HighLevelSelection

Full Name

neuroptimiser.core.processes.HighLevelSelection

Description

class HighLevelSelection

Bases: AbstractProcess

High-Level Selection Process

This process is designed to select the global best position and fitness from current candidates from spiking cores based on a given metric, i.e., fitness.

Inports

p_inInPort

Input port for the position variable.

fp_inInPort

Input port for the fitness variable.

Variables

pVar

Variable for the position of the agent.

fpVar

Variable for the fitness of the agent.

gVar

Variable for the global best position.

fgVar

Variable for the global best fitness.

Outports

g_outOutPort

Output port for the global best position.

fg_outOutPort

Output port for the global best fitness.

See also

neuroptimiser.core.models.PyHighLevelSelectorModel

Model implementation of the HighLevelSelection process.

__init__(num_dimensions: int = 2, num_agents: int = 1, **kwargs)

Initialise the HighLevelSelection with the given parameters.

Parameters:

• num_dimensions (int, optional) – Number of dimensions for the position and fitness variables. Default is 2.

• num_agents (int, optional) – Number of agents in the system. Default is 1.

Keyword Arguments:

**kwargs (dict, optional) – Additional keyword arguments to be passed to the parent class AbstractProcess.

reset() → None

Reset the HighLevelSelection to its initial state.