In the area of artificial intelligence, the development of Evolutionary Algorithms (EAs) has
been very active, especially in the last decade. These algorithms started to evolve when
scientists from various regions of the world applied the principles of evolution to algorithmic
search and problem solving. EAs have been utilised successfully in diverse complex
application areas. Their success in tackling hard problems has been the engine of the field of
Evolutionary Computation (EC). Nowadays, EAs are considered to be the best solution to
use when facing a hard search or optimisation problem.
Various improvements are continually being made with the design of new operators,
hybrid models, among others. A very important example of such improvements is the use of
parallel models of GAs (PGAs). PGAs have received widespread attention from various
researchers as they have proved to be more effective than panmictic GAs, especially in terms
of efficacy and speedup.
This thesis focuses on, and investigates, cellular Genetic Algorithms (cGAs)-a
competitive variant of parallel GAs. In a cGA, the tentative solutions evolve in overlapped
neighbourhoods, allowing smooth diffusion of the solutions. The benefits derived from using
cGAs come not only from flexibility gains and their fitness to the objective target in
combination with a robust behaviour but also from their high performance and amenability
to implementation using advanced custom silicon chip technologies. Nowadays, cGAs are
considered as adaptable concepts for solving problems, especially complex optimisation
problems. Due to their structural characteristics, cGAs are able to promote an adequate
exploration/exploitation trade-off and thus maintain genetic diversity. Moreover, cGAs are
characterised as being massively parallel and easy to implement.
The structural characteristics inherited in a cGA provide an active area for investigation.
Because of the vital role grid structure plays in determining the effectiveness of the
algorithm, cellular dimensionality is the main issue to be investigated here. The
implementation of cGAs is commonly carried out on a one- or two-dimensional structure.
Studies that investigate higher cellular dimensions are lacking. Accordingly, this research
focuses on cGAs that are implemented on a three-dimensional structure. Having a structure with three dimensions, specifically a cubic structure, facilitates faster spreading of solutions
due to the shorter radius and denser neighbourhood that result from the vertical expansion of
cells. In this thesis, a comparative study of cellular dimensionality is conducted. Simulation
results demonstrate higher performance achieved by 3D-cGAs over their 2D-cGAs
counterparts. The direct implementation of 3D-cGAs on the new advanced 3D-IC
technology will provide added benefits such as higher performance combined with a
reduction in interconnection delays, routing length, and power consumption.
The maintenance of system reliability and availability is a major concern that must be
addressed. A system is likely to fail due to either hard or soft errors. Therefore, detecting a
fault before it deteriorates system performance is a crucial issue. Single Event Upsets
(SEUs), or soft errors, do not cause permanent damage to system functionality, and can be
handled using fault-tolerant techniques. Existing fault-tolerant techniques include hardware
or software fault tolerance, or a combination of both. In this thesis, fault-tolerant techniques
that mitigate SEUs at the algorithmic level are explored and the inherent abilities of cGAs to
deal with these errors are investigated. A fault-tolerant technique and several mitigation
techniques are also proposed, and faulty critical data are evaluated critical fault scenarios
(stuck at ‘1’ and stuck at ‘0’ faults) are taken into consideration. Chief among several test
and real world problems is the problem of determining the attitude of a vehicle using a
Global Positioning System (GPS), which is an example of hard real-time application. Results
illustrate the ability of cGAs to maintain their functionality and give an adequate
performance even with the existence of up to 40% errors in fitness score cells.
The final aspect investigated in this thesis is the dynamic characteristic of cGAs. cGAs,
and EAs in general, are known to be stochastic search techniques. Hence, adaptive systems
are required to continue to perform effectively in a changing environment, particularly when
tackling real-world problems. The adaptation in cellular engines is mainly achieved through
dynamic balancing between exploration and exploitation. This area has received
considerable attention from researchers who focus on improving the algorithmic
performance without incurring additional computational effort.
The structural properties and the genetic operations provide ways to control selection
pressure and, as a result, the exploration/exploitation trade-off. In this thesis, the genetic
operations of cGAs, particularly the selection aspect and their influence on the search
process, are investigated in order to dynamically control the exploration/exploitation trade-off.
Two adaptive-dynamic techniques that use genetic diversity and convergence speeds to
guide the search are proposed. Results obtained by evaluating the proposed approaches on a test bench of diverse-characteristic real-world and test problems showed improvement in
dynamic cGAs performance over their static counterparts and other dynamic cGAs. For
example, the proposed Diversity-Guided 3D-cGA outperformed all the other dynamic cGAs
evaluated by obtaining a higher search success rate that reached to 55%
