---
_id: '42839'
author:
- first_name: Florian
  full_name: Mehlich, Florian
  last_name: Mehlich
citation:
  ama: Mehlich F. <i>An Evaluation of XCS on the OpenAI Gym</i>. Paderborn University;
    2023.
  apa: Mehlich, F. (2023). <i>An Evaluation of XCS on the OpenAI Gym</i>. Paderborn
    University.
  bibtex: '@book{Mehlich_2023, place={Paderborn}, title={An Evaluation of XCS on the
    OpenAI Gym}, publisher={Paderborn University}, author={Mehlich, Florian}, year={2023}
    }'
  chicago: 'Mehlich, Florian. <i>An Evaluation of XCS on the OpenAI Gym</i>. Paderborn:
    Paderborn University, 2023.'
  ieee: 'F. Mehlich, <i>An Evaluation of XCS on the OpenAI Gym</i>. Paderborn: Paderborn
    University, 2023.'
  mla: Mehlich, Florian. <i>An Evaluation of XCS on the OpenAI Gym</i>. Paderborn
    University, 2023.
  short: F. Mehlich, An Evaluation of XCS on the OpenAI Gym, Paderborn University,
    Paderborn, 2023.
date_created: 2023-03-07T12:22:57Z
date_updated: 2024-05-15T13:14:54Z
department:
- _id: '78'
extern: '1'
language:
- iso: eng
place: Paderborn
project:
- _id: '14'
  grant_number: '160364472'
  name: 'SFB 901 - C2: SFB 901 - Subproject C2'
- _id: '4'
  name: 'SFB 901 - C: SFB 901 - Project Area C'
- _id: '1'
  grant_number: '160364472'
  name: 'SFB 901: SFB 901'
publisher: Paderborn University
status: public
supervisor:
- first_name: Marco
  full_name: Platzner, Marco
  id: '398'
  last_name: Platzner
- first_name: Tim
  full_name: Hansmeier, Tim
  id: '49992'
  last_name: Hansmeier
  orcid: 0000-0003-1377-3339
title: An Evaluation of XCS on the OpenAI Gym
type: bachelorsthesis
user_id: '398'
year: '2023'
...
---
_id: '29151'
abstract:
- lang: eng
  text: Automation becomes a vital part in the High-Performance computing system in
    situational dynamics to take the decisions on the fly. Heterogeneous compute nodes
    consist of computing resources such as CPU, GPU and FPGA and are the important
    components of the high-performance computing system that can adapt the automation
    to achieve the given goal. While implanting automation in the computing resources,
    management of the resources is one of the essential aspects that need to be taken
    care of. Tasks are continuously executed on the resources using its unique characteristics.
    Effective scheduling is essential to make the best use of the characteristics
    provided by each resource. Scheduling enables the execution of each task by allocating
    resources so that they take advantage of all the characteristics of the compute
    resources. Various scheduling heuristics can be used to create effective scheduling,
    which might require the execution time to schedule the task efficiently. Providing
    actual execution time is not possible in many cases; hence we can provide the
    estimations for the actual execution time . The purpose of this master's thesis
    is to design a predictive model or system that estimates the execution time required
    to execute tasks using historical execution time data on the heterogeneous compute
    nodes. In this thesis, regression techniques(SGD Regressor, Passive-Aggressive
    Regressor, MLP Regressor, and XCSF Regressor) are compared in terms of their prediction
    accuracy in order to determine which technique produces reliable predictions for
    the execution time. These estimations must be generated in an online learning
    environment in which data points arrive in any sequence, one by one, and the regression
    model must learn from them. After evaluating the regression algorithms, it is
    seen that the XCSF regressor provides the highest overall prediction accuracy
    for the supplied data sets. The regression technique's parameters also play a
    significant role in achieving an acceptable prediction accuracy. As a remark,
    when using online learning in regression analysis, the accuracy depends upon both
    the order of sequential data points that are coming to train the model and the
    parameter configuration for each regression technique.
author:
- first_name: Chinmay
  full_name: Kashikar, Chinmay
  last_name: Kashikar
citation:
  ama: Kashikar C. <i>A Comparison of Machine Learning Techniques for the On-Line
    Characterization of Tasks Executed on Heterogeneous Compute Nodes</i>. Paderborn
    University; 2021.
  apa: Kashikar, C. (2021). <i>A Comparison of Machine Learning Techniques for the
    On-line Characterization of Tasks Executed on Heterogeneous Compute Nodes</i>.
