[jira] [Updated] (MATH-1563) Implementation of Adaptive Probability Generation Strategy for Genetic Algorithm
[ https://issues.apache.org/jira/browse/MATH-1563?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] AVIJIT BASAK updated MATH-1563: --- Attachment: chromosome hierarchy.png > Implementation of Adaptive Probability Generation Strategy for Genetic > Algorithm > > > Key: MATH-1563 > URL: https://issues.apache.org/jira/browse/MATH-1563 > Project: Commons Math > Issue Type: Improvement >Reporter: AVIJIT BASAK >Priority: Major > Attachments: GA-Model.uxf, GA-Model.uxf, GA-OperatorModel.uxf, > GA-OperatorModel.uxf, GA-Overview.uxf, GA-Overview.uxf, chromosome > hierarchy.png > > > In Genetic Algorithm probability of crossover and mutation operation can be > generated in an adaptive manner. Some experiment was done related to this and > published in this article > "https://www.ijcaonline.org/archives/volume175/number10/basak-2020-ijca-920572.pdf;. > Currently Apache's API works on constant probability strategy. I would like > to propose incorporation of rank based adaptive probability generation > strategy as described in the mentioned article. This will improve the > performance and robustness of the algorithm and would make this more suitable > for use in higher dimensional problems like machine learning or deep learning. -- This message was sent by Atlassian Jira (v8.20.1#820001)
[jira] [Updated] (MATH-1563) Implementation of Adaptive Probability Generation Strategy for Genetic Algorithm
[ https://issues.apache.org/jira/browse/MATH-1563?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] AVIJIT BASAK updated MATH-1563: --- Attachment: GA-OperatorModel.uxf > Implementation of Adaptive Probability Generation Strategy for Genetic > Algorithm > > > Key: MATH-1563 > URL: https://issues.apache.org/jira/browse/MATH-1563 > Project: Commons Math > Issue Type: Improvement >Reporter: AVIJIT BASAK >Priority: Major > Attachments: GA-Model.uxf, GA-Model.uxf, GA-OperatorModel.uxf, > GA-OperatorModel.uxf, GA-Overview.uxf, GA-Overview.uxf > > > In Genetic Algorithm probability of crossover and mutation operation can be > generated in an adaptive manner. Some experiment was done related to this and > published in this article > "https://www.ijcaonline.org/archives/volume175/number10/basak-2020-ijca-920572.pdf;. > Currently Apache's API works on constant probability strategy. I would like > to propose incorporation of rank based adaptive probability generation > strategy as described in the mentioned article. This will improve the > performance and robustness of the algorithm and would make this more suitable > for use in higher dimensional problems like machine learning or deep learning. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Updated] (MATH-1563) Implementation of Adaptive Probability Generation Strategy for Genetic Algorithm
[ https://issues.apache.org/jira/browse/MATH-1563?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] AVIJIT BASAK updated MATH-1563: --- Attachment: GA-Model.uxf > Implementation of Adaptive Probability Generation Strategy for Genetic > Algorithm > > > Key: MATH-1563 > URL: https://issues.apache.org/jira/browse/MATH-1563 > Project: Commons Math > Issue Type: Improvement >Reporter: AVIJIT BASAK >Priority: Major > Attachments: GA-Model.uxf, GA-Model.uxf, GA-OperatorModel.uxf, > GA-Overview.uxf, GA-Overview.uxf > > > In Genetic Algorithm probability of crossover and mutation operation can be > generated in an adaptive manner. Some experiment was done related to this and > published in this article > "https://www.ijcaonline.org/archives/volume175/number10/basak-2020-ijca-920572.pdf;. > Currently Apache's API works on constant probability strategy. I would like > to propose incorporation of rank based adaptive probability generation > strategy as described in the mentioned article. This will improve the > performance and robustness of the algorithm and would make this more suitable > for use in higher dimensional problems like machine learning or deep learning. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Updated] (MATH-1563) Implementation of Adaptive Probability Generation Strategy for Genetic Algorithm
