechanism and optimization strategies of value creation in manufacturing clusters from the perspective of cluster's value activity network. Design/methodology/approach This paper applies a genetic algorithm to optimally search in the target space, and repeatedly exerts genetic...

... a high performance genetic algorithm for community formation based on collective intelligence capacity. An experimental study is presented to illustrate the algorithm. Findings Collective intelligence does not represent the sum of individual intelligences, it is the ability of the community...

... (Bentley, 1999 ; Bentley and Corne, 2002 ; Sommerer and Mignonneau, 1998 ; Wilson, 2002). © MCB UP Limited 2003 Cybernetics Genetic algorithms Human‐computer interaction One thing that foreigners, computers and poets have in common is that they make unexpected linguistic...

...Sung‐Kwun Oh; Witold Pedrycz A comprehensive design methodology of hybrid fuzzy controllers (HFC) is proposed. The HFC comes in a form of a convex combination of a standard PID controller and a fuzzy controller. The design procedure dwells on the use of evolutionary computing (genetic algorithm...

... to present an other new kind of genetic algorithms, resulting from the integration of the recursivity in the basis functioning of genetic algorithms, and that we have named Recursive Genetic Algorithm. © MCB UP Limited 2002 The general structure of a prototyped genetic algorithm...

... are also provided. © MCB UP Limited 2001 Cybernetics Computation Genetic algorithms Non‐linear programming In this paper we provide new evidence on the performance of a multipurpose parallel genetic hybrid algorithm (GHA) for nonlinear, non‐convex programming problems. GHA has...

... Intelligent Optimisation Techniques D.T. Pham and D. KarabogaSpringer-VerlagLondon, Heidelberg and New York, NY1999302 pp.ISBN 1 85233 028 7 Keywords: Publication, Cybernetics, Genetic algorithms, Optimisation This is a book that covers the four optimisation...

..., such as neural networks and genetic algorithms, which are not traditionally considered in blackboard systems. 4. (4) The basic theory of blackboard systems does not impose any single scheduling theory. It describes the role of the scheduler, executor and agenda manager, but does not specify how each must...

... to modify some of the constraints, the optimisation procedure is required to be repeated. To overcome the latter problems, a multi‐objective genetic algorithm (MOGA) using Pareto ranking was implemented (Steuer, 1986; Fonseca, 1995; Haas, 1997). The MOGA was modified to enable the treatment planner...

...Hojjat Adeli Cybernetics Fuzzy systems Genetic algorithms Neural networks Publication This is a book that will be of interest to cyberneticians and systemists. Its contents list which is included below shows a wide range of interconnecting topics that form important studies in any...

...Wu Chen © MCB UP Limited 1999 --> Cybernetics Genetic algorithms Optimisation Predicting production based on genetic algorithms Keywords Cybernetics, Genetic algorithms, Optimisation Abstract Proposes a genetic algorithm approach to predict the production...