CCE Theses and Dissertations

Campus Access Only

All rights reserved. This publication is intended for use solely by faculty, students, and staff of Nova Southeastern University. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, now known or later developed, including but not limited to photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author or the publisher.

Date of Award


Document Type

Dissertation - NSU Access Only

Degree Name

Doctor of Philosophy in Computer Science (CISD)


Graduate School of Computer and Information Sciences


Michael J. Lazlo

Committee Member

Wei Li

Committee Member

Sumitra Mukherjee


coevolution, cooperative


Cooperative coevolutionary algorithms (CCEA) are a form of evolutionary algorithm that is applicable when the problem can be decomposed into components. Each component is assigned a subpopulation that evolves a good solution to the subproblem. To compute an individual's fitness, it is combined with collaborators drawn from the other subpopulations to form a complete solution. The individual's fitness is a function of this solution's fitness. The contributors to the comprehensive fitness formula are known as collaborators. The number of collaborators allowed from each subpopulation is called pool size. It has been shown that the outcome of the CCEA can be improved by allowing multiple collaborators from each subpopulation. This results in larger pool sizes, but improved fitness. The improvement in fitness afforded by larger pool sizes is offset by increased calculation costs. This study targeted the pool size parameter of CCEAs by devising dynamic strategies for the assignment of pool size to regulate selection pressure. Subpopulations were rewarded with a larger pool size or penalized with a smaller pool size based on measures of their diversity and/or fitness. Measures for population diversity and fitness used in this study were derived from various works involving evolutionary computation. This study showed that dynamically assigning pool size based on these measures of the diversity and fitness of the subpopulations can yield improved fitness results with significant reduction in calculation costs over statically assigned pool sizes.

To access this thesis/dissertation you must have a valid OR email address and create an account for NSUWorks.

Free My Thesis

If you are the author of this work and would like to grant permission to make it openly accessible to all, please click the Free My Thesis button.

  Contact Author

  Link to NovaCat