[ https://issues.apache.org/jira/browse/MATH-1563?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] AVIJIT BASAK updated MATH-1563: --- Attachment: GA-Overview.uxf > Implementation of Adaptive Probability Generation Strategy for Genetic > Algorithm > > > Key: MATH-1563 > URL: https://issues.apache.org/jira/browse/MATH-1563 > Project: Commons Math > Issue Type: Improvement >Reporter: AVIJIT BASAK >Priority: Major > Attachments: GA-Model.uxf, GA-Model.uxf, GA-OperatorModel.uxf, > GA-Overview.uxf, GA-Overview.uxf > > > In Genetic Algorithm probability of crossover and mutation operation can be > generated in an adaptive manner. Some experiment was done related to this and > published in this article > "https://www.ijcaonline.org/archives/volume175/number10/basak-2020-ijca-920572.pdf;. > Currently Apache's API works on constant probability strategy. I would like > to propose incorporation of rank based adaptive probability generation > strategy as described in the mentioned article. This will improve the > performance and robustness of the algorithm and would make this more suitable > for use in higher dimensional problems like machine learning or deep learning. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Updated] (MATH-1563) Implementation of Adaptive Probability Generation Strategy for Genetic Algorithm
[ https://issues.apache.org/jira/browse/MATH-1563?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] AVIJIT BASAK updated MATH-1563: --- Attachment: GA-OperatorModel.uxf > Implementation of Adaptive Probability Generation Strategy for Genetic > Algorithm > > > Key: MATH-1563 > URL: https://issues.apache.org/jira/browse/MATH-1563 > Project: Commons Math > Issue Type: Improvement >Reporter: AVIJIT BASAK >Priority: Major > Attachments: GA-Model.uxf, GA-OperatorModel.uxf, GA-Overview.uxf > > > In Genetic Algorithm probability of crossover and mutation operation can be > generated in an adaptive manner. Some experiment was done related to this and > published in this article > "https://www.ijcaonline.org/archives/volume175/number10/basak-2020-ijca-920572.pdf;. > Currently Apache's API works on constant probability strategy. I would like > to propose incorporation of rank based adaptive probability generation > strategy as described in the mentioned article. This will improve the > performance and robustness of the algorithm and would make this more suitable > for use in higher dimensional problems like machine learning or deep learning. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Updated] (MATH-1563) Implementation of Adaptive Probability Generation Strategy for Genetic Algorithm
[ https://issues.apache.org/jira/browse/MATH-1563?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] AVIJIT BASAK updated MATH-1563: --- Attachment: GA-Model.uxf > Implementation of Adaptive Probability Generation Strategy for Genetic > Algorithm > > > Key: MATH-1563 > URL: https://issues.apache.org/jira/browse/MATH-1563 > Project: Commons Math > Issue Type: Improvement >Reporter: AVIJIT BASAK >Priority: Major > Attachments: GA-Model.uxf, GA-OperatorModel.uxf, GA-Overview.uxf > > > In Genetic Algorithm probability of crossover and mutation operation can be > generated in an adaptive manner. Some experiment was done related to this and > published in this article > "https://www.ijcaonline.org/archives/volume175/number10/basak-2020-ijca-920572.pdf;. > Currently Apache's API works on constant probability strategy. I would like > to propose incorporation of rank based adaptive probability generation > strategy as described in the mentioned article. This will improve the > performance and robustness of the algorithm and would make this more suitable > for use in higher dimensional problems like machine learning or deep learning. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Updated] (MATH-1563) Implementation of Adaptive Probability Generation Strategy for Genetic Algorithm
[ https://issues.apache.org/jira/browse/MATH-1563?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] AVIJIT BASAK updated MATH-1563: --- Attachment: GA-Overview.uxf > Implementation of Adaptive Probability Generation Strategy for Genetic > Algorithm > > > Key: MATH-1563 > URL: https://issues.apache.org/jira/browse/MATH-1563 > Project: Commons Math > Issue Type: Improvement >Reporter: AVIJIT BASAK >Priority: Major > Attachments: GA-Model.uxf, GA-OperatorModel.uxf, GA-Overview.uxf > > > In Genetic Algorithm probability of crossover and mutation operation can be > generated in an adaptive manner. Some experiment was done related to this and > published in this article > "https://www.ijcaonline.org/archives/volume175/number10/basak-2020-ijca-920572.pdf;. > Currently Apache's API works on constant probability strategy. I would like > to propose incorporation of rank based adaptive probability generation > strategy as described in the mentioned article. This will improve the > performance and robustness of the algorithm and would make this more suitable > for use in higher dimensional problems like machine learning or deep learning. -- This message was sent by Atlassian Jira (v8.3.4#803005)
