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https://issues.apache.org/jira/browse/MATH-1618?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17417665#comment-17417665
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Gilles Sadowski edited comment on MATH-1618 at 9/20/21, 1:44 PM:
-----------------------------------------------------------------
*Note*: I've no GH account, so I'm
[commenting|https://github.com/apache/commons-math/pull/197#issuecomment-922933961]
here.
{quote}
{code}
private static class DummyChromosome extends AbstractChromosome<String> {
DummyChromosome() {
super(c -> {
return counter++;
}, c -> {
return "0";
});
}
{code}
{quote}
Please try something (untested) along:
{code}
private static class DummyChromosome extends AbstractChromosome<String> {
DummyChromosome() {
super(c -> counter++, c -> "0");
}
{code}
was (Author: erans):
*Note*: I've no GH account, so I'm
[commenting|https://github.com/apache/commons-math/pull/197#issuecomment-922933961]
here.
{quote}
{code}
private static class DummyChromosome extends AbstractChromosome<String> {
DummyChromosome() {
super(c -> {
return counter++;
}, c -> {
return "0";
});
}
{code}
{quote}
Please try something (unstested) along:
{code}
private static class DummyChromosome extends AbstractChromosome<String> {
DummyChromosome() {
super(c -> counter++, c -> "0");
}
{code}
> Change in Existing Design
> -------------------------
>
> Key: MATH-1618
> URL: https://issues.apache.org/jira/browse/MATH-1618
> Project: Commons Math
> Issue Type: Sub-task
> Affects Versions: 3.6.1
> Reporter: AVIJIT BASAK
> Priority: Major
>
> *1) Creation of abstraction for GeneticAlgorithm*: In order to have different
> types of implementation for Genetic Algorithm like adaptive GA along with the
> existing one, we need to introduce an abstraction and a hierarchy of
> algorithm. AbstracttGeneticAlgorithm class needs to be implemented which
> would be extended by all other Algorithm class. This would also ease any
> future extension.
> Removed Components: None
> New Components: AbstractGeneticAlgorithm
> Affected Components: GeneticAlgorithm
> *2) Delegation of fitness calculation*: As per the current design Fitness
> interface is implemented by chromosome class, which forces implementation of
> fitness() method for any concrete chromosome. However this restricts the use
> of same concrete chromosome implementation to be reused for different problem
> domain. This inheritance based implementation should be replaced by
> composition. A new interface FitnessCalculator would be introduced. An
> instance of FitnessCalculator will be provided during creation of every
> concrete chromosome. This will enable reuse of concrete chromosome classes in
> different problem domain and hence improve extensibility and re-usability.
> This will require addition of an argument for each factory method and
> constructors.
> Removed Components: Fitness
> New Components: FitnessFunction
> Affected Components: Chromosome, AbstractListChromosome, BinaryChromosome,
> RandomKey
> *3) Enable finer control for mutation and crossover probability*: Current
> design uses the crossover and mutation probability at the chromosome level.
> For finer control of mutation and crossover process the probability would be
> managed within MutationPolicy and CrossoverPolicy implementations.
> Probability would be passed as an argument to the respective operations. This
> way the corresponding operations will be responsible for managing probability
> and apply in convenient way. I have seen the controlling the mutation
> probability at the allele(gene) level improves the exploring capability of
> the optimization process and hence improves robustness.
> Removed Components: None
> New Components: None
> Affected Components: MutationPolicy, CrossoverPolicy and all other
> implementation classes
> *4) Addition of new Simulation Stopping conditions*: New simulation stopping
> conditions would be added based on population statistical characteristics.
> The simulation can be stopped based on variations of population average
> fitness or best fitness. These parameters are much better to represent nature
> of convergence. This will improve robustness to a considerable extent.
> Removed Components: None
> New Components: UnchangedAvgFitness, UnchangedBestFitness,
> Affected Components: SimulationStoppingCondition, GeneticAlgorithm,
> FixedElapsedTime, FixedGenerationCount
> *5) Introduction of Convergence listeners*: New convergence listener
> interface and registry would be introduced to enable tracking of convergence.
> Removed components: None
> New Components: ConvergenceListener, ListenerRegistry,
> PopulationStatisticalSummary
> Affected Components: AbstractGeneticAlgorithm, GeneticAlgorithm
> *6) Restructuring Elitism*: Elitism can be handled by the hierarchy of
> Algorithm classes. Use of a separate ElitisticListPopulation class may not be
> necessary. The method nextGeneration() in Population would accept the elitism
> rate as an argument. Any concrete instance of Population would provide the
> necessary implementation.
> Removed components: ElitisticListPopulation
> New Component: None
> Affected Components: AbstractGeneticAlgorithm, GeneticAlgorithm, Population,
> ListPopulation
> *7) Introducing Hierarchy in Crossover and Mutation Operator*: Abstract
> classes has been introduced for crossover and mutation operators.
> Removed components: None
> New Component: AbstractChromosomeCrossoverPolicy,
> AbstractListChromosomeCrossoverPolicy, AbstractListChromosomeMutationPolicy
> Affected Components: All concrete implementation classes for crossover and
> mutation.
> *8) Rename the method newFixedLengthChromosome() in
> AbstractList**Chromosome***: The method newFixedLengthChromosome would be
> renamed as newChromosome. As we are not considering any variable length
> chromosome and chromosome representation is immutable in
> AbstractListChromosome we don't need any explicit mention of the phrase
> 'FixedLength' as part of the method.
> *9) Introduction of Decoder and Phenotype:* Phenotype has been introduced as
> Generic. Decoder has been also introduced to transform the chromosome
> genotype to phenotype. The phenotype would be used for majority of
> computation like calculation of fitness, chromosome comparison etc. This is
> major design change from the previous model. This will enable same genotype
> to be used for both direct and indirect encoding of chromosomes with an
> exchange of decoder.
> Removed Components: None
> New Components: Decoder, AbstractListChromosomeDecoder, ,
> TransparentListChromosomeDecoder, RandomKeyDecoder.
> Affected Components: Chromosome, AbstractChromosome, FitnessFunction
> All classes were modified to incorporate phenotype <P> as Generic.
> 10) Introduction of RealValuedChromosome: A new genotype has been introduced
> as real valued chromosome. This can be also used for RandomKey encoding.
> Removed Components: RandomKey
> New Components: RealValuedChromosome, RandomKeyDecoder
> Affected Components: None
>
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