mgo
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openmole
Purely functional genetic algorithms for multi-objective optimisation
With the integration of Approximate Bayesian Computation in mgo, we have a foot in the door of bayesian approaches ; it would be interesting to discuss if we could integrate...
Integrate the experimental implementation done [here](https://github.com/openmole/mgo-benchmark)
Exploring a highly stochastic model with PSE lead some of us to a questioning on assumptions for a "reasonable" use of PSE regarding stochastic variability of indicators: from what I...
We have internal metrics in mgo to measure crowding, diversity (the hitmap) in pse ; but we do not have generic measures of open-endedness, which could be useful to test...
I came across this several time: when running a noisy nsga2, some solutions have too few replications ; but most of the time a filtering of the final population on...
HI, that's my first experience to use multi objectives algorithm with your code. My fitness function as below: ``` object test_food { var p:Vector[Vector[Double]] = Vector.empty[Vector[Double]] var size = 0...
It's not really clear in MGO, there is no declared step for fitness computation. The step is embeded in elitism() function call during evolution, perhaps there is a better way...
It seems the non dominated elitism used by NSGA2 implementation in MGO is not correct. Actually the different front are builded using information based on the number of domination, this...
It seems the construction of SMS-EMOA is not correct in MGO. Simple description of the algorithm say that : > The indicator-based EMOA use a quality indicator for the selection...