Supported Samplers

Tunny can utilize a number of sampling techniques.

The table below summarizes the optimizations supported by each sampler. Note that Tunny's UI will automatically display the available methods depending on the problem.

Name	Single-Objective	Multi-Objective	Constraints	Human-in-the-loop
AUTO Sampler	✓	✓	✓
TPE	✓	✓	✓	✓
cTPE	✓		✓
GP-Optuna	✓	✓	✓
GP-BoTorch	✓	✓	✓
GP-Preferential				✓
HEBO	✓	✓		✓
TuRBO	✓
CARBO	✓		✓
Robust GP	✓
NSGA-II	✓	✓	✓
NSGA-III	✓	✓	✓
MOEA/D		✓
DE	✓
SPEA-II	✓	✓
HypE		✓
CMA-ES	✓
MO-CMA-ES		✓
INGO	✓
PSO	✓
GreyWolf	✓
Whale	✓
Random	✓	✓
QMC	✓	✓
BruteForce	✓	✓

The specific characteristics of each sampling technique are as follows

Name	Type	Note
AUTO Sampler	Auto	Automatically selects the optimal sampler based on problem characteristics (objectives, constraints, variable types). Recommended for beginners or when unsure which sampler to use.
TPE	Bayesian Optimization	Tree-structured Parzen Estimator. One of the most versatile methods alongside NSGA-II. Builds probabilistic models to efficiently explore search spaces. Excellent for expensive function evaluations and high-dimensional problems with mixed parameter types.
cTPE	Bayesian Optimization	Constrained TPE with enhanced constraint handling. Models constraint violations explicitly alongside objectives. Use when standard TPE struggles with constraint satisfaction or when strict constraint requirements exist.
GP-Optuna	Bayesian Optimization	Gaussian Process with Optuna's implementation. Faster than GP-BoTorch while maintaining good optimization quality. Provides uncertainty estimates and works well for smooth, continuous objective functions with moderate dimensionality.
GP-BoTorch	Bayesian Optimization	Gaussian Process using Facebook's BoTorch library. Highly flexible with advanced acquisition functions and state-of-the-art algorithms. Slower but offers superior optimization quality. Best when optimization quality is more important than speed.
GP-Preferential	Bayesian Optimization	Designed exclusively for Human-in-the-loop optimization. Learns from user preferences and pairwise comparisons instead of numerical objectives. Ideal for design optimization based on aesthetic preferences or subjective quality assessment.
HEBO	Bayesian Optimization	Heteroscedastic Evolutionary Bayesian Optimization. State-of-the-art algorithm that excels at highly nonlinear and multimodal problems. Combines evolutionary strategies with Bayesian optimization for faster convergence on challenging landscapes. Also supports multi-objective optimization and Human-in-the-loop optimization.
TuRBO	Bayesian Optimization	Bayesian Optimization with Trust Regions. Uses local trust regions to overcome the limitations of global BO in high dimensions. Enables more efficient optimization in complex high-dimensional spaces by solving each subproblem within a restricted region.
CARBO	Bayesian Optimization	Constrained Robust Bayesian Optimization of expensive noisy black-box functions. Handles both constraints and input noise simultaneously with guaranteed regret bounds. Well-suited for practical engineering problems where measurements are noisy and constraints must be satisfied.
Robust GP	Bayesian Optimization	Robust Gaussian Process-based Bayesian Optimization under Input Noise. Accounts for uncertainty in the input variables during optimization. Provides reliable optimization results when variable settings contain measurement or execution errors.
NSGA-II	Evolutionary Algorithm	Non-dominated Sorting Genetic Algorithm II. Widely used versatile method (also used in Wallacei). Uses non-dominated sorting and crowding distance to maintain diversity. Recommended for 2-3 objective problems requiring diverse Pareto fronts.
NSGA-III	Evolutionary Algorithm	Enhanced NSGA-II for many-objective optimization (3+ objectives). Uses reference points to maintain diversity in high-dimensional objective spaces. Better convergence and diversity for 4 or more objectives compared to NSGA-II.
MOEA/D	Evolutionary Algorithm	Multi-Objective EA based on Decomposition. Decomposes multi-objective problems into single-objective subproblems using scalarization. Efficient for 3- objectives with good computational performance and well-distributed Pareto fronts.
DE	Evolutionary Algorithm	Differential Evolution. Robust global optimization using vector differences. Effective for continuous, non-differentiable functions with many local optima. Simple yet powerful for rugged fitness landscapes.
SPEA-II	Evolutionary Algorithm	Strength Pareto Evolutionary Algorithm II. Uses a strength-based fitness assignment and an external archive to maintain elite solutions. Produces well-distributed Pareto fronts and is effective for both single- and multi-objective problems.
HypE	Evolutionary Algorithm	Hypervolume Estimation Algorithm for multi-objective optimization. Uses hypervolume contribution as the selection criterion to find well-spread Pareto-optimal solutions. Particularly effective for problems requiring diverse coverage of the Pareto front.
CMA-ES	Evolutionary Strategy	Covariance Matrix Adaptation Evolution Strategy. One of the most powerful single-objective continuous optimizers. Adapts covariance matrix to learn problem structure. Very fast convergence on smooth problems with self-adaptive search distribution.
MO-CMA-ES	Evolutionary Strategy	Multi-objective extension of CMA-ES. Maintains multiple search distributions for different Pareto front regions. Fast convergence for 2-3 objectives on smooth landscapes. Efficient use of function evaluations.
INGO	Evolutionary Strategy	Implicit Natural Gradient Optimizer. Uses information geometry and natural gradient methods for efficient single-objective optimization. Fast convergence on well-structured problems with limited function evaluations.
PSO	Swarm Intelligence	Particle Swarm Optimization. Nature-inspired algorithm that simulates the social behavior of birds flocking. Each particle moves through the search space influenced by its own best-known position and the swarm's best-known position.
GreyWolf	Swarm Intelligence	Grey Wolf Optimizer. Inspired by grey wolf hunting behavior and social hierarchy. Uses a leadership hierarchy (alpha, beta, delta, omega) to guide the swarm toward optimal solutions. Effective for continuous single-objective problems.
Whale	Swarm Intelligence	Whale Optimization Algorithm. Bio-inspired algorithm mimicking the bubble-net hunting strategy of humpback whales. Features both exploitation (bubble-net attack) and exploration (random search) phases for robust single-objective optimization.
Random	Sampling	Pure random sampling with uniform distribution. No optimization strategy. Useful for baseline performance, initial exploration, generating diverse populations, and testing purposes. Provides unbiased exploration of search space.
QMC	Sampling	Quasi-Monte Carlo using low-discrepancy sequences (e.g., Sobol). Better space coverage than random sampling with more even distribution. Reduced clustering, more efficient exploration. Effective for design of experiments and sensitivity analysis.
BruteForce	Sampling	Exhaustively evaluates all discrete variable combinations. Guarantees finding global optimum for discrete problems but only feasible for small search spaces. Computational cost grows exponentially. Use with caution.