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Evolving Algorithms that Use Memory

Evolving Algorithms that Use Memory. Different types of memory Indexed memory Named memory Automatically defined storage Evolving the swap function. Types of Memory. Indexed Memory Named memory Write and read operators are used to access both types of memory.

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Evolving Algorithms that Use Memory

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  1. Evolving Algorithms that Use Memory • Different types of memory • Indexed memory • Named memory • Automatically defined storage • Evolving the swap function

  2. Types of Memory • Indexed Memory • Named memory • Write and read operators are used to access both types of memory.

  3. Automatically Defined Storage • An ADS is comprised of two branches: • A storage writing branch (SWB) • A storage reading branch (SRB) • Each ADS has a: • Name • Dimension • Type • Size

  4. Dimensions and Types Dimension Type 0 Named memory, pushdown stack,queue Indexed memory, list 1 2 Two-dimensional array, relational memory 3 Three-dimensional array 4 Four – dimensional array

  5. SWB and SRB • An individual can contain a number of ADSs, each of a different dimension, type and size. • The arity of SWB and SRB is dependent on the type of memory used. • The system maintains a memory structure of the correct type. • Architecture-altering operators

  6. ADS Example

  7. Evolving the swap Function • The write operator • The change operator • Each individual • Parse tree • Memory Structure • Initial population generation • Evaluation • Effect on genetic operators

  8. GP Parameters • Function to generate: Induce an algorithm for the swap function • Terminal set:T = {var#01, var#02} • Function set:F = {write, change, block3} • Number of generations:51 • Population size: 500 • Raw fitness: The number of fitness cases for which both the output values are correct. • Bound: 0

  9. GP Parameters • Method of selection:Tournament selection with a tournament size of 4. • Initial population generator:The ramped half-and-half method with an initial tree depth of 3 and a node size limit of ten on the trees created by the genetic operators. • Genetic operators: • Crossover - 50% • Reproduction - 0% • Mutation - 50% (mutation depth of 2)

  10. Evolved Solution Fitness case: Input: n1=2 , n2=3 Target: n1 =3 , n2 =2

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