# Genetic Algorithms

Τεχνίτη Νοημοσύνη και Ρομποτική

24 Οκτ 2013 (πριν από 4 χρόνια και 7 μήνες)

100 εμφανίσεις

Genetic Algorithms

CS460: Capstone Experience Project

Sergii

S.
Bilokhatniuk

Project

Simulate genetic algorithms

and analyze effects of mutations

General Requirement

Develop a gentle tutorial for the concept of genetic algorithms.

Pick an existing program and modify it.

The system graphically displays the state of each generation with
appropriate statistics that show progress toward the goal.

The system should allow dynamic modification of parameters, operators,
and probabilities.

Pick a new problem and create a genetic solution by mutating populations.
The problem should be NP
-
complete and your results should be compared
analytically to a known algorithm that approximates a solution.

Potential Applications of GA

virtually anything where potential solution is

a)
string of symbols

b)
testable for fitness

Generating automatons

Finding routes

Constructing formulas

Writing
War & Peace
(not really)

Choosing the Problem

Traveling Salesman Problem (TSP):

Given a list of cities and a map of the roads

visit each city
once,

come back to hometown

use the
shortest

route.

TSP, Domain and Range

Input: Map

Output: Path

TSP Solution Process

a)

b)
???

c)
Profit

TSP Solution Process

a)
Create initial population of routes

b)
Assess fitness of each route

c)
If not satisfactory
, create new population

d)
Introduce mutation (optional)

e)
Goto

b)

Choosing Implementation

Implementation

Assessment

Java Applet / JavaScript

seems popular

Server
-
side (Java/.NET) model
and client
-
side view
-
controller
(JavaScript/HTML)

would be
awesome

Standalone desktop application
(C#, Window Forms)

could actually
work

Species

Generation of Solution

a)
select first/last node (using schemata*)

b)
randomly generate a specie

c)
test if good (not bad or ugly)

d)
Repeat

*

Procreation

Schema One

Small rate of success

More Procreation

Schema Two

Greater rate of success

Creates Good/Ugly

Mutation

Schema One, Random

(for not
-
connected graph)

More Mutation

Schema Two, Selective

Generates Good/Ugly

Fitness

Simple comparer

Maximum

Minimum

Average

Population Control

Elitism Rate

% of population selected to be carried over to
next generation
without
change

Elite gets to procreate too

Discard same % of least performing part of
population

Mutation Rate

% of genes of each new specie that get mutated

Process

Demo

“Let There Be Algorithms…”

What I have learned

It was all worth it.

Thanks! I hope it went well

Question?

Suggestions?

Job Offers?