Automated Design and Knowledge Discovery of Logic Circuits Using a Multi-objective Adaptive GA

2005 ◽  
pp. 997-1000 ◽  
Author(s):  
Shuguang Zhao ◽  
Licheng Jiao ◽  
Min Tang
Keyword(s):  
Knowledge Discovery ◽  
Automated Design ◽  
Logic Circuits ◽  
Multi Objective

Multi-Objective Design and Optimisation of Bypass Outlet-Guide Vanes

2003 ◽  
Author(s):  
Shahrokh Shahpar ◽  
David Giacche ◽  
Leigh Lapworth
Keyword(s):  
Design Space ◽  
Guide Vane ◽  
Automated Design ◽  
Analysis Procedure ◽  
High Fidelity ◽  
Cfd Analysis ◽  
Optimization System ◽  
Multi Objective ◽  
Cluster Of Workstations ◽  
Stagger Angle

This paper describes the development of an automated design optimization system that makes use of a high fidelity Reynolds-Averaged CFD analysis procedure to minimize the fan forcing and fan BOGV (bypass outlet guide vane) losses simultaneously taking into the account the down-stream pylon and RDF (radial drive fairing) distortions. The design space consists of the OGV’s stagger angle, trailing-edge recambering, axial and circumferential positions leading to a variable pitch optimum design. An advanced optimization system called SOFT (Smart Optimisation for Turbomachinery) was used to integrate a number of pre-processor, simulation and in-house grid generation codes and postprocessor programs. A number of multi-objective, multi-point optimiztion were carried out by SOFT on a cluster of workstations and are reported herein.

Multi-Objective Genetic and Fuzzy Approaches in Rule Mining Problem of Knowledge Discovery in Databases

2014 ◽  
pp. 147-179
Author(s):  
Harihar Kalia ◽  
Satchidananda Dehuri ◽  
Ashish Ghosh
Keyword(s):  
Knowledge Discovery ◽  
Optimization Problems ◽  
Fuzzy Rule ◽  
Numerical Data ◽  
Knowledge Discovery In Databases ◽  
Rule Mining ◽  
Boundary Problems ◽  
Multi Objective ◽  
Advantages And Disadvantages

Knowledge Discovery in Databases (KDD) is the process of automatically searching patterns from large volumes of data by using specific data mining techniques. Classification, association, and associative classification (integration of classification and association) rule mining are popularly used rule mining techniques in KDD for harvesting knowledge in the form of rule. The classical rule mining techniques based on crisp sets have bad experience of “sharp boundary problems” while mining rule from numerical data. Fuzzy rule mining approaches eliminate these problems and generate more human understandable rules. Several quality measures are used in order to quantify the quality of these discovered rules. However, most of these objectives/criteria are conflicting to each other. Thus, fuzzy rule mining problems are modeled as multi-objective optimization problems rather than single objective. Due to the ability of finding diverse trade-off solutions for several objectives in a single run, multi-objective genetic algorithms are popularly employed in rule mining. In this chapter, the authors discuss the multi-objective genetic-fuzzy approaches used in rule mining along with their advantages and disadvantages. In addition, some of the popular applications of these approaches are discussed.

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