Genetic Algorithms Solve Combinatorial Optimisation Problems in the Calibration of Combustion Engines

2002 ◽  
pp. 45-56 ◽  
Author(s):  
K. Knödler ◽  
J. Poland ◽  
A. Mitterer ◽  
A. Zell
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimisation ◽  
Combustion Engines

scholarly journals Development of a Genetic Algorithm Based Search Strategy Suited For Design Optimisation of Internal Combustion Engines

2008 ◽  
Vol 31 (1) ◽  
pp. 127-138
Author(s):  
N.G Nalitolela ◽  
H. Kadete
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Internal Combustion Engines ◽  
Design Space ◽  
Parametric Design ◽  
Random Search ◽  
Internal Combustion ◽  
Combustion Engines ◽  
Design Optimisation ◽  
Adaptive Random Search

Engine design optimisation is a multi-objective, multi-domain problem in a discontinuous design space. The state of the art of optimisation techniques shows that only methods of direct and adaptive search are appropriate for this type of problem. These include, adaptive random search, simulated annealing, evolution strategies and genetic algorithms. Ofthese methods, the genetic algorithms have been shown to be the most suited for the optimisation of multi-modal response functions in a discontinuous design space. This paper considers the important characteristics of genetic algorithms and their adaptation for use in parametric design optimisation of internal combustion engines. In order to verify the basicfunctionality of the proposed optimisation strategy, a genetic algorithm based, optimisation software was developed and tested on a number of analytical functions, selected from optimisation literature, with satisfactory results.

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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