coevolutionary automated software correction

Coevolutionary Automated Software Correction Coevolutionary Automated Software Correction

Josh WilkersonJosh Wilkerson

PhD Candidate in Computer SciencePhD Candidate in Computer Science

Missouri S&TMissouri S&T

Technical Background

Evolutionary Algorithms (EAs)– Subfield of evolutionary computation (in artificial intelligence)

– Based on biological evolution

– Uses mutation, reproduction, and selection

– Population composed of candidate solutions

– Needed:

• Solution representation

• Fitness function

– Applicable to a wide variety of fields

– Makes no assumptions about the problem space (ideally)


EA Operation– Start with an initial population

– Each generation

• Create new individuals and evaluate them

• Population competition (survival of the fittest)

– Mutation and reproduction

• Explore the problem space

• Bring in new genetic material

– Selection

• Applies pressure to individuals

• More fit individuals are selected for mutation and reproduction more often


Genetic Programming– Type of EA

– Evolves tree representations

– E.g., computer program parse trees

Coevolution– Extension of standard EA

– Fitness dependency between individuals

– Dependency can be either cooperative or competitive

– CASC system uses competitive coevolution

– Evolutionary arms-race

High Level View of CASC

CASC Evolutionary Model

Reproduction Phase: Programs

Randomly select a genetic operation to perform

– Probability of operation selection is configurable

Perform operation, generate new program(s)

Add new individuals to population

Repeat until specified number of individuals has been created

Reproduction Phase: Programs

Genetic Operations

– Reset

– Copy

– Crossover

• Two individuals are randomly selected based off fitness

• Randomly select and exchange compatible sub-trees

• Generates two new programs

– Mutation

• Randomly select individual based off fitness

• Randomly select and change mutable node

• Generate a new sub-tree (if necessary)

– Architecture Altering Operations

Reselection is allowed for all operators

Reproduction Phase: Test Cases

Reproduction employs uniform crossover

Each offspring has a chance to mutate

Genes to mutate are selected random

Mutated gene is randomly adjusted

– The amount adjusted is selected from a Gaussian distribution

CASC Evolutionary Model

CASC Implementation Details

Adaptive parameter control

– EAs typically have many control parameters

– Difficult to find optimal settings for these parameters

– In CASC genetic operator probabilities are adaptive parameters

– Rewarded/punished based on performance

• If one operator is generating improved individuals more than the others make it more likely to be used

– Allows the system to adapt to the different phases in the search

CASC Implementation Details

Parallel Computation– Computational complexity is generally a problem for Eas

– CASC writes, compiles, and executes hundreds (or even thousands) of C++ programs in a given run

– To reduce run times this is done in parallel (on the NIC cluster here on campus)

– Main node: responsible for generating and writing programs

– Worker nodes: responsible for compiling and executing programs

– Dramatically speeds up execution

– Investigating new options for this (discussed later)

Current and Future Work

Fitness Function Design– For each new problem CASC needs a new fitness function

– Fitness function design can often be difficult

– Developing a guide for fitness function design

– Starts a program specifications

– Walks through the thought process for designing a fitness function for the problem

– Long term goal: automate fitness function creation


File system slow down– CASC is writing and compiling many many programs each run

– I.e., many many files in the file system each run

– File system access is bottlenecking the speed of the CASC system

– Currently reworking the system to store program files and executables in RAM

– Uses a virtually mounted hard disk that stored data in RAM

– Expecting a dramatic speed up (fingers crossed…)

– Other option: distributed computing (like BOINC, Folding@home, etc.)


Scalability– As program size increases so does the problem space

• Many more modifications possible

• More genetic material

– Investigating options to allow CASC to scale with problem size

– Current idea: break the program up into pieces

• Multiple program populations

• Each population is based on a piece of the original program

• Each population has its own objective

• Cooperative coevolution

Questions?

coevolutionary automated software correction

Documents

casc evolutionary modelpage

competitive casc system

new programsmutationrandomly

reproduction phase

new subtree

improved individuals

configurableperform

problem spacebring