NSGA2 minimizaton fits only the first fitness value in the multiobjective optimization problem

[rat-h]

• inspyred version: 1.0.1 (latest)
• Python version: 3.7.3 (default, Oct 7 2019, 12:56:13)
• Operating System: Ubuntu based, Pop-OS!

Description

I'm trying to follow this paper Optimizing computer models of corticospinal neurons to replicate in vitro dynamics and employ NSGA2 for parameter fitting of neurons.

I found that NSGA2 optimizes only the first fitness value in the multiobjective optimization problem.

I think, there is a problem in inspyred/ec/replacers, function: nsga_replacement line 350:

combined[p] < combined[q]

which leads to inspyred/ec/ec class Individual, operator __lt__, where two fitness lists are comparing (lines 335 and 337)

return self.fitness < other.fitness

A simple test shows that python compares only the first element of the list and, seems, ignores others:

>>> [11.1 , 0. , 0.]<[ 11.0 , 3393.63 , 3411.87 ] 
False

For non-dominant minimization, fitness function on the left of < is the dominant solution, but compassing makes it non-dominant.

[aarongarrett]

If you use a multiobjective method, you have to wrap your fitness in a Pareto object in the evaluator, as described in the documentation. The Pareto class overloads less-than to provide a dominance operator.