Dynamic flies: a new pattern recognition tool applied to stereo sequence processing

Louchet, Jean; Guyon, Maud; Lesot, Marie‐Jeanne; Boumaza, Amine

doi:10.1016/s0167-8655(01)00129-5

Cited by 30 publications

(15 citation statements)

References 6 publications

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“…This way of exploiting the artificial evolution scheme is versatile enough to facilitate the integration of constraints and the development of various strategies (archive and embossing points as in section 4, or variable population size as stated in [5] for instance). The experiments described in this chapter join previous studies on "Parisian evolution", that experimentally proved that very efficient algorithms can be built on this cooperation-coevolution basis, in terms of rapidity [41], or in terms of size and complexity of the problems [17,63].…”

Section: Resultsmentioning

confidence: 61%

“…Future work on this algorithm will be aimed at evaluating if a global calculation may accelerate its convergence and robustness. Note however that for instance the fly algorithm [63,41] does not use any global fitness either, but is able to provide extremely rapid results: the cooperation mechanisms may operate in some cases without global fitness. The common characteristics of these two examples is that the cooperative scheme has allowed representing in an indirect way some complex structures (classification rules in the first example and Bayesian Networks in the second one).…”

Section: Resultsmentioning

confidence: 99%

“…Here, IMPEA only computes the global evaluation at the end of the evolution, and thus do not use any feedback mechanism. This approach, which is an extreme case of the Parisian CCEA, has already been used with success for example in real-time evolutionary algorithms, such as the flies algorithm [41].…”

Section: Evolution Of An Independence Modelmentioning

confidence: 99%

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Cooperative Coevolution for Agrifood Process Modeling

Barrière

Lutton

Wuillemin

et al. 2013

EVOLVE- A Bridge Between Probability, Set Oriented Numerics and Evolutionary Computation

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On the contrary to classical schemes of evolutionary optimisations algorithms, single population Cooperative Co-evolution techniques (CCEAs, also called "Parisian" approaches) make it possible to represent the evolved solution as an aggregation of several individuals (or even as a whole population). In other words, each individual represents only a part of the solution. This scheme allows simulating the principles of Darwinian evolution in a more economic way, which results in gain in robustness and efficiency. The counterpart however is a more complex design phase. In this chapter, we detail the design of efficient CCEAs schemes on two applications related to the modeling of an industrial agri-food process. The experiments correspond to complex optimisations encountered in the modeling of a Camembert-cheese ripening process. Two problems are considered:• A deterministic modeling problem, phase prediction, for which a search for a closed form tree expression is performed using genetic programming (GP).• A Bayesian network structure estimation problem. The novelty of the proposed approach is based on the use of a two step process based on an intermediate representation called independence model. The search for an independence model is formulated as a complex optimisation problem, for which the CCEA scheme is particularly well suited. A Bayesian network is finally deduced using a deterministic algorithm, as a representative of the equivalence class figured by the independence model.

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Section: Resultsmentioning

confidence: 61%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Cooperative Coevolution for Agrifood Process Modeling

Barrière

Lutton

Wuillemin

et al. 2013

EVOLVE- A Bridge Between Probability, Set Oriented Numerics and Evolutionary Computation

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“…The general mathematical expression of the fitness function is [7,8]: -∇´M L µ and ∇´M R µ are Sobel gradient norms on left and right projections of the fly. That is intended to penalise flies which project onto uniform regions, i.e.…”

Section: Discussionmentioning

confidence: 99%

“…A sharing operator [7,8] reduces the fitness of flies packed together and forces them to explore other areas of the search space.…”

Section: Selectionmentioning

confidence: 99%

Evolutionary Optimisation for Obstacle Detection and Avoidance in Mobile Robotics

Pauplin¹,

Louchet²,

Lutton³

et al. 2005

J. Adv. Comput. Intell. Intell. Inform.

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This paper presents an artificial evolution-based method for stereo image analysis and its application to real-time obstacle detection and avoidance for a mobile robot. It uses the Parisian approach, which consists here in splitting the representation of the robot's environment into a large number of simple primitives, the "flies", which are evolved according to a biologically inspired scheme. Results obtained on real scene with different fitness functions are presented and discussed, and an exploitation for obstacle avoidance in mobile robotics is proposed.

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Bibliography

2016

Evolutionary Algorithms for Food Science and Technology

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Dynamic flies: a new pattern recognition tool applied to stereo sequence processing

Cited by 30 publications

References 6 publications

Cooperative Coevolution for Agrifood Process Modeling

Cooperative Coevolution for Agrifood Process Modeling

Evolutionary Optimisation for Obstacle Detection and Avoidance in Mobile Robotics

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