scholarly journals The table: an illustration of evolutionary design using genetic algorithms

1995 ◽  
Author(s):  
P.J. Bentley
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Design

Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

2014 ◽  
Vol 30 (4) ◽  
pp. 488-510 ◽  
Author(s):  
R. Jha ◽  
F. Pettersson ◽  
G. S. Dulikravich ◽  
H. Saxen ◽  
N. Chakraborti
Keyword(s):  
Genetic Algorithms ◽  
Data Driven ◽  
Evolutionary Design ◽  
Using Data

Evolutionary Cooperative Design Methodology: The Genetic Sculpture Park

Leonardo ◽  
2002 ◽  
Vol 35 (2) ◽  
pp. 193-196 ◽  
Author(s):  
Duncan Rowland ◽  
Frank Biocca
Keyword(s):  
Genetic Algorithms ◽  
Virtual Reality ◽  
Design Methodology ◽  
Computer Graphic ◽  
Three Dimensional ◽  
Evolutionary Design ◽  
Modeling Language ◽  
Theory Of Evolution ◽  
Virtual Reality Modeling Language ◽  
Complex Computer

The Genetic Sculpture Park seeks to engage artists and observers in a creative dialogue and to empower novices in the creation of complex computer-graphic models. Each visitor to the park experiences a unique set of sculptural forms and takes part in a cooperative conversation with the computer to produce more aesthetically pleasing designs. Inspired by Darwin's theory of evolution, the project uses genetic algorithms to allow visitors to “breed” forms tailored to their individual sense of aesthetics. In this article, the authors recount investigations into evolutionary design methodologies (using shampoo bottles and three-dimensional head models) and describe their implementation in the Genetic Sculpture Park, an interactive Java/Virtual Reality Modeling Language (VRML) world.

Genetic Algorithms—a Tool for OR?

1996 ◽  
Vol 47 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Kathryn A Dowsland
Keyword(s):  
Genetic Algorithms

Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

2018 ◽  
Vol 1 (1) ◽  
pp. 2-19
Author(s):  
Mahmood Sh. Majeed ◽  
Raid W. Daoud
Keyword(s):  
Genetic Algorithms ◽  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
New Method ◽  
Proper Solution ◽  
Artificial Intelligent ◽  
Crystal Fiber ◽  
Fitness Value ◽  
First Pass ◽  
Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

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