GeneHunter

- A -

ability to recreate the same population

adjustable cells (chromosomes)

advanced topics

allele

alphabet

appendix A - genetic algorithm internals and advanced topics

appendix B - glossary

appendix C - references

- B -

best solutions

binary and enumerated chromosomes in GALIB

- C -

Changes from 1.0 or 1.1 to 2.0

Changes from 2.0 to 2.1

Changes from 2.1 to 2.2

Changes from 2.2 to 2.3

Changes from 2.3 to 2.4

Changes from 2.4 to 2.5

Chapter 1 - Getting Started

chapter 10 - integrating genetic algorithms and neural networks

Chapter 2 - What are Genetic Algorithms

Chapter 3 - GeneHunter Excel Interface Reference

Chapter 4 - GeneHunter Tutorial for the Excel Interface

chapter 5 - GeneHunter examples in Excel

chapter 6 - programming your genetic algorithm using the DLL

Chapter 7 - Programming Combinatorial Problems with the DLL

chapter 8 - programming reference

chromosomes

	Adjustable Cells (Chromosomes)
	How to Build a Population

clusters example

combinatorial problems

combinatorial problems - programming

constants

	Chapter 9 - Global Constants and Error Messages Details
	Global Constants

constraints

constraints - hard

constraints (soft constraints)

continuous chromosome

continuous chromosomes

continuous chromosomes - length

continuous crossover

crossover

	Continuous Crossover
	crossover

crossover of enumerated chromosomes

crossover rate

- D -

dialog box

display x best solutions

distributing applications

diversity

DLL

- E -

elitist selection

enumerated chromosome

enumerated chromosomes

	Enumerated Chromosomes
	Mutation of Enumerated Chromosomes

enumerated chromosomes - length

error messages

errors

evolution and genetic algorithms

evolution parameters

evolutionary loop

evolutionary operator parameters

evolutionary parameters

example - NYSE pediction

example - scheduler

example - traveling salesman problem

example programs

examples - camping trip

examples - clusters

examples - indicators

examples - MAXI

examples - neural net for Bob's deli

examples - NYSE 2 prediction

examples - optimize

examples - polynomial approximation

examples - portfolio

examples - portfolio optimization

Excel tutorial

Exploring the Tutorial

extinction

- F -

Faster Excel Processing

find x best solutions

FindBest

fitness function

fitness function cell

fitness functions

fitness functions (multiple)

function GA_error

function reference chart

- G -

gene

GeneHunter dialog screen

generation gap

genetic agorithms

genetic algorithm

	Appendix A - Genetic Algorithm Internals and Advanced Topics Details
	genetic algorithm

genetic algorithms

genetic algorithms and optimum search methods

GetChromosome

GetChromosomePool

GetEnumChromosome

GetEnumChromosomeParm

global constants

	Chapter 9 - Global Constants and Error Messages Details
	Global Constants

- H -

how to build a population

how to print the manual

- I -

indicators example

individual

- K -

KillPopulation

- L -

length of continuous chromosomes

length of enumerated chromosomes

- M -

multiple fitness functions

mutation

	Mutation of Continous Chromosomes
	mutation

mutation of continuous chromosomes

mutation of enumerated chromosomes

mutation rate

- N -

neural net example

neural networks

	Chapter 10 - Integrating Genetic Algorithms and Neural Networks Details
	Using Neural Networks as Fitness Functions

NYSE 2 prediction

NYSE prediction example

- O -

optimization problem

optimize example

optimum search methods

options

overview of the GA process

overview of the terminology

- P -

parameters of the evolutionary operators

partially-matched crossover

penalties

polynomial approximation

population

	Set Random Seed
	How to Build a Population
	population

population numbers

population parameters

population size

portfolio example

portfolio optimization example

print the manual

priority parameters

probabilistic versus deterministic methods

Product Support Information

programming

programming combinatorial programs

programming the DLL for combinatorial problems

Programming with GeneHunter

- R -

range (hard constraint)

ranges (hard constraints)

repeating representation

Reproduce

reproduction

reset all button

roulette wheel selection

Running GeneHunter as an Excel Add-In

- S -

Selling or Distributing Applications

setting GeneHunter options

solving optimizatin problems

starting GeneHunter

stop evolution

stopping the algorithm

System Requirements

- T -

technical support

technical support:help

terminology

tolerance and priority parameters

traveling salesman problem example

tutorial

	Chapter 4 - GeneHunter Tutorial for the Excel Interface Details
	Camping Trip Example

- U -

unique gene representation

unique representation

update screen

- W -

What GeneHunter Includes

when should the algorithm be stopped

writing the fitness function