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[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]## Re: [tlug] genetic algorithm/optimization framework

- Date: Fri, 29 Apr 2016 11:48:38 +0900
From:"Stephen J. Turnbull" <stephen@example.com>Subject:Re: [tlug] genetic algorithm/optimization framework- References: <20160427222225.fcdb36d543326580f701d5a5@kinali.ch> <20160427133932.223654a7@sakura> <20160428094937.ec87240ac79f98bd60d86867@kinali.ch> <20160428092551.2c8ad2ee@sakura> <22306.15852.463024.385458@turnbull.sk.tsukuba.ac.jp> <20160428100348.7d38e17e@sakura>

Lars Kotthoff writes: > Right. The problem with applications usually is that it's not clear how the > different objectives should be weighted relative to each other :) That almost goes without saying. If you know the weights (or have a more general functional aggregation of the objectives), you're back in the single-objective world (mathematically -- in practical decision- making, having the vector of sub-objective values can be extremely valuable). One thing I forgot to say (which the multiobjective packages do as a matter of course) is to evaluate all of the objectives at each solution. That gives you a way to measure the cost (loss of value of alternative objectives) of optimizing each objective while ignoring the others. This is especially important if some of the objectives are "satisficing" (some level is "good enough" but you'll take improvements if they're available). It does occasionally happen that no matter what you do the "good enough" level is achieved. If not, it may be a better approach to turn such objective into a constraint at the "good enough" level. > Depending on the application, it may also make sense to have a > portfolio of different parameter settings instead of a single one, You may also be able to kick the problem upstairs to your boss if you have a bunch of solutions with different advantages. Of course if your boss is worth working for, she already knew that and that's why you got delegated the problem in the first place! :-D > i.e. in one case you really care about bandwidth and want to use > the optimal configuration for that and in another case you care > more about something else. This would certainly make the > optimisation easier (and there are good methods for working with > such portfolios). > > Of course it's also possible (albeit unlikely) that, when > optimising for each objective in turn, the same parameter settings > (or at least very close settings) come out at the top. Isn't that the "everybody is welcome to my opinion" solution? :-)

References:

[tlug] genetic algorithm/optimization framework

From:Attila KinaliRe: [tlug] genetic algorithm/optimization framework

From:Lars KotthoffRe: [tlug] genetic algorithm/optimization framework

From:Attila KinaliRe: [tlug] genetic algorithm/optimization framework

From:Lars KotthoffRe: [tlug] genetic algorithm/optimization framework

From:Stephen J. TurnbullRe: [tlug] genetic algorithm/optimization framework

From:Lars Kotthoff

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