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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
Affected Components: None
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) Introducing Elitism interface*: In current design ElitisticListPopulation
introduces couple of new operations related to elitism without declaring them
in any abstraction. Elitism interface would be introduced, which would be
implemented by ElitisticListPopulation.
Removed Components: None
New Components: Elitism
Affected Components: ElitisticListPopulation
*4) Change of Indirect encoding chromosome hierarchy*: The hierarchy of
chromosome having indirect encoding would be changed. Currently the design only
considers permutation chromosome for combinatorial optimization. The base
interface is PermutationChromosome which is implemented by RandomKey
chromosome. A more appropriate name(like IndirectEncoding) of
PermutationChromosome can be used which will declare the decode() method. This
interface will be implemented by RandomKey chromosome. Tt would be more
meaningful for any other new indirectly encoded chromosome representing
different domain to implement the new interface.
Removed Components: PermutationChromosome
New Components: IndirectEncoding
Affected Components: RandomKey
*5) 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
*6) 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.
> 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
> Affected Components: None
>
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