From 5114723804a6c9dd6c4d8a8830696d122be0489d Mon Sep 17 00:00:00 2001 From: nojhan Date: Tue, 10 Nov 2020 14:26:34 +0100 Subject: [PATCH] better wording --- docs/index.html | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/docs/index.html b/docs/index.html index d7abadb37..b067bfa26 100644 --- a/docs/index.html +++ b/docs/index.html @@ -325,7 +325,7 @@

You can expect that validating an algorithm implemented with will be up to 10 times faster than its (heavily optimized) Python counterpart.

To give an order of magnitude:

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An example of binding between , IOHexperimenter and irace. ParadisEO provides a on-the-fly algorithm instanciation, which is ran on an IOH problem, with performances estimated with a generic and fast module. All components figured with lego bricks forms a single, integrated, binary.
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A simplified example of binding between , IOHexperimenter and irace. ParadisEO provides a on-the-fly algorithm instanciation, which is ran on an IOH problem, with performances estimated with a generic and fast module. All components figured with lego bricks forms a single, integrated, binary.
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As the codebase provides generic algorithm and a large set of operators, this can make a huge number of algorithms alternatives (easily millions of them). To evaluate those algorithms, you can use bindings toward fast benchmarking tools (like IOHexperimenter), which allow for insanely fast runs. For instance, you can plug this binary with irace ( provides an automatic interface generation) and reach budgets of 10 000 runs in just one hour (!).

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As the codebase provides generic algorithm and a large set of operators, it's easy to have access to a huge number of algorithms alternatives: there's easily millions of unique combinations.

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To evaluate those algorithms, you can use bindings toward fast benchmarking tools (like IOHexperimenter), which allow for the fastests runs of the market.

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You can then wrap this evaluation within an optimizer, to automatically search for the best algorithm instance. For instance, you can either use an optimizer made with , either plug the binary with irace ( provides an automatic interface generation) and reach budgets of 10 000 runs in just one hour (!).

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And voilà! You started by just designing a high-level view of a solver, and you now know which algorithm instance allow for the best performance.

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Note that the binding with IOHexperimenter also allow to easily import and explore experimental results in the IOHanalyzer HMI. Ohlala.