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(Genetic Algorithm Interface Architecture)
Final Presentation
CS 425
Created By:
Chuck Hall
Simone Connors
Héctor Aybar
Mike Grim
WHAT IS A GENETIC ALGORITHM?
Search algorithm that mimics natural genetics
Searches for optimal solutions to complex functions
Shared fundamental units of natural genetics

Chromosomes

Genes
WHAT IS A GENETIC ALGORITHM?
Chromosomes are usually represented as arrays
Chromosome
7 Genes
1
0
25
0
0
100
1
0
320
1
1
90
0
0
37
1
1
222
0
1
10
WHAT IS A GENETIC ALGORITHM?
GA Hierarchy

Populations are comprised of chromosomes

A single chromosome is a member of the population

Chromosomes are comprised of genes

Genes contain values
If the GA is solving f(x), then one chromosome is an ‘x’
WHAT IS A GENETIC ALGORITHM?
Shared fundamental principles of natural genetics

Crossover (parent chromosomes swap some genes)

Mutation (specific genes are given new values)

Parents/Offspring

Populations/Generations

Higher quality offspring have a better chance of reproducing and
surviving (chromosomes are assigned a ‘fitness value’)
WHAT IS A
GENETIC ALGORITHM?
Choose parent chromosomes
Perform Crossover
Basic flow of the algorithm
Perform Mutation
Can be specialized

Elitism

Convergence criteria
Place children in the next generation
 Number of generations
Is the
generation
full?
 Best fitness value
 Average fitness value

Yes
Pool
Terminate the algorithm
No
Yes
Have the
convergence
criteria
been met?
No
Create another
generation
SCENARIO – minimize f(x) = x2
Joe makes some decisions
 Minimize between 0 and 15
 Represent x with a 4 gene chromosome
 Represent each gene with a single bit
He writes a fitness function
He randomly creates the first generation
SCENARIO – minimize f(x) = x2
SCENARIO – minimize f(x) = x2
Choose parent chromosomes
Duplicate them
Perform crossover
Perform mutation
Determine fitness value
Store new chromosomes
mutation
0
1
0
0
0
0
0
1
1
crossover point
SCENARIO – minimize f(x) = x2
GAIA terminates after the 500th generation
Best chromosome in the final generation:

bit-string of all zeroes

fitness value of 0
WHY USE A GENETIC ALGORITHM?
Calculus doesn’t always work
It’s not feasible to try every possibility.
3 Vital fundamentals of genetics
 Crossover (refine within a region)
 Mutation (explore a new region)
 Survival of the fittest
WHY USE A GENETIC ALGORITHM?
STAGED DELIVERY LIFECYCLE
Software
Concept
Requirements
Analysis
Architectural
Design
Stage 1: default bit-string and function pointer prototype
Delivery Date: October 11, 2004
Stage 2: default integer and double-string prototype
Delivery Date: November 8, 2004
Stage 3: module efficiency prototype
Delivery Date: December 6, 2004
Detailed
Design
Coding
Testing
Delivery
STAGE 1
Function pointers and default bit string prototype




User can specify a user-defined gene type
Function pointers allow GAIA to use user supplied functions
Bit string is the default gene type that will be provided
Functions that perform operations on bit string gene types will
be provided
STAGE 2
Default integer and double string prototype

Integer gene type is a default gene type that will be provided

Default functions to perform operations on integer gene types


Double string gene type is a default gene type that will be
provided
Default functions to perform operations on double string gene
types
STAGE 3
Module efficiency prototype


Includes improvements to algorithms used in the GAIA library
 Copy memory to and from internal data structures
 Slows Gaia down
 Make improvements to internal data structures
Improves efficiency of GAIA library
TIMELINE FOR FALL 2004
WHY STAGED DELIVERY?
Deliver a functioning part of GAIA library early in
development
Shows signs of progress to client
Allows for the discovery of problems early in
development
Client knew exactly what wanted
TIMELINE FOR SPRING 2004
TEAM ORGANIZATION
Edmond
Abrahamian
Dr. Yu
Upper
Management
Client
Chuck Hall
Project Manager
Simone Connors
Hector Aybar
Mike Grim
Project Member
Project Member
Project Member
HARDWARE/SOFTWARE MAPPING
SYSTEM
ARCHITECTURE
Interface
3-Tier
– Interface
– Application Logic
– Storage
API
Application Logic
Genetic Algorithm
Management
Storage
Librarian Instance
Storage
Create two pointers:
Instance of GAIA
GA Parameters structure
Initialize Memory:
Instance of GAIA
Instance of GA Parameters structure
FLOW OF EVENTS (API)
GA Parameters and Function
Pointers are setup:
parameters
delete_chromosome
copy_chromosome
crossover
mutation
selection
fitness
Is there
an error?
No
Yes
Primary application fills the initial population.
Optimize problem using the
genetic algorithm.
(Details in another figure.)
Primary application asks for the
solution to be returned to it.
Free all the memory associated
with an instance of GAIA.
Primary application requests
a human-readable error to
be returned.
If no error, just return.
Precondition:
Initial population has been filled and all
Fitness values have been calculated.
FLOW OF EVENTS:
GENETIC ALGORITHM
Select 2 parent chromosomes.
Make 2 copies of each.
Start creating the next generation.
No
Perform crossover and mutation on one set.
Have any of the
convergence criteria
been met?
Calculate fitness values for the 2 children.
No
Check each
parent and child
chromosome.
Is it already in
the pool?
Insert into the pool
sorted by
fitness value.
Copy the best chromosomes
from the pool into
the next generation.
Yes
Delete the copy.
No
Is the pool
filled?
Yes
Yes
Halt the algorithm.
WHERE DO WE GO NOW?
What affected our design
Languages and Development Tools
Plans for Implementation, Testing, and Development
WHAT AFFECTED OUR DESIGN?
Unix/Linux
Symmetric Multi-Processing
Programming Languages:
 C
 XML
Ensuring functions are reentrant
Abstraction
Dynamic Design
LANGUAGES AND DEVELOPMENT TOOLS
Languages:

C

XML
Development Tools:

expat

GCC/GDB

CVS

OpenOffice
PLANS FOR IMPLEMENTATION
Two primary development leads:

Chuck

Mike
Chuck will lead the implementation of the GA.
Mike will lead the implementation of the API and
support libraries.
Mike Grim
Chuck Hall
Lead Developer
Lead Developer
Simone Connors
Héctor Aybar
Backup Developer
Backup Developer
PLANS FOR TESTING
Two primary testing leads:

Hector

Simone
Hector will lead the testing of the GA.
Simone will lead the testing of the API and support
libraries.
Héctor Aybar
Simone Connors
Lead Tester
Lead Tester
Chuck Hall
Mike Grim
Backup Tester
Backup Tester
PLANS FOR TESTING
Hierarchical Testing Plan

Unit Testing (During Implementation)

Integration Testing

System Testing (F6 Function)
Acceptance Testing
PLANS FOR DEPLOYMENT
Client will take source and Makefiles.
Client will build GAIA and link with his code.
Code will be available to client in multiple forms:

CVS of most recent code

Tarball (archive) of latest stable build
Code and project site will be transferred to
SourceForge.
WHAT IS IT THOU SEEKS?