-A≤x≤A.

Values of A from 10 to 10⁵ have been used successfully. Higher values of A increase the difficulty of the problem.

References

[1] Schaffer, J. David (1984). Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. Proceedings of the First Int. Conference on Genetic Algortihms, Ed. G.J.E Grefensette, J.J. Lawrence Erlbraum (PhD). Vanderbilt University. OCLC 20004572 [2] https://en.wikipedia.org/wiki/Testfunctionsforoptimization#Testfunctionsformulti-objective_optimization

References

[1] Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2), 173–195.

References

[1] Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2), 173–195.

References

[1] Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2), 173–195.

index = ParetoSet(X[, sense])

Get the Pareto set from a given set of oberservations X.

Argument

• X::AbstractMatrix{Real} size(X)=(nobjectives, nsamples)
• sense::AbstractVector{Union{Symbol, EGOSense}}=repeat(:Min, n_objectives) sense of each objective, size(S)=(n_objectives,)