Unsga3

U-NSGA-III (Unified NSGA-III) for .NET — single-, multi-, and many-objective evolutionary optimization with Das–Dennis reference directions, SBX crossover, polynomial mutation, and niching-based tournament selection (Seada & Deb, 2016).

v0.1.2 — production-usable core with pymoo-aligned normalization.
15-seed IGD vs pymoo UNSGA3: ZDT1 median 0.053 vs 0.070 (we win; MWU p≈0.05); DTLZ2 median 0.0045 vs 0.0028 (~1.6×, same order; pymoo still ahead).
Details: docs/WILCOXON-RESULTS.md · single-seed notes: docs/ORACLE-RESULTS.md

https://github.com/AppSprout-dev/Unsga3

Why this exists

Most strong MOEA reference stacks are Python/MATLAB/Java. Unsga3 brings a careful U-NSGA-III port to idiomatic C# / .NET — deterministic seeds, no native runtime deps, NuGet-friendly — validated against pymoo on standard ZDT/DTLZ IGD protocols (not just “it runs”).

Install

GitHub Packages (current):

dotnet nuget add source https://nuget.pkg.github.com/AppSprout-dev/index.json \
  --name github-appsprout --username YOUR_GH_USER --password YOUR_PAT --store-password-in-clear-text

dotnet add package Unsga3

PAT needs read:packages. Releases publish on v* tags.

nuget.org — planned (see roadmap).

Quick start

using Unsga3.Algorithm;
using Unsga3.Problems;
using Unsga3.Utilities;

var problem = new Zdt1Problem();
var dirs = ReferenceDirections.DasDennis(numberOfObjectives: 2, partitions: 12);
var algo = new Unsga3Algorithm(dirs, populationSize: 40, seed: 42);
var result = algo.Run(problem, maxGenerations: 100);

foreach (var ind in result.NonDominatedSolutions)
    Console.WriteLine($"{ind.Objectives[0]:F4}  {ind.Objectives[1]:F4}");

Many-objective:

var algo = Unsga3Algorithm.WithDasDennis(numberOfObjectives: 3, partitions: 12, seed: 1);
var result = algo.Run(new Dtlz2Problem(nObjectives: 3), maxGenerations: 150);

Fairer mating comparison vs pymoo:

using Unsga3.Operators.Selection;

var algo = new Unsga3Algorithm(dirs, populationSize: 92, seed: 1,
    tournamentMode: TournamentMode.PymooCompatible);

Public surface

Type	Role
IProblem / ProblemBase	Problem definition (minimize; g≤0 constraints)
Unsga3Algorithm	Main entry — Run(problem, gens)
Individual	Variables / Objectives / Constraints
OptimizationResult	Final population + non-dominated set
ReferenceDirections.DasDennis	Structured reference points
SimulatedBinaryCrossover / PolynomialMutation	Variation operators
PerformanceIndicators	IGD, GD, 2-D hypervolume
TournamentMode	Default rank→niche vs PymooCompatible

Built-in problems: ZDT1–4/6, DTLZ1–4/7, Sphere, Ackley, Rosenbrock.

Build & test

dotnet build Unsga3.slnx -c Release
dotnet test Unsga3.slnx -c Release
dotnet run --project samples/BasicUsage -c Release

Requires .NET 10 SDK. Optional oracle: Python 3 + pip install pymoo (see CONTRIBUTING.md).

Equivalence & research

Doc	Contents
docs/ORACLE-RESULTS.md	Single-seed C# vs pymoo
docs/WILCOXON-RESULTS.md	Multi-seed Mann–Whitney / Wilcoxon
docs/EQUIVALENCE.md	Protocol & intentional deltas
docs/RESEARCH-STANDARDS.md	Literature + indicator standards
docs/NOTICE.md	Attribution (papers + validation tools)
docs/ROADMAP.md	Near / medium term plan

Layout

Unsga3/
├── src/Unsga3/              # Library (no third-party runtime deps)
├── tests/Unsga3.Tests/
├── samples/BasicUsage/
├── tools/oracle/            # pymoo oracle + multi-seed stats (optional)
├── tools/OracleCompare/     # C# side of the oracle
├── docs/
└── .github/workflows/       # CI + GitHub Packages publish

Contributing

See CONTRIBUTING.md, CODE_OF_CONDUCT.md, and SECURITY.md. Issues and PRs welcome — especially real multi-objective use cases and oracle gaps.

Cite

Software: use CITATION.cff (GitHub “Cite this repository”).

Algorithm papers (please cite these when publishing results):

• Seada, H. & Deb, K. (2016). A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives. IEEE Trans. Evol. Comput.
• Deb, K. & Jain, H. (2014). An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach (NSGA-III), Part I. IEEE Trans. Evol. Comput.
• Das, I. & Dennis, J. E. (1998). Normal-Boundary Intersection. SIAM J. Optim.

License

MIT — see LICENSE.

Not affiliated with pymoo. Validation compares against pymoo as an external oracle; no pymoo code is shipped in the NuGet package (NOTICE).