Initial data commit

Task/Evolutionary-algorithm/MATLAB/evolutionary-algorithm-1.m

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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