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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: 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.
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: 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
> 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.
>
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