Optimization of multistage vapour compression systems using genetic algorithms. Part 1: Vapour compression system model

2001 ◽  
Vol 25 (9) ◽  
pp. 803-812 ◽  
Author(s):  
A. C. West ◽  
S. A. Sherif
Keyword(s):  
Genetic Algorithms ◽  
System Model ◽  
Compression System ◽  
Vapour Compression

Designing the Dynamic Behavior of an Engine Suspension System Through Genetic Algorithms

2001 ◽  
Vol 123 (4) ◽  
pp. 480-486 ◽  
Author(s):  
Niccolo` Baldanzini ◽  
Davide Caprioli ◽  
Marco Pierini
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Dynamic Behavior ◽  
Multibody System ◽  
Suspension System ◽  
Innovative Approach ◽  
System Model ◽  
Design Stage ◽  
Dynamic Design ◽  
Dynamic Constraints

This work presents an innovative approach to dynamic design that has the significant advantage of allowing the dynamic requirements to be specified from the earliest design stage. The method applies genetic algorithms to optimize the dynamic behavior of the engine-subframe system and its links to the chassis. The optimization minimizes the sum of the amplitudes of the forces transmitted to the chassis from each mounting, while complying with the static and dynamic constraints. The genetic algorithm was applied to a multibody system model of the engine-subframe system and its links to derive new, improved configurations.

Using Genetic Algorithms to Explore Pattern Recognition in the Immune System

1993 ◽  
Vol 1 (3) ◽  
pp. 191-211 ◽  
Author(s):  
Stephanie Forrest ◽  
Brenda Javornik ◽  
Robert E. Smith ◽  
Alan S. Perelson
Keyword(s):  
Pattern Recognition ◽  
Genetic Algorithms ◽  
Immune System ◽  
System Model ◽  
Central Component ◽  
Simulation Experiments ◽  
Immune Systems ◽  
Fitness Sharing ◽  
The Individual ◽  
Binary Strings

This paper describes an immune system model based on binary strings. The purpose of the model is to study the pattern-recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of the model. The paper reports simulation experiments on two pattern-recognition problems that are relevant to natural immune systems. Finally, it reviews the relation between the model and explicit fitness-sharing techniques for genetic algorithms, showing that the immune system model implements a form of implicit fitness sharing.

Genetic Optimization Applied to the Dynamic Design of an Engine Cradle

1999 ◽  
Author(s):  
Niccolò Baldanzini ◽  
Paolo Citti ◽  
Marco Pierini
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Dynamic Behavior ◽  
Multibody System ◽  
System Model ◽  
Design Stage ◽  
Genetic Optimization ◽  
Dynamic Design ◽  
Dynamic Constraints ◽  
Engine Cradle

Abstract An innovative approach to dynamic design is presented that has the notable advantage of allowing the dynamic requirements to be specified from the earliest design stage. The method applies genetic algorithms to optimize the dynamic behavior of the engine-cradle system and its links to the chassis. The optimization minimizes the sum of the amplitudes of the forces transmitted to the chassis, while complying with the static and dynamic constraints. The genetic algorithm was applied to a multibody system model of the engine-cradle system and its links to derive new, improved configurations.

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