org.uncommons.watchmaker.framework
Interface FitnessEvaluator<T>

Type Parameters:

T - The type of evolvable entity that can be evaluated.

All Known Implementing Classes:

CachingFitnessEvaluator

public interface FitnessEvaluator<T>

Calculates the fitness score of a given candidate of the appropriate type. Fitness evaluations may be executed concurrently and therefore any access to mutable shared state should be properly synchronised.

Author:

Daniel Dyer

Method Summary

double	getFitness(T candidate, List<? extends T> population) Calculates a fitness score for the given candidate.
boolean	isNatural() Specifies whether this evaluator generates natural fitness scores or not.

Method Detail

getFitness

double getFitness(T candidate,
                  List<? extends T> population)

Calculates a fitness score for the given candidate. Whether a higher score indicates a fitter candidate or not depends on whether the fitness scores are natural (see isNatural()). This method must always return a value greater than or equal to zero. Framework behaviour is undefined for negative fitness scores.

Parameters:

candidate - The candidate solution to calculate fitness for.

population - The entire population. This will include the specified candidate. This is provided for fitness evaluators that evaluate individuals in the context of the population that they are part of (e.g. a program that evolves game-playing strategies may wish to play each strategy against each of the others). This parameter can be ignored by simple fitness evaluators. When iterating over the population, a simple reference equality check (==) can be used to identify which member of the population is the specified candidate.

Returns:

The fitness score for the specified candidate. Must always be a non-negative value regardless of natural or non-natural evaluation is being used.

isNatural

boolean isNatural()

Specifies whether this evaluator generates natural fitness scores or not.

Natural fitness scores are those in which the fittest individual in a population has the highest fitness value. In this case the algorithm is attempting to maximise fitness scores. There need not be a specified maximum possible value.

In contrast, non-natural fitness evaluation results in fitter individuals being assigned lower scores than weaker individuals. In the case of non-natural fitness, the algorithm is attempting to minimise fitness scores.

An example of a situation in which non-natural fitness scores are preferable is when the fitness corresponds to a cost and the algorithm is attempting to minimise that cost.

The terminology of natural and non-natural fitness scores is introduced by the Watchmaker Framework to describe the two types of fitness scoring that exist within the framework. It does not correspond to either standardised fitness or normalised fitness in the EA literature. Standardised fitness evaluation generates non-natural scores with a score of zero corresponding to the best possible fitness. Normalised fitness evaluation is similar to standardised fitness but with the scores adjusted to fall within the range 0 - 1.

Returns:

True if a high fitness score means a fitter candidate or false if a low fitness score means a fitter candidate.