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Ingy döt Net 2023-07-01 11:58:00 -04:00
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Task/Evolutionary-algorithm/MATLAB/evolutionary-algorithm-1.m Normal file
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%This class impliments a string that mutates to a target
classdef EvolutionaryAlgorithm
properties
target;
parent;
children = {};
validAlphabet;
%Constants
numChildrenPerIteration;
maxIterations;
mutationRate;
end
methods
%Class constructor
function family = EvolutionaryAlgorithm(target,mutationRate,numChildren,maxIterations)
family.validAlphabet = char([32 (65:90)]); %Space char and A-Z
family.target = target;
family.children = cell(numChildren,1);
family.numChildrenPerIteration = numChildren;
family.maxIterations = maxIterations;
family.mutationRate = mutationRate;
initialize(family);
end %class constructor
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Helper functions and class get/set functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%setAlphabet() - sets the valid alphabet for the current instance
%of the EvolutionaryAlgorithm class.
function setAlphabet(family,alphabet)
if(ischar(alphabet))
family.validAlphabet = alphabet;
%Makes change permanent
assignin('caller',inputname(1),family);
else
error 'New alphabet must be a string or character array';
end
end
%setTarget() - sets the target for the current instance
%of the EvolutionaryAlgorithm class.
function setTarget(family,target)
if(ischar(target))
family.target = target;
%Makes change permanent
assignin('caller',inputname(1),family);
else
error 'New target must be a string or character array';
end
end
%setMutationRate() - sets the mutation rate for the current instance
%of the EvolutionaryAlgorithm class.
function setMutationRate(family,mutationRate)
if(isnumeric(mutationRate))
family.mutationRate = mutationRate;
%Makes change permanent
assignin('caller',inputname(1),family);
else
error 'New mutation rate must be a double precision number';
end
end
%setMaxIterations() - sets the maximum number of iterations during
%evolution for the current instance of the EvolutionaryAlgorithm class.
function setMaxIterations(family,maxIterations)
if(isnumeric(maxIterations))
family.maxIterations = maxIterations;
%Makes change permanent
assignin('caller',inputname(1),family);
else
error 'New maximum amount of iterations must be a double precision number';
end
end
%display() - overrides the built-in MATLAB display() function, to
%display the important class variables
function display(family)
disp([sprintf('Target: %s\n',family.target)...
sprintf('Parent: %s\n',family.parent)...
sprintf('Valid Alphabet: %s\n',family.validAlphabet)...
sprintf('Number of Children: %d\n',family.numChildrenPerIteration)...
sprintf('Mutation Rate [0,1]: %d\n',family.mutationRate)...
sprintf('Maximum Iterations: %d\n',family.maxIterations)]);
end
%disp() - overrides the built-in MATLAB disp() function, to
%display the important class variables
function disp(family)
display(family);
end
%randAlphabetElement() - Generates a random character from the
%valid alphabet for the current instance of the class.
function elements = randAlphabetElements(family,numChars)
%Sample the valid alphabet randomly from the uniform
%distribution
N = length(family.validAlphabet);
choices = ceil(N*rand(1,numChars));
elements = family.validAlphabet(choices);
end
%initialize() - Sets the parent to a random string of length equal
%to the length of the target
function parent = initialize(family)
family.parent = randAlphabetElements(family,length(family.target));
parent = family.parent;
%Makes changes to the instance of EvolutionaryAlgorithm permanent
assignin('caller',inputname(1),family);
end %initialize
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Functions required by task specification
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%mutate() - generates children from the parent and mutates them
function mutate(family)
sizeParent = length(family.parent);
%Generate mutatant children sequentially
for child = (1:family.numChildrenPerIteration)
parentCopy = family.parent;
for charIndex = (1:sizeParent)
if (rand(1) < family.mutationRate)
parentCopy(charIndex) = randAlphabetElements(family,1);
end
end
family.children{child} = parentCopy;
end
%Makes changes to the instance of EvolutionaryAlgorithm permanent
