10,190 research outputs found
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
Development of an automated aircraft subsystem architecture generation and analysis tool
Purpose – The purpose of this paper is to present a new computational framework to address future
preliminary design needs for aircraft subsystems. The ability to investigate multiple candidate
technologies forming subsystem architectures is enabled with the provision of automated architecture
generation, analysis and optimization. Main focus lies with a demonstration of the frameworks
workings, as well as the optimizers performance with a typical form of application problem.
Design/methodology/approach – The core aspects involve a functional decomposition, coupled
with a synergistic mission performance analysis on the aircraft, architecture and component levels.
This may be followed by a complete enumeration of architectures, combined with a user defined
technology filtering and concept ranking procedure. In addition, a hybrid heuristic optimizer, based on
ant systems optimization and a genetic algorithm, is employed to produce optimal architectures in both
component composition and design parameters. The optimizer is tested on a generic architecture
design problem combined with modified Griewank and parabolic functions for the continuous space.
Findings – Insights from the generalized application problem show consistent rediscovery of the
optimal architectures with the optimizer, as compared to a full problem enumeration. In addition
multi-objective optimization reveals a Pareto front with differences in component composition as well
as continuous parameters.
Research limitations/implications – This paper demonstrates the frameworks application on a
generalized test problem only. Further publication will consider real engineering design problems.
Originality/value – The paper addresses the need for future conceptual design methods of complex
systems to consider a mixed concept space of both discrete and continuous nature via automated methods
VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.
The Genetic Algorithm is an area in the field of Artificial Intelligence that is
founded on the principles of biological evolution. Visualization techniques help in
understanding the searching behaviour of Genetic Algorithm. lt also makes possible
the user interactions during the searching process. It is noted that active user
intervention increases the acceleration of Genetic Algorithm towards an optimal
solution.
In proposed research work, the user is aided by a visualization based on the
representation of multidimensional Genetic Algorithm data on 2-0 space. The aim of
the proposed approach is to study the benefit of using visualization techniques to
explorer Genetic Algorithm data based on gene values. The user participates in the
search by proposing a new individual. This is difTerent from existing Interactive
Genetic Algorithm in which selection and evaluation of solutions is done by the users.
A tool termed as VIGA-20 (Visualization of Genetic Algorithm using 2-0 Graph) is
implemented to accomplish this goal. This visual tool enables the display of the
evolution of gene values from generation to generation to observing and analysing the
behaviour of the search space with user interactions. Individuals for the next
generation are selected by using the objective function. Hence, a novel humanmachine
interaction is developed in the proposed approach.
The efficiency of the proposed approach is evaluated by two benchmark
functions. The analysis and comparison of VIGA-20 is based on convergence test
against the results obtained from the Simple Genetic Algorithm. This comparison is
based on the same parameters except for the interactions of the user. The application
of proposed approach is the modelling the branching structures by deriving a rule
from best solution of VIGA-20. The comparison of results is based on the different
user's perceptions, their involvement in the VIGA-20 and the difference of the fitness
convergence as compared to Simple Genetic Algorithm
An Interactive Visualisation System for Engineering Design using Evolutionary Computing
This thesis describes a system designed to promote collaboration between the human and computer
during engineering design tasks. Evolutionary algorithms (in particular the genetic algorithm) can
find good solutions to engineering design problems in a small number of iterations, but a review of
the interactive evolutionary computing literature reveals that users would benefit from
understanding the design space and having the freedom to direct the search. The main objective of
this research is to fulfil a dual requirement: the computer should generate data and analyse the
design space to identify high performing regions in terms of the quality and robustness of solutions,
while at the same time the user should be allowed to interact with the data and use their experience
and the information provided to guide the search inside and outside regions already found.
To achieve these goals a flexible user interface was developed that links and clarifies the
research fields of evolutionary computing, interactive engineering design and multivariate
visualisation. A number of accessible visualisation techniques were incorporated into the system.
An innovative algorithm based on univariate kernel density estimation is introduced that quickly
identifies the relevant clusters in the data from the point of view of the original design variables or
a natural coordinate system such as the principal or independent components. The robustness of
solutions inside a region can be investigated by novel use of 'negative' genetic algorithm search to
find the worst case scenario. New high performance regions can be discovered in further runs of
the evolutionary algorithm; penalty functions are used to avoid previously found regions. The
clustering procedure was also successfully applied to multiobjective problems and used to force the
genetic algorithm to find desired solutions in the trade-off between objectives.
The system was evaluated by a small number of users who were asked to solve simulated
engineering design scenarios by finding and comparing robust regions in artificial test functions.
Empirical comparison with benchmark algorithms was inconclusive but it was shown that even a
devoted hybrid algorithm needs help to solve a design task. A critical analysis of the feedback and
results suggested modifications to the clustering algorithm and a more practical way to evaluate the
robustness of solutions. The system was also shown to experienced engineers working on their real
world problems, new solutions were found in pertinent regions of objective space; links to the
artefact aided comparison of results. It was confirmed that in practice a lot of design knowledge is
encoded into design problems but experienced engineers use subjective knowledge of the problem
to make decisions and evaluate the robustness of solutions. So the full potential of the system was
seen in its ability to support decision making by supplying a diverse range of alternative design
options, thereby enabling knowledge discovery in a wide-ranging number of applications
Open-ended evolution to discover analogue circuits for beyond conventional applications
This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s10710-012-9163-8. Copyright @ Springer 2012.Analogue circuits synthesised by means of open-ended evolutionary algorithms often have unconventional designs. However, these circuits are typically highly compact, and the general nature of the evolutionary search methodology allows such designs to be used in many applications. Previous work on the evolutionary design of analogue circuits has focused on circuits that lie well within analogue application domain. In contrast, our paper considers the evolution of analogue circuits that are usually synthesised in digital logic. We have developed four computational circuits, two voltage distributor circuits and a time interval metre circuit. The approach, despite its simplicity, succeeds over the design tasks owing to the employment of substructure reuse and incremental evolution. Our findings expand the range of applications that are considered suitable for evolutionary electronics
Evolutionary Computing and Second generation Wavelet Transform optimization: Current State of the Art
The Evolutionary Computation techniques are exposed to number of domains to achieve optimization. One of those domains is second generation wavelet transformations for image compression. Various types of Lifting Schemes are being introduced in recent literature. Since the growth in Lifting Schemes is in an incremental way and new types of Lifting Schemes are appearing continually. In this context, developing flexible and adaptive optimization approaches is a severe challenge. Evolutionary Computing based lifting scheme optimization techniques are a valuable technology to achieve better results in image compression. However, despite the variety of such methods described in the literature in recent years, security tools incorporating anomaly detection functionalities are just starting to appear, and several important problems remain to be solved. In this paper, we present a review of the most well-known EC approaches for optimizing Secondary level Wavelet transformations
Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks
This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the NP-complete multicast routing problem (MRP). The main contribution is the extension of PSO from continuous domain to the binary or discrete domain. Firstly, a novel bi-velocity strategy is developed to represent possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP where 1 stands for a node being selected to construct the multicast tree while 0 stands for being otherwise. Secondly, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the OR-library (Operation Research Library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly as it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on GA, ACO, and PSO
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