User:Vegardli

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= State of the project (16.01.16) =
= State of the project (16.01.16) =
* <tt><++++++++++></tt> Essay: Done
* <tt><++++++++++></tt> Essay: Done
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* <tt><++++++++--></tt> GA Application: Mostly done
+
* <tt><++++++++--></tt> GA Application: Mostly done (~900 lines)
* <tt><++++++++++></tt> CPU Evaluator: Done
* <tt><++++++++++></tt> CPU Evaluator: Done
-
* <tt><----------></tt> GPU Evaluator: Not started
+
* <tt><----------></tt> GPU Evaluator: Not started (~1100 lines)
-
* <tt><+++++++++-></tt> HDL Evaluator: Mostly done
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* <tt><+++++++++-></tt> HDL Evaluator: Mostly done (~2300 lines, many of which are not mine)
* <tt><----------></tt> Thesis: Not started
* <tt><----------></tt> Thesis: Not started
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== GA Application ==
== GA Application ==
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Mostly done. Need to work on methods for diversity.
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Mostly done.
 +
 
 +
Successful demonstration of diversity algorithm under deception:
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 +
[[Fil:vegardli-divergence-success.png]]
== CPU Evaluator ==
== CPU Evaluator ==

Current revision as of 14:49, 3 March 2016

Pathfinding using genetic algorithms and hardware acceleration for trajectory cost calculations.

Contents

State of the project (16.01.16)

  • <++++++++++> Essay: Done
  • <++++++++--> GA Application: Mostly done (~900 lines)
  • <++++++++++> CPU Evaluator: Done
  • <----------> GPU Evaluator: Not started (~1100 lines)
  • <+++++++++-> HDL Evaluator: Mostly done (~2300 lines, many of which are not mine)
  • <----------> Thesis: Not started

Essay

Handed in and awaiting feedback

GA Application

Mostly done.

Successful demonstration of diversity algorithm under deception:

Fil:vegardli-divergence-success.png

CPU Evaluator

Done.

Also comes with visualization. Generation 0, 5 and 10 of a typical run:

Fil:vegardli-visualization-screenshot-2-gen-0.png Fil:vegardli-visualization-screenshot-2-gen-5.png Fil:vegardli-visualization-screenshot-2-gen-10.png

GPU Evaluator

Not started

HDL Evaluator

Mostly done, need more timing info and integration with GA application

Thesis

Not started

Future work plan

January

  • Implement diversity methods in application
  • Establish communications between application and HDL model

February

  • Run experiments to find performance and limits of model
  • Start focusing on writing

March

April

May

  • Hand-in
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