AVIJIT BASAK created MATH-1618: ---------------------------------- Summary: 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
*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. -- This message was sent by Atlassian Jira (v8.3.4#803005)