    Paderborn University.
  bibtex: '@book{Kashikar_2021, place={Paderborn}, title={A Comparison of Machine
    Learning Techniques for the On-line Characterization of Tasks Executed on Heterogeneous
    Compute Nodes}, publisher={Paderborn University}, author={Kashikar, Chinmay},
    year={2021} }'
  chicago: 'Kashikar, Chinmay. <i>A Comparison of Machine Learning Techniques for
    the On-Line Characterization of Tasks Executed on Heterogeneous Compute Nodes</i>.
    Paderborn: Paderborn University, 2021.'
  ieee: 'C. Kashikar, <i>A Comparison of Machine Learning Techniques for the On-line
    Characterization of Tasks Executed on Heterogeneous Compute Nodes</i>. Paderborn:
    Paderborn University, 2021.'
  mla: Kashikar, Chinmay. <i>A Comparison of Machine Learning Techniques for the On-Line
    Characterization of Tasks Executed on Heterogeneous Compute Nodes</i>. Paderborn
    University, 2021.
  short: C. Kashikar, A Comparison of Machine Learning Techniques for the On-Line
    Characterization of Tasks Executed on Heterogeneous Compute Nodes, Paderborn University,
    Paderborn, 2021.
date_created: 2022-01-04T09:24:52Z
date_updated: 2022-01-06T06:58:46Z
department:
- _id: '78'
language:
- iso: eng
place: Paderborn
project:
- _id: '14'
  name: SFB 901 - Subproject C2
- _id: '4'
  name: SFB 901 - Project Area C
- _id: '1'
  name: SFB 901
publisher: Paderborn University
status: public
supervisor:
- first_name: Marco
  full_name: Platzner, Marco
  id: '398'
  last_name: Platzner
- first_name: Tim
  full_name: Hansmeier, Tim
  id: '49992'
  last_name: Hansmeier
  orcid: 0000-0003-1377-3339
title: A Comparison of Machine Learning Techniques for the On-line Characterization
  of Tasks Executed on Heterogeneous Compute Nodes
type: mastersthesis
user_id: '49992'
year: '2021'
...
---
_id: '22483'
abstract:
- lang: eng
  text: This bachelor thesis presents a C/C++ implementation of the XCS algorithm
    for an embedded system and profiling results concerning the execution time of
    the functions. These are then analyzed in relation to the input characteristics
    of the examined learning environments and compared with related work. Three main
    conclusions can be drawn from the measured results. First, the maximum size of
    the population of the classifiers influences the runtime of the genetic algorithm;
    second, the size of the input space has a direct effect on the execution time
    of the matching function; and last, a larger action space results in a longer
    runtime generating the prediction for the possible actions. The dependencies identified
    here can serve to optimize the computational efficiency and make XCS more suitable
    for embedded systems.
author:
- first_name: Mathis
  full_name: Brede, Mathis
  last_name: Brede
citation:
  ama: 'Brede M. <i>Implementation and Profiling of XCS in the Context of Embedded
    Systems</i>. Paderborn: Paderborn University; 2021.'
  apa: 'Brede, M. (2021). <i>Implementation and Profiling of XCS in the Context of
    Embedded Systems</i>. Paderborn: Paderborn University.'
  bibtex: '@book{Brede_2021, place={Paderborn}, title={Implementation and Profiling
    of XCS in the Context of Embedded Systems}, publisher={Paderborn University},
    author={Brede, Mathis}, year={2021} }'
  chicago: 'Brede, Mathis. <i>Implementation and Profiling of XCS in the Context of
    Embedded Systems</i>. Paderborn: Paderborn University, 2021.'
  ieee: 'M. Brede, <i>Implementation and Profiling of XCS in the Context of Embedded
    Systems</i>. Paderborn: Paderborn University, 2021.'
  mla: Brede, Mathis. <i>Implementation and Profiling of XCS in the Context of Embedded
    Systems</i>. Paderborn University, 2021.
  short: M. Brede, Implementation and Profiling of XCS in the Context of Embedded
    Systems, Paderborn University, Paderborn, 2021.
date_created: 2021-06-21T09:35:03Z
date_updated: 2022-01-06T06:55:33Z
department:
- _id: '78'
extern: '1'
language:
- iso: eng
place: Paderborn
project:
- _id: '14'
  name: SFB 901 - Subproject C2
- _id: '4'
  name: SFB 901 - Project Area C
- _id: '1'
  name: SFB 901
publisher: Paderborn University
status: public
supervisor:
- first_name: Marco
  full_name: Platzner, Marco
  id: '398'
  last_name: Platzner
- first_name: Tim
  full_name: Hansmeier, Tim
  id: '49992'
  last_name: Hansmeier
  orcid: 0000-0003-1377-3339
title: Implementation and Profiling of XCS in the Context of Embedded Systems
type: bachelorsthesis
user_id: '477'
year: '2021'
...