evomo.algorithms.hype#

Classes#

HypE

The tensorized version of HypE algorithm.

Functions#

cal_hv(fit, ref, pop_size, n_sample)

Module Contents#

evomo.algorithms.hype.cal_hv(fit: torch.Tensor, ref: torch.Tensor, pop_size: int, n_sample: int)#
class evomo.algorithms.hype.HypE(pop_size: int, n_objs: int, lb: torch.Tensor, ub: torch.Tensor, n_sample: int = 10000, selection_op: Callable | None = None, mutation_op: Callable | None = None, crossover_op: Callable | None = None, device: torch.device | None = None)#

Bases: evox.core.Algorithm

The tensorized version of HypE algorithm.

References:
[1] J. Bader and E. Zitzler, “HypE: An algorithm for fast hypervolume-based many-objective optimization,”

Evolutionary Computation, vol. 19, no. 1, pp. 45-76, 2011. Available: https://direct.mit.edu/evco/article-abstract/19/1/45/1363/HypE-An-Algorithm-for-Fast-Hypervolume-Based-Many

[2] Z. Liang, H. Li, N. Yu, K. Sun, and R. Cheng, “Bridging Evolutionary Multiobjective Optimization and

GPU Acceleration via Tensorization,” IEEE Transactions on Evolutionary Computation, 2025. Available: https://ieeexplore.ieee.org/document/10944658

Initialize the HypE population and optimization state.

Parameters:

  • pop_size (int) – Required. Requested number of candidate solutions. Use a positive integer; algorithm-specific minimums and reference-vector sampling are described below.

  • n_objs (int) – Required. Number of objectives, matching the second dimension of the problem’s objective tensor. Objectives are minimized; this library targets two or more objectives.

  • lb (torch.Tensor) – Required. Lower decision bounds of shape (D,), where D is the number of decision variables. Use floating-point bounds with the same shape, dtype and device as ub, and lb <= ub elementwise.

  • ub (torch.Tensor) – Required. Upper decision bounds of shape (D,). The decision dimension is inferred from the bounds, rather than passed separately. Match lb in shape, dtype and device.

  • n_sample (int) – Default: 10000. Positive number of Monte Carlo samples for each hypervolume contribution estimate. More samples reduce sampling noise at higher computation and memory cost.

  • selection_op (Callable or None) – Default: None. Accepted in the signature, but the current constructor overwrites it with EvoX’s tournament_selection. A custom value has no effect.

  • mutation_op (Callable or None) – Default: None. Mutation callable mutation_op(offspring, lb, ub) -> mutated_offspring. Input and output are decision tensors of shape (B, D); preserve device and return an appropriate decision dtype. None selects EvoX’s polynomial_mutation.

  • crossover_op (Callable or None) – Default: None. Crossover callable crossover_op(parents) -> offspring receiving a two-dimensional decision tensor. None selects EvoX’s simulated_binary. Preserve the decision dimension and device, and produce the offspring count expected by this algorithm.

  • device (torch.device or None) – Default: None. Execution device. None uses torch.get_default_device(); it does not infer the device from the bounds. Bounds are copied to this device. Pass torch.device('cuda') explicitly for GPU execution.

Note

Use this algorithm through a workflow that connects the problem’s evaluation method. Call workflow.init_step() before workflow.step() or compiling the step. The current evaluation path expects an objective tensor of shape (B, n_objs). It does not consume a (fitness, constraint_violation) tuple.

init_step()#

Evaluate the initial population and initialize algorithm state.

Invoke through workflow.init_step() before the first optimization step.

Returns:

None; results are stored in algorithm state.

step()#

Advance optimization and update population and fitness state in place.

Invoke through workflow.step() after initialization. The caller controls termination.

Returns:

None; results are stored in algorithm state.