assignin('caller',inputname(1),family);
end %mutate
%fitness() - Computes the Hamming distance between the target
%string and the string input as the familyMember argument
function theFitness = fitness(family,familyMember)
if not(ischar(familyMember))
error 'The second argument must be a string';
end
theFitness = sum(family.target == familyMember);
end
%evolve() - evolves the family until the target is reached or it
%exceeds the maximum amount of iterations
function [iteration,mostFitFitness] = evolve(family)
iteration = 0;
mostFitFitness = 0;
targetFitness = fitness(family,family.target);
disp(['Target fitness is ' num2str(targetFitness)]);
while (mostFitFitness < targetFitness) && (iteration < family.maxIterations)
iteration = iteration + 1;
mutate(family);
parentFitness = fitness(family,family.parent);
mostFit = family.parent;
mostFitFitness = parentFitness;
for child = (1:family.numChildrenPerIteration)
childFitness = fitness(family,family.children{child});
if childFitness > mostFitFitness
mostFit = family.children{child};
mostFitFitness = childFitness;
end
end
family.parent = mostFit;
disp([num2str(iteration) ': ' mostFit ' - Fitness: ' num2str(mostFitFitness)]);
end
%Makes changes to the instance of EvolutionaryAlgorithm permanent
assignin('caller',inputname(1),family);
end %evolve
end %methods
end %classdef

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Task/Evolutionary-algorithm/MATLAB/evolutionary-algorithm-2.m Normal file
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>> instance = EvolutionaryAlgorithm('METHINKS IT IS LIKE A WEASEL',.08,50,1000)
Target: METHINKS IT IS LIKE A WEASEL
Parent: UVEOCXXFBGDCSFNMJQNWTPJ PCVA
Valid Alphabet: ABCDEFGHIJKLMNOPQRSTUVWXYZ
Number of Children: 50
Mutation Rate [0,1]: 8.000000e-002
Maximum Iterations: 1000
>> evolve(instance);
Target fitness is 28
1: MVEOCXXFBYD SFCMJQNWTPM PCVA - Fitness: 2
2: MEEOCXXFBYD SFCMJQNWTPM PCVA - Fitness: 3
3: MEEHCXXFBYD SFCMJXNWTPM ECVA - Fitness: 4
4: MEEHCXXFBYD SFCMJXNWTPM ECVA - Fitness: 4
5: METHCXAFBYD SFCMJXNWXPMARPVA - Fitness: 5
6: METHCXAFBYDFSFCMJXNWX MARSVA - Fitness: 6
7: METHCXKFBYDFBFCQJXNWX MATSVA - Fitness: 7
8: METHCXKFBYDFBF QJXNWX MATSVA - Fitness: 8
9: METHCXKFBYDFBF QJXNWX MATSVA - Fitness: 8
10: METHCXKFUYDFBF QJXNWX MITSEA - Fitness: 9
20: METHIXKF YTBOF LIKN G MIOSEI - Fitness: 16
30: METHIXKS YTCOF LIKN A MIOSEL - Fitness: 19
40: METHIXKS YTCIF LIKN A MEUSEL - Fitness: 21
50: METHIXKS YT IS LIKE A PEUSEL - Fitness: 24
100: METHIXKS YT IS LIKE A WEASEL - Fitness: 26
150: METHINKS YT IS LIKE A WEASEL - Fitness: 27
195: METHINKS IT IS LIKE A WEASEL - Fitness: 28

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Task/Evolutionary-algorithm/MATLAB/evolutionary-algorithm-3.m Normal file
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%% Genetic Algorithm -- Solves For A User Input String
% #### PLEASE NOTE: you can change the selection and crossover type in the
% parameters and see how the algorithm changes. ####
clear;close all;clc; %Clears variables, closes windows, and clears the command window
tic % Begins the timer
%% Select Target String
target = 'METHINKS IT IS LIKE A WEASEL';
% *Can Be Any String With Any Values and Any Length!*
% but for this example we use 'METHINKS IT IS LIKE A WEASEL'
%% Parameters
popSize = 1000; % Population Size (100-10000 generally produce good results)
genome = length(target); % Genome Size
mutRate = .01; % Mutation Rate (5%-25% produce good results)
S = 4; % Tournament Size (2-6 produce good results)
best = Inf; % Initialize Best (arbitrarily large)
MaxVal = max(double(target)); % Max Integer Value Needed
ideal = double(target); % Convert Target to Integers
selection = 0; % 0: Tournament
% 1: 50% Truncation
crossover = 1; % 0: Uniform crossover
% 1: 1 point crossover
% 2: 2 point crossover
%% Initialize Population
Pop = round(rand(popSize,genome)*(MaxVal-1)+1); % Creates Population With Corrected Genome Length
for Gen = 1:1e6 % A Very Large Number Was Chosen, But Shouldn't Be Needed
%% Fitness
% The fitness function starts by converting the characters into integers and then
% subtracting each element of each member of the population from each element of
% the target string. The function then takes the absolute value of
% the differences and sums each row and stores the function as a mx1 matrix.
