Domain: Locate the maximum value in an array. More...
#include <max.h>
Inheritance diagram for find_max_value::FindMaxValue:
Public Member Functions | |
| size_t | inputs () const override |
| Number of inputs to a Brain. | |
| size_t | outputs () const override |
| Number of outputs from a Brain. | |
| bool | evaluatePopulation (darwin::Population *population) const override |
| Assigns fitness values to every genotype. More... | |
 Public Member Functions inherited from darwin::Domain | |
| virtual unique_ptr< core::PropertySet > | calibrateGenotype ([[maybe_unused]] const Genotype *genotype) const |
| Optional: additional fitness metrics (normally not used in the population evaluation, ie a test set) | |
Detailed Description
Domain: Locate the maximum value in an array.
This is a minimalist problem intended to evaluate a population's ability to evolve basic memory facilities: locate the maximum value in an array of integers.
This deceptively simple problem requires the emergence of a few capabilities:
- Navigating the one-dimensional array, covering all the values
- Remembering values (at least a current maximum)
- Comparing integer values (to determine the maximum)
Core concepts
- The test "world": a one dimensional array of a random length and initialized with random values.
- The agent is a "pointer/index" within the test world.
Inputs
| Input | Value |
|---|---|
| 0 | left_antenna (1.0 if the the agent is positioned at the left edge) |
| 1 | right_antenna (1.0 if the the agent is positioned at the right edge) |
| 2 | input_value |
Outputs
| Output | Value |
|---|---|
| 0 | move_left |
| 1 | move_right |
| 2 | done (Accept current position as the final answer) |
The largest output signal determines the agent's action at the current step.
Member Function Documentation
◆ evaluatePopulation()
|
inlineoverridevirtual |
Assigns fitness values to every genotype.
Having a good fitness function is a key part of evolutionary algorithms:
- Perhaps obvious, the fitness value should accurately estimate the quality of a particular solution
- A "smooth" distribution is preferable since it provides a gradient which can guide the incremental search in the solutions space. (ex. if most fitness values are 1.0 or 0.0 it's hard to know which genotypes are good candidates for reproduction)
- Returns
trueif the evolution goal was reached
Implements darwin::Domain.
The documentation for this class was generated from the following file:
- C:/Users/lemo/work/darwin/domains/max/max.h
