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Your search keyword '"Kaufmann, Paul"' showing total 7 results
Start Over Author "Kaufmann, Paul" Topic genetic programming
7 results on '"Kaufmann, Paul"'

1. Evolution of application-specific cache mappings.

Author

Ho, Nam, Kaufmann, Paul, and Platzner, Marco

Subjects
*MULTICORE processors, *CACHE memory, *ENERGY consumption, *GENETIC programming
Abstract

Reconfigurable caches offer an intriguing opportunity to tailor cache behavior to applications for better run-times and energy consumptions. While one may adapt structural cache parameters such as cache and block sizes, we adapt the memory-address-to-cache-index mapping function to the needs of an application. Using a LEON3 embedded multi-core processor with reconfigurable cache mappings, a metaheuristic search procedure, and MiBench applications, we show in this work how to accurately compare non-deterministic performances of applications and how to use this information to implement an optimization procedure that evolves application-specific cache mappings for the LEON3 multi-core processor. [ABSTRACT FROM AUTHOR]

Published
2020
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2. Evolving Trust Formula to Evaluate Data Trustworthiness in VANETs Using Genetic Programming

Author

Aslan, Mehmet, Sen, Sevil, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Kaufmann, Paul, editor, and Castillo, Pedro A., editor

Published
2019
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3. Genetic Programming for Feature Selection and Feature Combination in Salient Object Detection

Author

Afzali, Shima, Al-Sahaf, Harith, Xue, Bing, Hollitt, Christopher, Zhang, Mengjie, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Kaufmann, Paul, editor, and Castillo, Pedro A., editor

Published
2019
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4. Supporting Medical Decisions for Treating Rare Diseases Through Genetic Programming

Author

Bakurov, Illya, Castelli, Mauro, Vanneschi, Leonardo, Freitas, Maria João, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Kaufmann, Paul, editor, and Castillo, Pedro A., editor

Published
2019
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5. An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming

Author

Bi, Ying, Xue, Bing, Zhang, Mengjie, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Sim, Kevin, editor, and Kaufmann, Paul, editor

Published
2018
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6. Investigating the Evolvability of Web Page Load Time

Author

Cody-Kenny, Brendan, Manganiello, Umberto, Farrelly, John, Ronayne, Adrian, Considine, Eoghan, McGuire, Thomas, O’Neill, Michael, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Sim, Kevin, editor, and Kaufmann, Paul, editor

Published
2018
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7. Reconsideration and extension of Cartesian genetic programming

Author

Kalkreuth, Roman Tobias, Rudolph, Günter, and Kaufmann, Paul

Subjects
Evolution��re Programmierung, Genetische Programmierung, Evolutionary computation, Genetic programming, Cartesian genetic programming
Abstract

This dissertation aims on analyzing fundamental concepts and dogmas of a graph-based genetic programming approach called Cartesian Genetic Programming (CGP) and introduces advanced genetic operators for CGP. The results of the experiments presented in this thesis lead to more knowledge about the algorithmic use of CGP and its underlying working mechanisms. CGP has been mostly used with a parametrization pattern, which has been prematurely generalized as the most efficient pattern for standard CGP and its variants. Several parametrization patterns are evaluated with more detailed and comprehensive experiments by using meta-optimization. This thesis also presents a first runtime analysis of CGP. The time complexity of a simple (1+1)-CGP algorithm is analyzed with a simple mathematical problem and a simple Boolean function problem. In the subfield of genetic operators for CGP, new recombination and mutation techniques that work on a phenotypic level are presented. The effectiveness of these operators is demonstrated on a widespread set of popular benchmark problems. Especially the role of recombination can be seen as a big open question in the field of CGP, since the lack of an effective recombination operator limits CGP to mutation-only use. Phenotypic exploration analysis is used to analyze the effects caused by the presented operators. This type of analysis also leads to new insights into the search behavior of CGP in continuous and discrete fitness spaces. Overall, the outcome of this thesis leads to a reconsideration of how CGP is effectively used and extends its adaption from Darwin's and Lamarck's theories of biological evolution.

Published
2021

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