F = sum(abs(bsxfun(@minus,Pop,ideal)),2);
% Finding Best Members for Score Keeping and Printing Reasons
[current,currentGenome] = min(F); % current is the minimum value of the fitness array F
% currentGenome is the index of that value in the F array
% Stores New Best Values and Prints New Best Scores
if current < best
best = current;
bestGenome = Pop(currentGenome,:); % Uses that index to find best value
fprintf('Gen: %d | Fitness: %d | ',Gen, best); % Formatted printing of generation and fitness
disp(char(bestGenome)); % Best genome so far
elseif best == 0
break % Stops program when we are done
end
%% Selection
% TOURNAMENT
if selection == 0
T = round(rand(2*popSize,S)*(popSize-1)+1); % Tournaments
[~,idx] = min(F(T),[],2); % Index to Determine Winners
W = T(sub2ind(size(T),(1:2*popSize)',idx)); % Winners
% 50% TRUNCATION
elseif selection == 1
[~,V] = sort(F,'descend'); % Sort Fitness in Ascending Order
V = V(popSize/2+1:end); % Winner Pool
W = V(round(rand(2*popSize,1)*(popSize/2-1)+1))'; % Winners
end
%% Crossover
% UNIFORM CROSSOVER
if crossover == 0
idx = logical(round(rand(size(Pop)))); % Index of Genome from Winner 2
Pop2 = Pop(W(1:2:end),:); % Set Pop2 = Pop Winners 1
P2A = Pop(W(2:2:end),:); % Assemble Pop2 Winners 2
Pop2(idx) = P2A(idx); % Combine Winners 1 and 2
% 1-POINT CROSSOVER
elseif crossover == 1
Pop2 = Pop(W(1:2:end),:); % New Population From Pop 1 Winners
P2A = Pop(W(2:2:end),:); % Assemble the New Population
Ref = ones(popSize,1)*(1:genome); % The Reference Matrix
idx = (round(rand(popSize,1)*(genome-1)+1)*ones(1,genome))>Ref; % Logical Indexing
Pop2(idx) = P2A(idx); % Recombine Both Parts of Winners
% 2-POINT CROSSOVER
elseif crossover == 2
Pop2 = Pop(W(1:2:end),:); % New Pop is Winners of old Pop
P2A = Pop(W(2:2:end),:); % Assemble Pop2 Winners 2
Ref = ones(popSize,1)*(1:genome); % Ones Matrix
CP = sort(round(rand(popSize,2)*(genome-1)+1),2); % Crossover Points
idx = CP(:,1)*ones(1,genome)<Ref&CP(:,2)*ones(1,genome)>Ref; % Index
Pop2(idx)=P2A(idx); % Recombine Winners
end
%% Mutation
idx = rand(size(Pop2))<mutRate; % Index of Mutations
Pop2(idx) = round(rand([1,sum(sum(idx))])*(MaxVal-1)+1); % Mutated Value
%% Reset Poplulations
Pop = Pop2;
end
toc % Ends timer and prints elapsed time

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Task/Evolutionary-algorithm/MATLAB/evolutionary-algorithm-4.m Normal file
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Gen: 1 | Fitness: 465 | C<EFBFBD>I1%G+<%?R<EFBFBD>8>9<EFBFBD>JU#(E<EFBFBD>UO<EFBFBD>PHI
