44,147 research outputs found
An experimental comparative study for interactive evolutionary computation problems
Proceeding of: EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC, Budapest, Hungary, April 10-12, 2006This paper presents an objective experimental comparative study between four algorithms: the Genetic Algorithm, the Fitness Prediction Genetic Algorithm, the Population Based Incremental Learning algorithm and the purposed method based on the Chromosome Appearance Probability Matrix. The comparative is done with a non subjective evaluation function. The main objective is to validate the efficiency of several methods in Interactive Evolutionary Computation environments. The most important constraint of working within those environments is the user interaction, which affects the results adding time restrictions for the experimentation stage and subjectivity to the validation. The experiments done in this paper replace user interaction with several approaches avoiding user limitations. So far, the results show the efficiency of the purposed algorithm in terms of quality of solutions and convergence speed, two known keys to decrease the user fatigue.This article has been financed by the Spanish founded research MCyT project OPLINK, Ref: TIN2006-08818-C04-02
Computation Environments, An Interactive Semantics for Turing Machines (which P is not equal to NP considering it)
To scrutinize notions of computation and time complexity, we introduce and
formally define an interactive model for computation that we call it the
\emph{computation environment}. A computation environment consists of two main
parts: i) a universal processor and ii) a computist who uses the computability
power of the universal processor to perform effective procedures. The notion of
computation finds it meaning, for the computist, through his
\underline{interaction} with the universal processor.
We are interested in those computation environments which can be considered
as alternative for the real computation environment that the human being is its
computist. These computation environments must have two properties: 1- being
physically plausible, and 2- being enough powerful.
Based on Copeland' criteria for effective procedures, we define what a
\emph{physically plausible} computation environment is.
We construct two \emph{physically plausible} and \emph{enough powerful}
computation environments: 1- the Turing computation environment, denoted by
, and 2- a persistently evolutionary computation environment, denoted by
, which persistently evolve in the course of executing the computations.
We prove that the equality of complexity classes and
in the computation environment conflicts with the
\underline{free will} of the computist.
We provide an axiomatic system for Turing computability and
prove that ignoring just one of the axiom of , it would not be
possible to derive from the rest of axioms.
We prove that the computist who lives inside the environment , can never
be confident that whether he lives in a static environment or a persistently
evolutionary one.Comment: 33 pages, interactive computation, P vs N
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
Improving problem definition through interactive evolutionary computation
Poor definition and uncertainty are primary characteristics of conceptual design processes. During the initial stages of these generally human-centric activities, little knowledge pertaining to the problem at hand may be available. The degree of problem definition will depend on information available in terms of appropriate variables, constraints, and both quantitative and qualitative objectives. Typically, the problem space develops with information gained in a dynamical process in which design optimization plays a secondary role, following the establishment of a sufficiently well-defined problem domain. This paper concentrates on background human-computer interaction relating to the machine-based generation of high-quality design information that, when presented in an appropriate manner to the designer, supports a better understanding of a problem domain. Knowledge gained from such information combined with the experiential knowledge of the designer can result in a reformulation of the problem, providing increased definition and greater confidence in the machine-based representation. Conceptual design domains related to gas turbine blade cooling systems and a preliminary air frame configuration are introduced. These are utilized to illustrate the integration of interactive evolutionary strategies that support the extraction of optimal design information, its presentation to the designer, and subsequent human-based modification of the design domain based on knowledge gained from the information received. An experimental iterative designer or evolutionary search process resulting in a better understanding of the problem and improved machine-based representation of the design domain is thus established
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
Comparing Evolutionary Operators, Search Spaces, and Evolutionary Algorithms in the Construction of Facial Composites
Facial composite construction is one of the most successful applications of interactive evolutionary computation.
In spite of this, previous work in the area of composite construction has not investigated the
algorithm design options in detail. We address this issue with four experiments. In the first experiment a
sorting task is used to identify the 12 most salient dimensions of a 30-dimensional search space. In the second
experiment the performances of two mutation and two recombination operators for interactive genetic
algorithms are compared. In the third experiment three search spaces are compared: a 30-dimensional
search space, a mathematically reduced 12-dimensional search space, and a 12-dimensional search space
formed from the 12 most salient dimensions. Finally, we compare the performances of an interactive
genetic algorithm to interactive differential evolution. Our results show that the facial composite construction
process is remarkably robust to the choice of evolutionary operator(s), the dimensionality of the search
space, and the choice of interactive evolutionary algorithm. We attribute this to the imprecise nature of human
face perception and differences between the participants in how they interact with the algorithms.
Povzetek: Kompozitna gradnja obrazov je ena izmed najbolj uspešnih aplikacij interaktivnega evolucijskega
ra?cunanja. Kljub temu pa do zdaj na podro?cju kompozitne gradnje niso bile podrobno raziskane
možnosti snovanja algoritma. To vprašanje smo obravnavali s štirimi poskusi. V prvem je uporabljeno
sortiranje za identifikacijo 12 najbolj izstopajo?cih dimenzij 30-dimenzionalnega preiskovalnega prostora.
V drugem primerjamo u?cinkovitost dveh mutacij in dveh rekombinacijskih operaterjev za interaktivni
genetski algoritem. V tretjem primerjamo tri preiskovalne prostore: 30-dimenzionalni, matemati?cno reducirani
12-dimenzionalni in 12-dimenzionalni prostor sestavljen iz 12 najpomembnejših dimenzij. Na
koncu smo primerjali uspešnost interaktivnega genetskega algoritma z interaktivno diferencialno evolucijo.
Rezultati kaĹľejo, da je proces kompozitne gradnje obrazov izredno robusten glede na izbiro evolucijskega
operatorja(-ev), dimenzionalnost preiskovalnega prostora in izbiro interaktivnega evolucijskega algoritma.
To pripisujemo nenatan?cni naravi percepcije in razlikam med interakcijami uporabnikov z algoritmom
Multi-agent evolutionary systems for the generation of complex virtual worlds
Modern films, games and virtual reality applications are dependent on
convincing computer graphics. Highly complex models are a requirement for the
successful delivery of many scenes and environments. While workflows such as
rendering, compositing and animation have been streamlined to accommodate
increasing demands, modelling complex models is still a laborious task. This
paper introduces the computational benefits of an Interactive Genetic Algorithm
(IGA) to computer graphics modelling while compensating the effects of user
fatigue, a common issue with Interactive Evolutionary Computation. An
intelligent agent is used in conjunction with an IGA that offers the potential
to reduce the effects of user fatigue by learning from the choices made by the
human designer and directing the search accordingly. This workflow accelerates
the layout and distribution of basic elements to form complex models. It
captures the designer's intent through interaction, and encourages playful
discovery
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