6,970 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
Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria using Interactive Genetic Algorithms
This paper emphasizes the necessity of formally bringing qualitative and
quantitative criteria of ergonomic design together, and provides a novel
complementary design framework with this aim. Within this framework, different
design criteria are viewed as optimization objectives; and design solutions are
iteratively improved through the cooperative efforts of computer and user. The
framework is rooted in multi-objective optimization, genetic algorithms and
interactive user evaluation. Three different algorithms based on the framework
are developed, and tested with an ergonomic chair design problem. The parallel
and multi-objective approaches show promising results in fitness convergence,
design diversity and user satisfaction metrics
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
Towards a human eye behavior model by applying Data Mining Techniques on Gaze Information from IEC
In this paper, we firstly present what is Interactive Evolutionary
Computation (IEC) and rapidly how we have combined this artificial intelligence
technique with an eye-tracker for visual optimization. Next, in order to
correctly parameterize our application, we present results from applying data
mining techniques on gaze information coming from experiments conducted on
about 80 human individuals
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
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
Adopt an optimal location using a genetic algorithm for audio steganography
With the development of technologies, most of the users utilizing the Internet for transmitting information from one place to another place. The transmitted data may be affected because of the intermediate user. Therefore, the steganography approach is applied for managing the secret information. Here audio steganography is utilized to maintain the secret information by hiding the image into the audio files. In this work, discrete cosine transforms, and discrete wavelet transform is applied to perform the Steganalysis process. The optimal hiding location has been identified by using the optimization technique called a genetic algorithm. The method utilizes the selection, crossover and mutation operators for selecting the best location. The chosen locations are difficult to predict by unauthorized users because the embedded location is varied from information to information. Then the efficiency of the system ensures the high PSNR, structural similarity index (SSIM), minimum mean square error value and Jaccard, which is evaluated on the audio Steganalysis dataset
Learning to Extract Action Descriptions from Narrative Text
This paper focuses on the mapping of natural language sentences in written stories to a structured knowledge representation. This process yields an exponential explosion of instance combinations since each sentence may contain a set of ambiguous terms, each one giving place to a set of instance candidates. The selection of the best combination of instances is a structured classification problem that yields a highdemanding combinatorial optimization problem which, in this paper, is approached by a novel and efficient formulation of a genetic algorithm, which is able to exploit the conditional independence among variables, while improving the parallel scalability. The automatic rating of the resulting set of instance combinations, i.e. possible text interpretations, demands an exhaustive exploitation of the state-of-the-art resources in natural language processing to feed the system with pieces of evidence to be fused by the proposed framework. In this sense, a mapping framework able to reason with uncertainty, to integrate supervision, and evidence from external sources, was adopted. To improve the generalization capacity while learning from a limited amount of annotated data, a new constrained learning algorithm for Bayesian networks is introduced. This algorithm bounds the search space through a set of constraints which encode information on mutually exclusive values. The mapping of natural language utterances to a structured knowledge representation is important in the context of game construction, e.g. in an RPG setting, as it alleviates the manual knowledge acquisition bottleneck. The effectiveness of the proposed algorithm is evaluated on a set of three stories, yielding nine experiments. Our mapping framework yields performance gains in predicting the most likely structured representations of sentences when compared with a baseline algorithm
Evolutionary Decomposition of Complex Design Spaces
This dissertation investigates the support of conceptual engineering design through the
decomposition of multi-dimensional search spaces into regions of high performance. Such
decomposition helps the designer identify optimal design directions by the elimination of
infeasible or undesirable regions within the search space. Moreover, high levels of
interaction between the designer and the model increases overall domain knowledge and
significantly reduces uncertainty relating to the design task at hand.
The aim of the research is to develop the archetypal Cluster Oriented Genetic Algorithm
(COGA) which achieves search space decomposition by using variable mutation
(vmCOGA) to promote diverse search and an Adaptive Filter (AF) to extract solutions of
high performance [Parmee 1996a, 1996b]. Since COGAs are primarily used to decompose
design domains of unknown nature within a real-time environment, the elimination of
apriori knowledge, speed and robustness are paramount. Furthermore COGA should
promote the in-depth exploration of the entire search space, sampling all optima and the
surrounding areas. Finally any proposed system should allow for trouble free integration
within a Graphical User Interface environment.
The replacement of the variable mutation strategy with a number of algorithms which
increase search space sampling are investigated. Utility is then increased by incorporating
a control mechanism that maintains optimal performance by adapting each algorithm
throughout search by means of a feedback measure based upon population convergence.
Robustness is greatly improved by modifying the Adaptive Filter through the introduction
of a process that ensures more accurate modelling of the evolving population.
The performance of each prospective algorithm is assessed upon a suite of two-dimensional
test functions using a set of novel performance metrics. A six dimensional
test function is also developed where the areas of high performance are explicitly known,
thus allowing for evaluation under conditions of increased dimensionality. Further
complexity is introduced by two real world models described by both continuous and
discrete parameters. These relate to the design of conceptual airframes and cooling hole
geometries within a gas turbine.
Results are promising and indicate significant improvement over the vmCOGA in terms of
all desired criteria. This further supports the utilisation of COGA as a decision support
tool during the conceptual phase of design.British Aerospace plc, Warton and
Rolls Royce plc, Filto
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