| Package | Description |
|---|---|
| ec | |
| ec.eval | |
| ec.gp.koza | |
| ec.multiobjective | |
| ec.multiobjective.nsga2 | |
| ec.multiobjective.spea2 | |
| ec.pso | |
| ec.simple |
| Modifier and Type | Field and Description |
|---|---|
Fitness |
Species.f_prototype
The prototypical fitness for individuals of this species.
|
Fitness |
Individual.fitness
The fitness of the Individual.
|
| Modifier and Type | Method and Description |
|---|---|
abstract boolean |
Fitness.betterThan(Fitness _fitness)
Should return true if this fitness is clearly better than _fitness;
You may assume that _fitness is of the same class as yourself.
|
boolean |
Fitness.contextIsBetterThan(Fitness other)
Given another Fitness,
returns true if the trial which produced my current context is "better" in fitness than
the trial which produced his current context, and thus should be retained in lieu of his.
|
abstract boolean |
Fitness.equivalentTo(Fitness _fitness)
Should return true if this fitness is in the same equivalence class
as _fitness, that is, neither is clearly better or worse than the
other.
|
void |
Fitness.merge(EvolutionState state,
Fitness other)
Merges the other fitness into this fitness.
|
void |
Fitness.setToBestOf(EvolutionState state,
Fitness[] fitnesses)
Sets the fitness to be the same value as the best of the provided fitnesses.
|
void |
Fitness.setToMeanOf(EvolutionState state,
Fitness[] fitnesses)
Sets the fitness to be the same value as the mean of the provided fitnesses.
|
void |
Fitness.setToMedianOf(EvolutionState state,
Fitness[] fitnesses)
Sets the fitness to be the median of the provided fitnesses.
|
| Modifier and Type | Method and Description |
|---|---|
void |
MetaProblem.combine(EvolutionState state,
Fitness[] runs,
Fitness finalFitness)
Combines fitness results from multiple runs into a final Fitness.
|
void |
MetaProblem.combine(EvolutionState state,
Fitness[] runs,
Fitness finalFitness)
Combines fitness results from multiple runs into a final Fitness.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KozaFitness
KozaFitness is a Fitness which stores an individual's fitness as described in
Koza I.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
KozaFitness.betterThan(Fitness _fitness) |
boolean |
KozaFitness.equivalentTo(Fitness _fitness) |
void |
KozaFitness.setToMeanOf(EvolutionState state,
Fitness[] fitnesses) |
| Modifier and Type | Class and Description |
|---|---|
class |
MultiObjectiveFitness
MultiObjectiveFitness is a subclass of Fitness which implements basic
multi-objective mechanisms suitable for being used with a variety of
multi-objective selection mechanisms, including ones using pareto-optimality.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
MultiObjectiveFitness.betterThan(Fitness fitness)
Returns true if I'm better than _fitness.
|
boolean |
MultiObjectiveFitness.equivalentTo(Fitness _fitness)
Returns true if I'm equivalent in fitness (neither better nor worse) to
_fitness.
|
void |
MultiObjectiveFitness.setToBestOf(EvolutionState state,
Fitness[] fitnesses) |
void |
MultiObjectiveFitness.setToMeanOf(EvolutionState state,
Fitness[] fitnesses) |
void |
MultiObjectiveFitness.setToMedianOf(EvolutionState state,
Fitness[] fitnesses) |
| Modifier and Type | Class and Description |
|---|---|
class |
NSGA2MultiObjectiveFitness
NSGA2MultiObjectiveFitness is a subclass of MultiObjeciveFitness which
adds auxiliary fitness measures (sparsity, rank) largely used by MultiObjectiveStatistics.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
NSGA2MultiObjectiveFitness.betterThan(Fitness _fitness)
We specify the tournament selection criteria, Rank (lower
values are better) and Sparsity (higher values are better)
|
boolean |
NSGA2MultiObjectiveFitness.equivalentTo(Fitness _fitness) |
| Modifier and Type | Class and Description |
|---|---|
class |
SPEA2MultiObjectiveFitness
SPEA2MultiObjectiveFitness is a subclass of MultiObjectiveFitness which adds three auxiliary fitness
measures used in SPEA2: strength S(i), kthNNDistance D(i), and a final fitness value R(i) + D(i).
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
SPEA2MultiObjectiveFitness.betterThan(Fitness _fitness)
The selection criteria in SPEA2 uses the computed fitness, and not
pareto dominance.
|
boolean |
SPEA2MultiObjectiveFitness.equivalentTo(Fitness _fitness)
The selection criteria in SPEA2 uses the computed fitness, and not
pareto dominance.
|
| Modifier and Type | Field and Description |
|---|---|
Fitness[] |
PSOBreeder.globalBestFitness |
Fitness |
Particle.neighborhoodBestFitness |
Fitness |
Particle.personalBestFitness |
| Modifier and Type | Class and Description |
|---|---|
class |
SimpleFitness
A simple default fitness, consisting of a double floating-point value
where fitness A is superior to fitness B if and only if A > B.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
SimpleFitness.betterThan(Fitness _fitness) |
boolean |
SimpleFitness.equivalentTo(Fitness _fitness) |
void |
SimpleFitness.setToMeanOf(EvolutionState state,
Fitness[] fitnesses) |
Copyright © 2014 Evolutionary Computation Laboratory at George Mason University. All rights reserved.