# Genetic Algorithms

Τεχνίτη Νοημοσύνη και Ρομποτική

23 Οκτ 2013 (πριν από 4 χρόνια και 6 μήνες)

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Genetic Algorithms
:

An Examination of the Traveling Salesman Problem

Troy Cok

Engineering 315

December 3, 2001

Basic Overview

o
Genetic algorithms are attempts to model
evolutionary behavior

o
Survival of the fittest, etc.

o
More than mere
simulations

of life

o
Goal: Exhibit real characteristics of living things

o
GA’s have several uses

o
Problem solvers

o
Basis for competent machine learning

o
Computational models of innovation, creativity, etc.

Classic GA Problem

Traveling Salesman Problem (TSP)

Implications in science, engineering

Control of routing system

Constraints:

Can be in one city at a time

Each city visited once and only once

Problem:

What is the shortest route through N cities?

Mathematical Attempts at TSP

Testing every possibility would require

For a 15 city tour:

15! = 1.31 x 10
12

separate calculations

Assuming 1 million calculations per second

15.2 days

Increasing complexity…

Solving TSP using GA

Generates a “near
-
perfect” solution in
minutes

Steps:

1.
Create group of random tours

Stored as sequence of numbers (parents)

2.
Choose 2 of the better solutions

Combine and create new sequences (children)

Problems here:

City 1 repeated in Child 1

City 5 repeated in Child 2

Modifications Needed

Algorithm must not allow repeated cities

Also, order must be considered

12345 is same as 32154

Based upon these considerations, a
computer model for N cities can be
created

Gets quite detailed

Some Existing GA Programs

Final Notes

“solution” to TSP

Much faster than sheer
number
-
crunching

Enables efficient system
control

The better the algorithm,
the better the solution

Ongoing research in this
area