evomo.operators#

Submodules#

Functions#

crowding_distance(costs, mask)

Compute the crowding distance for a set of solutions in multi-objective optimization.

non_dominate_rank(→ torch.Tensor)

Compute the non-domination rank for a set of solutions in multi-objective optimization.

Package Contents#

evomo.operators.crowding_distance(costs: torch.Tensor, mask: torch.Tensor)#

Compute the crowding distance for a set of solutions in multi-objective optimization.

The crowding distance is a measure of the diversity of solutions within a Pareto front.

Parameters:

  • costs – A 2D tensor where each row represents a solution, and each column represents an objective.

  • mask – A 1D boolean tensor indicating which solutions should be considered.

Returns:

A 1D tensor containing the crowding distance for each solution.

evomo.operators.non_dominate_rank(x: torch.Tensor, cv: torch.Tensor = None) → torch.Tensor#

Compute the non-domination rank for a set of solutions in multi-objective optimization.

The non-domination rank is a measure of the Pareto optimality of each solution.

Parameters:

  • x – A 2D tensor where each row represents a solution, and each column represents an objective.

  • cv – An optional tensor containing the constraint violations of the solutions.

Returns:

A 1D tensor containing the zero-based non-domination rank for each solution. Solutions in the first Pareto front have rank 0.