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List:       jakarta-commons-dev
Subject:    [jira] [Updated] (MATH-1618) Change in Existing Design
From:       "AVIJIT BASAK (Jira)" <jira () apache ! org>
Date:       2021-08-31 15:27:00
Message-ID: JIRA.13392905.1627796850000.953118.1630423620820 () Atlassian ! JIRA
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     [ https://issues.apache.org/jira/browse/MATH-1618?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel \
]

AVIJIT BASAK updated MATH-1618:
-------------------------------
    Description: 
*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:  FitnessCalculator

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, \
PopulationStatisticalSummary

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

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

  was:
*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:  FitnessCalculator

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, \
PopulationStatisticalSummary

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

Affected Components: AbstractGeneticAlgorithm, GeneticAlgorithm


> 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:  FitnessCalculator
> 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, \
> PopulationStatisticalSummary 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
> 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



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