Genetic algorithm involving coevolution mechanism to search for effective genetic information

Handa, Hisashi; Baba, Norio; Katai, Osamu; Sawaragi, Tetsuo; Horiuchi, Tadashi

doi:10.1109/icec.1997.592427

Cited by 23 publications

(8 citation statements)

References 4 publications

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“…The used co-evolutionary system involves two genetic algorithm models [17]. The first GA model searches for a solution in a given environment, and the second GA model searches for useful genetic information in the first GA model.…”

Section: B Eyes Localizationmentioning

confidence: 99%

Face Localization Using an Effective Co-evolutionary Genetic Algorithm

Hajati

Lucas

Gao

2010

2010 International Conference on Digital Image Computing: Techniques and Applications

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In this paper, a new method for face localization in color images, which is based on co-evolutionary systems, is introduced. The proposed method uses a co-evolutionary system to locate the eyes in a face image. The used coevolutionary system involves two genetic algorithm models. The first GA model searches for a solution in the given environment, and the second GA model searches for useful genetic information in the first GA model. In the next step, by using the location of eyes in image the parameters of face's bounding ellipse (center, orientation, major and minor axis) are computed. To evaluate and compare the proposed method with other methods, high order Pseudo Zernike Moments (PZM) are utilized to produce feature vectors and a Radial Basis Function (RBF) neural network is used as the classifier. Simulation results indicate that the speed and accuracy of the new system using the proposed face localization method which uses a co-evolutionary approach is higher than the system proposed in [10].

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Section: B Eyes Localizationmentioning

confidence: 99%

Face Localization Using an Effective Co-evolutionary Genetic Algorithm

Hajati

Lucas

Gao

2010

2010 International Conference on Digital Image Computing: Techniques and Applications

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“…In [29], [28] Handa et al. formulate a coevolutionary algorithm where the host population is parasited on by a population of schemata.…”

Section: Coevolutionary Approach With Heuristics (Coe-h Ga)mentioning

confidence: 99%

Comparing evolutionary algorithms on binary constraint satisfaction problems

Craenen

Eiben

Hemert

2003

IEEE Trans. Evol. Computat.

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Abstract-Constraint handling is not straightforward in evolutionary algorithms (ea) since the usual search operators, mutation and recombination, are 'blind' to constraints. Nevertheless, the issue is highly relevant, for many challenging problems involve constraints. Over the last decade numerous eas for solving constraint satisfaction problems (csp) have been introduced and studied on various problems. The diversity of approaches and the variety of problems used to study the resulting algorithms prevents a fair and accurate comparison of these algorithms. This paper aligns related work by presenting a concise overview and an extensive performance comparison of all these eas on a systematically generated test suite of random binary csps. The random problem instance generator is based on a theoretical model that fixes deficiencies of models and respective generators that have been formerly used in the Evolutionary Computing (ec) field.

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“…Reference [41] applied an evolution strategy to coevolve two populations, one of decision variables and one of Lagrange multipliers to solve efficiently generic nonlinear constrained problems, formulated as a zero-sum minimax problem. Reference [42] used coevolution of one host and one parasitic population to enhance the search for useful schemata during evolution. The host GA searched for good solutions to the problem at hand, while the parasitic one explored the solution space for schemata that improved the search within the host.…”

Section: Previous Coevolutionary Paradigmsmentioning

confidence: 99%

A collective-based adaptive symbiotic model for surface reconstruction in area-based stereo

Goulermas¹,

Liatsis

2003

IEEE Trans. Evol. Computat.

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This paper proposes a novel optimization algorithm for image-space matching and three-dimensional space analysis, using an adapted scheme of evolutionary computation that employs the concept of symbiosis in a collective of homogeneous populations. It is applied to the automatic generation of disparity surfaces used for depth estimation in stereo vision. The global task of approximating the complete disparity surface is decomposed to a large number of smaller local problems, each solvable by a smaller processing unit. Coevolution is sustained in such a way as to counteract the arbitrary decomposition of the original super-problem, so that the local evolutions of all the subproblems become interlocked. This, in the long run, provides a consistent global solution, and it does so via an asynchronous and massively parallel architecture. The entire surface is partitioned to a set of adjoining patches represented by distinct species or populations, with phenotypes corresponding to different polynomial functionals. The credit assignment functions take into account both self and symbiotic terms in an adaptive and dynamic manner, in order to produce disparity patches that are fit within their own domain and at the same time fit in association with their symbionts. This persistent propagation of local interactions to a global scale throughout evolution generates a unified disparity surface composed of the many smaller patch surfaces.

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Genetic algorithm involving coevolution mechanism to search for effective genetic information

Cited by 23 publications

References 4 publications

Face Localization Using an Effective Co-evolutionary Genetic Algorithm

Face Localization Using an Effective Co-evolutionary Genetic Algorithm

Comparing evolutionary algorithms on binary constraint satisfaction problems

A collective-based adaptive symbiotic model for surface reconstruction in area-based stereo

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