Salomon, R. and Eggenberger, P. (1997). Adaptation on the evolutionary time scale: A working hypothesis and basic experiments. In Hao, J.-K. et al., editors, Proc. of the Third Conference on Artificial Evolution --- EA'97, pages 251--262. Springer Verlag, Heidelberg.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....Back [4] compares di#erent variants of mutative self adaptation and presents evidence that seems to indicate that the lognormal self adaptation used in evolution strategies performs better than the variant of self adaptation commonly used in evolutionary programming. Salomon and Eggenberger [13] compare the performance of evolution strategies with that of a breeder genetic algorithm on the sphere, an ellipsoid, and Rastrigin s function, where the coordinates are shifted by a constant increment in every time step. The search space dimensionalities they consider for the sphere are N = 10 ....

....as a model for fitness landscapes at a stage where the population of candidate solutions is in relatively close proximity to the target and is most often studied in the limit of very high search space dimensionality. So as to study the tracking behavior of evolutionary algorithms, several authors ([1, 4, 13]) have added a dynamic component to the sphere model by stipulating that the target x varies with time. Several modes of motion of the target are conceivable and have been explored empirically. Examples include random motion, linear motion, and circular motion in search space. For the present ....

R. Salomon and P. Eggenberger. Adaptation on the evolutionary time scale: A working hypothesis and basic experiments. In Proc. of the Third Conference on Artificial Evolution, pages 251--262. Springer Verlag, Heidelberg, 1997.

....Bck [4] compares different variants of muratire self adaptation and presents evi dence that seems to indicate that the lognormal self adaptation used in evolution strategies performs better than the variant of self adaptation commonly used in evolutionary programming. Salomon and Eggenberger [12] compare the perfor mance of evolution strategies with that of a breeder genetic algorithm on the sphere, an ellipsoid, and Rastrigin s function, where the coordinates are shiRed by a constant increment in every time step. The search space dimensionalities they consider for the sphere are N 10 ....

....a model for fitness landscapes at a stage where the population of candidate solutions is in relatively close proximity to the target and is most often studied in the limit of very high search space dimensionality. So as to study the track ing behavior of evolutionary algorithms, several authors ([1,4, 12]) have added a dynamic component to the sphere model by stipulating that the target : varies with time. Several modes of motion of the target are conceivable and have been explored empirically. Examples include random motion, linear motion, and circular motion in search space. For the present ....

R. Salomon and P. Eggenberger. Adaptation on the evolutionary time scale: A working hypothesis and basic experiments. In Proc, of the Third Conference on Artificial Evolution, pages 251 262. Springer Verlag, Heidelberg, 1997.

....for detecting malignant regions in colonoscopy images though a formulation of the problem that is based on the idea of tracking the moving optimum of a dynamically changing pattern based error measure. This approach coincides with the way adaptation on the evolutionary time scale is considered [10], and allows us to explore and expand further research on the tracking performance of evolution strategies and genetic algorithms [10] 11] 12] However, the reader should keep in mind that in this paper we do not seek global minimisers of the error function, but we are interested in developing ....

....moving optimum of a dynamically changing pattern based error measure. This approach coincides with the way adaptation on the evolutionary time scale is considered [10] and allows us to explore and expand further research on the tracking performance of evolution strategies and genetic algorithms [10], 11] 12] However, the reader should keep in mind that in this paper we do not seek global minimisers of the error function, but we are interested in developing an on line evolution strategy that will converge to an approximation of the optimum solution (the interesting topic of finding global ....

[Article contains additional citation context not shown here]

Salomon, R.; Eggenberger, P., 1998, "Adaptation on the evolutionary time scale: a working hypothesis and basic experiments", 3rd European Conference on Artificial Evolution (AE'97), Nimes, France, Lecture Notes in Computer Science vol. 1363, Springer.

....training and retraining of ANNs, we adopt a formulation of this problem which is based on tracking the changing location of the minimum of a pattern based, and, thus, dynamically changing, error function. This approach coincides with the way adaptation in the evolutionary time scale is considered [20], and allows us to explore and expand further research on the tracking performance of evolution strategies and genetic algorithms [1, 20, 26] 3 The Hybrid Evolutionary Algorithm In this section, we present a Lamarck inspired combination of Di#erential Evolution strategy and Stochastic Gradient ....

....of a pattern based, and, thus, dynamically changing, error function. This approach coincides with the way adaptation in the evolutionary time scale is considered [20] and allows us to explore and expand further research on the tracking performance of evolution strategies and genetic algorithms [1, 20, 26]. 3 The Hybrid Evolutionary Algorithm In this section, we present a Lamarck inspired combination of Di#erential Evolution strategy and Stochastic Gradient Descent (SGD) The DE strategy works on the termination point of the SGD. Thus, the method consists of a SGD based on line training stage ....

R. Salomon and P. Eggenberger, "Adaptation on the evolutionary time scale: a working hypothesis and basic experiments". In:Proc. of the Third European Conference on Artificial Evolution (AE'97), Nimes, France, Lecture Notes in Computer Science vol. 1363, Springer, (1998).

....Function associates at time t such a fitness to each individual. The Time Dependent Optimization Problem (Tdo) which consists in tracking the successive optima of such a non stationary function, is known to be difficultly handled by classical GAs. As early underlined by Salomon and Eggenberger ([23]) Tdo is at the crossing road between research on evolutionary optimization and adaptive behaviors. In order to optimize efficiently such environments, GAs must abandon the convergent dynamic and maintain their ability to react to unpredictable environmental changes. Tdo can therefore be ....

R. Salomon and P. Eggenberger. Adaptation on the evolutionary time scale: A working hypothesis and basic experiments. In Evolution Artificielle, pages 297--308, 1998.

No context found.

Salomon, R. and Eggenberger, P. (1997). Adaptation on the evolutionary time scale: A working hypothesis and basic experiments. In Hao, J.-K. et al., editors, Proc. of the Third Conference on Artificial Evolution --- EA'97, pages 251--262. Springer Verlag, Heidelberg.

No context found.

R. Salomon and P. Eggenberger. Adaptation on the evolutionary time scale: A working hypothesis and basic experiments. In Evolution Artificielle, pages 297--308, 1998.

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