Gen: 2 | Fitness: 429 | W=P6>D<EFBFBD>I)VU6$T 99,<EFBFBD> B<EFBFBD>BMP0JH
Gen: 3 | Fitness: 366 | P<EFBFBD>;R08AS<EFBFBD>GJ<EFBFBD>IS&T38IE<EFBFBD>)SJERLJ
Gen: 4 | Fitness: 322 | KI8M5LAS<EFBFBD>GJ<EFBFBD>IS<EFBFBD>SP<EFBFBD>@)D<EFBFBD>V@
JCP
Gen: 5 | Fitness: 295 | UAUR08AS<EFBFBD>GJ<EFBFBD>IS<EFBFBD>8HG*<EFBFBD>+<EFBFBD>=C?UB(
Gen: 6 | Fitness: 259 | VCUQH35S<EFBFBD>HR4.L<EFBFBD>ISJQ%J<EFBFBD>OC*T=E
Gen: 7 | Fitness: 226 | LFB8GPET(LODKQ<EFBFBD>KQ<K E*PEMA6I
Gen: 8 | Fitness: 192 | EPKOLCIR<EFBFBD>QQ<EFBFBD>NF<EFBFBD>QG:B(D/U>BQGF
Gen: 9 | Fitness: 159 | N8R7?SOU<EFBFBD>NO$OK O?K?!;<EFBFBD>MB?QHG
Gen: 10 | Fitness: 146 | TGN@EQR4)PS%IS#TFJQ%A!U>BVLI
Gen: 11 | Fitness: 120 | L?VMALJS%?R EK IILE<EFBFBD>6'RRERLJ
Gen: 12 | Fitness: 102 | R@T9COMR<EFBFBD>NU CS*R?K?!; VD>LCL
Gen: 13 | Fitness: 96 | NENMVOMR<EFBFBD>NU CS*R?K?!; VD>LCL
Gen: 14 | Fitness: 82 | REJGNPMU<EFBFBD>KR CS JKI@+D<EFBFBD>UD?QHG
Gen: 15 | Fitness: 75 | NETI=HPQ<EFBFBD>FT ID EFKE D"WD>QDQ
Gen: 16 | Fitness: 70 | R@TKCOOT)@R$IS KKLE<EFBFBD>D"WC?UBJ
Gen: 17 | Fitness: 61 | NESIKQRP<EFBFBD>NU CS<EFBFBD>MFKE ; SEETCP
Gen: 18 | Fitness: 57 | LFSGLPTN<EFBFBD>NU GQ IIKE D"VD>LCL
Gen: 19 | Fitness: 40 | NENKJLMS<EFBFBD>GS%IS#MFKE B UFATCL
Gen: 21 | Fitness: 39 | NETIGPEU<EFBFBD>KR IS IIKD"? UFDQEK
Gen: 22 | Fitness: 33 | NETGCOMT<EFBFBD>LU IS#MFKE B UFATCL
Gen: 23 | Fitness: 32 | NETIKNPQ<EFBFBD>NU IS#IIKE B UFATCL
Gen: 24 | Fitness: 27 | NETKJLMS<EFBFBD>LU IS MFKE B UFATCL
Gen: 25 | Fitness: 23 | LETIKOMS LU IS IIKE D WEDQEK
Gen: 26 | Fitness: 22 | NETIKMJS LU IS IIKE D WEDQEK
Gen: 27 | Fitness: 20 | LETIKOMS LU IS KILE B"WFATCL
Gen: 28 | Fitness: 19 | NESGJQJS<EFBFBD>GU IS KIKE B WFATEK
Gen: 29 | Fitness: 16 | NETIHPMS KR IS KIKE B WFATEK
Gen: 30 | Fitness: 15 | NESHLPKS KU IS KIKE B WFATEK
Gen: 31 | Fitness: 13 | NETGGNKS KU IS KIKE C WFATEK
Gen: 32 | Fitness: 12 | NETHGNJS IU IS JIKE B WFATCL
Gen: 33 | Fitness: 11 | NETIJPKS IU IS KIKE B WFATEK
Gen: 35 | Fitness: 8 | LEUIHNJS IT IS JIKE A WEATEL
Gen: 37 | Fitness: 7 | NETIHNJS IS IS LIKE B WFASEL
Gen: 38 | Fitness: 6 | NETHGNJS IT IS LIKE A WFASEK
Gen: 39 | Fitness: 4 | METGHNKS IT IS LIKE B WEATEL
Gen: 42 | Fitness: 3 | NETHINKS IT IS KIKE B WEASEL
Gen: 43 | Fitness: 2 | NETHINKS IT IS LIKE A WFASEL
Gen: 44 | Fitness: 1 | METHHNKS IT IS LIKE A WEASEL
Gen: 46 | Fitness: 0 | METHINKS IT IS LIKE A WEASEL
Elapsed time is 0.099618 seconds.