Geneticalgorithmsandgeneticprogrammingarevery good at finding solutions to very
largeproblems.Theydoitbytakingmillionsofsamplesfromthesearchspace,making
smallchanges,possiblyrecombiningparts ofthe bestsolutions, comparingtheresultant
fitness against that of the current best solution, and keeping the better of the two. This
processrepeatsuntilastopconditionlikeoneofthefollowingoccurs:theknownsolution
isfound,asolutionmeetingallrequirementsisfound,acertainnumberofgenerationshas
passed,aspecificamountoftimehaspassed,etc.
Firstproject
Imagine you are asked to guess a 3-letter password; what kind of feedback would you
want? If the password is ‘aaa‘ and you guess ‘abc‘ what should the fitness value be?
Would something simple like how many of the letters in your guess are correct be
sufficient?Should‘bab‘,whichhasonecorrectletter,getabetterfitnessvaluethan‘zap‘,
alsoonecorrectletterbutthewronglettersarealphabeticallyfartheraway,orshouldthe
fitness be the same? These are some of the first decisions you have to make when
planning to implement a genetic algorithm to find a solution to your problem. Genetic
algorithms are good at finding good solutions to problems with large search spaces
becausetheycanquicklyfindthepartsoftheguessesthatimprovefitnessvaluesorlead
tobettersolutions.
Intheprojectabove,let’ssaythefitnessfunctionreturnsacountofthenumberofletters
thatmatchthepassword.Thatmeans‘abc‘,‘bab‘and‘zba‘allgetafitnessvalueofone,
becausetheyeachhaveonelettercorrect.Thegeneticalgorithmmightthencombinethe
firsttwolettersof‘abc‘withthelastletterof‘zba‘throughcrossover,tocreatetheguess
‘aba‘.Thefitnessfor that guess would be two because two letters matchthepassword.
Thealgorithmmightalsomutatethelastletterof‘zba‘toget‘zbc‘andafitnessvalueof
zero.Dependingonhowtheengineisconfigureditmightthrowout‘zbc‘immediately,it
mightkeepitinordertomaintaingeneticdiversity,orperhapsitwouldonlykeepitifitis
betterthansomecutofffitnessvaluewhencomparedwithalltheotherguessestriedbythe
engine.
We will look more at the password project in the first chapter, and go on to explore a
variety of projects to learn different ways of solving problems with genetic algorithms.
However,thisbookisnotaboutshowingyouahand-pickedsetofproblemsyoucansolve
withgeneticalgorithms.Itisaboutgivingyouexperiencemakinggeneticalgorithmswork
foryouusingsampleprojectsthatyouunderstandandcanfallbackuponwhenlearningto
useothermachinelearningtools andtechniques, orapplying geneticalgorithmsin your
ownfieldofexpertise.
GeneticprogrammingwithPython
This book uses the Python programming language to explore genetic algorithms. Why
Python?BecausePython is a low ceremony, powerfulandeasy-to-readlanguage whose
code can be understood by entry-level programmers. I explain the occasional Python
featurebutshouldyouencounteraprogrammingconstructyou’veneverseenbeforeand
can’t intuit, Python.organd StackOverflow.com are great places to find explanations. If
you have experience with another programming language then you should have no
difficultylearningPythonbyinductionwhilealsoexploringgeneticalgorithms.
