Genetic algorithms for modelling and optimisation

Genetic Algorithms for Multiple-Choice Optimisation Problems

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse ...

Breeder Genetic Algorithms for Airfoil Design Optimisation

A new version of Genetic Algorithms, the Breeder Genetic Algorithms, has been recently proposed in literature and successfully applied to the continuous parameter optimisation. In this paper we aim to test this technique against a classical discrete Genetic Algorithm on a typical optimisation problem in Aerodynamics, the problem of determining the coordinates of an airfoil given a surface press...

Using Neural Networks and Genetic Algorithms for Modelling and Multi-objective Optimal Heat Exchange through a Tube Bank

In this study, by using a multi-objective optimization technique, the optimal design points of forced convective heat transfer in tubular arrangements were predicted upon the size, pitch and geometric configurations of a tube bank. In this way, the main concern of the study is focused on calculating the most favorable geometric characters which may gain to a maximum heat exchange as well as a m...

A framework for modelling stochastic optimisation algorithms with Markov chains

Sequential Process Optimisation Using Genetic Algorithms

Locating good design solutions within a sequential process environment is necessary to improve the quality and overall productivity of the processes. Multi-objective, multi-stage sequential process design is a complex problem involving large number of design variables and sequential relationship between any two stages. The aim of this paper is to propose a novel framework to handle real-life se...