8 research outputs found
Anisotropic selection in cellular genetic algorithms
In this paper we introduce a new selection scheme in cellular genetic
algorithms (cGAs). Anisotropic Selection (AS) promotes diversity and allows
accurate control of the selective pressure. First we compare this new scheme
with the classical rectangular grid shapes solution according to the selective
pressure: we can obtain the same takeover time with the two techniques although
the spreading of the best individual is different. We then give experimental
results that show to what extent AS promotes the emergence of niches that
support low coupling and high cohesion. Finally, using a cGA with anisotropic
selection on a Quadratic Assignment Problem we show the existence of an
anisotropic optimal value for which the best average performance is observed.
Further work will focus on the selective pressure self-adjustment ability
provided by this new selection scheme
On the Influence of Selection Operators on Performances in Cellular Genetic Algorithms
In this paper, we study the influence of the selective pressure on the
performance of cellular genetic algorithms. Cellular genetic algorithms are
genetic algorithms where the population is embedded on a toroidal grid. This
structure makes the propagation of the best so far individual slow down, and
allows to keep in the population potentially good solutions. We present two
selective pressure reducing strategies in order to slow down even more the best
solution propagation. We experiment these strategies on a hard optimization
problem, the quadratic assignment problem, and we show that there is a value
for of the control parameter for both which gives the best performance. This
optimal value does not find explanation on only the selective pressure,
measured either by take over time and diversity evolution. This study makes us
conclude that we need other tools than the sole selective pressure measures to
explain the performances of cellular genetic algorithms
Centric selection: a way to tune the exploration/exploitation trade-off
In this paper, we study the exploration / exploitation trade-off in cellular
genetic algorithms. We define a new selection scheme, the centric selection,
which is tunable and allows controlling the selective pressure with a single
parameter. The equilibrium model is used to study the influence of the centric
selection on the selective pressure and a new model which takes into account
problem dependent statistics and selective pressure in order to deal with the
exploration / exploitation trade-off is proposed: the punctuated equilibria
model. Performances on the quadratic assignment problem and NK-Landscapes put
in evidence an optimal exploration / exploitation trade-off on both of the
classes of problems. The punctuated equilibria model is used to explain these
results
A Species-Conserving Genetic Algorithm for Multimodal Optimization
The problem of multimodal functional optimization has been addressed by much research producing many different search techniques. Niche Genetic Algorithms is one area that has attempted to solve this problem. Many Niche Genetic Algorithms use some type of radius. When multiple optima occur within the radius, these algorithms have a difficult time locating them. Problems that have arbitrarily close optima create a greater problem. This paper presents a new Niche Genetic Algorithm framework called Dynamic-radius Species-conserving Genetic Algorithm. This new framework extends existing Genetic Algorithm research.
This new framework enhances an existing Niche Genetic Algorithm in two ways. As the name implies the radius of the algorithm varies during execution. A uniform radius can cause issues if it is not set correctly during initialization. A dynamic radius compensates for these issues. The framework does not attempt to locate all of the optima in a single pass. It attempts to find some optima and then uses a tabu list to exclude those areas of the domain for future iterations. To exclude these previously located optima, the framework uses a fitness sharing approach and a seed exclusion approach. This new framework addresses many areas of difficulty in current multimodal functional optimization research.
This research used the experimental research methodology. A series of classic benchmark functional optimization problems were used to compare this framework to other algorithms. These other algorithms represented classic and current Niche Genetic Algorithms.
Results from this research show that this new framework does very well in locating optima in a variety of benchmark functions. In functions that have arbitrarily close optima, the framework outperforms other algorithms. Compared to other Niche Genetic Algorithms the framework does equally well in locating optima that are not arbitrarily close. Results indicate that varying the radius during execution and the use of a tabu list assists in solving functional optimization problems for continuous functions that have arbitrarily close optima
Dynamic and fault tolerant three-dimensional cellular genetic algorithms
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%