Fuzzy Fitness Assignment in an Interactive Genetic Algorithm for a Cartoon Face Search

Nazerfard, Ehsan; Nishio, K.; Murakami, Masayuki; Mizutani, Eiji; Honda, Nakaji; Sanchez, Élie; Shibata, Takanori; Zadeh, Lotfi A.

doi:10.1142/9789814261296_0010

Cited by 19 publications

(10 citation statements)

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“…Nevertheless, a small number or a reduced population affects the productivity of GAs negatively, and as a result, improvements on the conventional GA are proposed. These improvements depend on the size of the population and on the fitness prediction for all the individuals not evaluated by the user [17,30]. Thus, they deal with an algorithm of M individuals, and only a subset of N is shown to the user.…”

Section: Fitness Predictive Genetic Algorithmmentioning

confidence: 99%

Optimization Using Genetic Algorithms with Micropopulations

Sáez

2008

Optimization Techniques for Solving Complex Problems

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Section: Fitness Predictive Genetic Algorithmmentioning

confidence: 99%

Optimization Using Genetic Algorithms with Micropopulations

Sáez

2008

Optimization Techniques for Solving Complex Problems

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“…Nevertheless A small number or a reduced population affects negatively the productivity of the GAs and, as a result, improvements over the conventional GA are proposed. These improvements depend on the size of the population and the fitness prediction of all the individuals which are not evaluated by the user, [15], [27]. Thus it deals with an algorithm of M individuals and only a subset of N are shown to the user.…”

Section: Fitness Predictive Genetic Algorithmmentioning

confidence: 99%

An Experimental Comparative Study for Interactive Evolutionary Computation Problems

Sáez

Isasi

Segovia

et al. 2006

Lecture Notes in Computer Science

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Abstract. This 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.

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“…Instead of rating chromosomes on certain values (fitness), Interactive GeneticAlgorithms rate chromosomes interactively based on evaluations of a user. Interactive Genetic Algorithms are useful for searching through an object space when assessment criteria are not well defined enough to form explicit rules [16]. Some applications of Genetic Algorithms are interactive design aid system [15], montage face image generation [5] and line drawing [1].…”

Section: Muscle Tuningmentioning

confidence: 99%

Exporting Vector Muscles for Facial Animation

2003

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Abstract. In this paper we introduce a method of exporting vector muscles from one 3D face to another for facial animation. Starting from a 3D face with an extended version of Waters' linear muscle system, we transfer the linear muscles to a target 3D face. We also transfer the region division, which is used to increase the performance of the muscle as well as to control the animation. The human involvement is just as simple as selecting the faces which shows the most natural facial expressions in the animator's view. The method allows the transfer of the animation to a new 3D model within a short time. The transferred muscles can then be used to create new animations.

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Fuzzy Fitness Assignment in an Interactive Genetic Algorithm for a Cartoon Face Search

Cited by 19 publications

References 0 publications

Optimization Using Genetic Algorithms with Micropopulations

Optimization Using Genetic Algorithms with Micropopulations

An Experimental Comparative Study for Interactive Evolutionary Computation Problems

Exporting Vector Muscles for Facial Animation

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