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https://issues.apache.org/jira/browse/MATH-1618?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17394018#comment-17394018
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Gilles Sadowski commented on MATH-1618:
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bq. [...] {{AbstractGeneticAlgorithm}} [...]
With what functionality?
All the proposed enhancements seem fine, but they would be easier to grasp if
the textual explanation were accompanied with (pseudo-)code excerpts (and/or
pointers to source 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: 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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