17,537 research outputs found
Fitness Uniform Optimization
In evolutionary algorithms, the fitness of a population increases with time
by mutating and recombining individuals and by a biased selection of more fit
individuals. The right selection pressure is critical in ensuring sufficient
optimization progress on the one hand and in preserving genetic diversity to be
able to escape from local optima on the other hand. Motivated by a universal
similarity relation on the individuals, we propose a new selection scheme,
which is uniform in the fitness values. It generates selection pressure toward
sparsely populated fitness regions, not necessarily toward higher fitness, as
is the case for all other selection schemes. We show analytically on a simple
example that the new selection scheme can be much more effective than standard
selection schemes. We also propose a new deletion scheme which achieves a
similar result via deletion and show how such a scheme preserves genetic
diversity more effectively than standard approaches. We compare the performance
of the new schemes to tournament selection and random deletion on an artificial
deceptive problem and a range of NP-hard problems: traveling salesman, set
covering and satisfiability.Comment: 25 double-column pages, 12 figure
Genetic algorithms with elitism-based immigrants for dynamic shortest path problem in mobile ad hoc networks
This article is posted here with permission from the IEEE - Copyright @ 2009 IEEEIn recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks (ANNs), genetic algorithms (GAs), particle swarm optimization (PSO), etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless sensor network (WSN), etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP problem turns out to be a dynamic optimization problem (DOP) in MANETs. In this paper, we propose to use elitism-based immigrants GA (EIGA) to solve the dynamic SP problem in MANETs. We consider MANETs as target systems because they represent new generation wireless networks. The experimental results show that the EIGA can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1
A Survey on Software Testing Techniques using Genetic Algorithm
The overall aim of the software industry is to ensure delivery of high
quality software to the end user. To ensure high quality software, it is
required to test software. Testing ensures that software meets user
specifications and requirements. However, the field of software testing has a
number of underlying issues like effective generation of test cases,
prioritisation of test cases etc which need to be tackled. These issues demand
on effort, time and cost of the testing. Different techniques and methodologies
have been proposed for taking care of these issues. Use of evolutionary
algorithms for automatic test generation has been an area of interest for many
researchers. Genetic Algorithm (GA) is one such form of evolutionary
algorithms. In this research paper, we present a survey of GA approach for
addressing the various issues encountered during software testing.Comment: 13 Page
Paired Comparisons-based Interactive Differential Evolution
We propose Interactive Differential Evolution (IDE) based on paired
comparisons for reducing user fatigue and evaluate its convergence speed in
comparison with Interactive Genetic Algorithms (IGA) and tournament IGA. User
interface and convergence performance are two big keys for reducing Interactive
Evolutionary Computation (IEC) user fatigue. Unlike IGA and conventional IDE,
users of the proposed IDE and tournament IGA do not need to compare whole
individuals each other but compare pairs of individuals, which largely
decreases user fatigue. In this paper, we design a pseudo-IEC user and evaluate
another factor, IEC convergence performance, using IEC simulators and show that
our proposed IDE converges significantly faster than IGA and tournament IGA,
i.e. our proposed one is superior to others from both user interface and
convergence performance points of view
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