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<!DOCTYPE html>
<html lang="en" xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8" />
<title>Genetic Algorithm Homework</title>
<script type='text/javascript' src='scripts/linq.min.js'></script>
<script type='text/javascript' src='scripts/knockout-3.2.0.js'></script>
<script src="scripts/jquery.min.js"></script>
<script src="scripts/highcharts.js"></script>
<script src="scripts/highcharts-3d.js"></script>
<script src="scripts/exporting.js"></script>
<style>
.chromosome {
width: 49%;
display: inline-block;
margin: 5px 0.25%;
font-size: smaller;
z-index: 900;
}
.chromosome .index {
background-color: #323537;
color: #EEE;
width: 1.5em;
text-align: center;
padding: 1px;
border-radius: 0.25em;
height: 100%;
display: inline-block;
z-index: 900;
}
.chromosome .gene-sequence {
margin: 0px 2px 0px 2px;
display: inline-block;
}
.chromosome .gene-sequence.viable {
border-left: 4px solid green;
}
.chromosome .gene-sequence.non-viable {
border-left: 4px solid red;
}
.chromosome .gene-sequence.undecided {
border-left: 4px solid yellow;
}
.chromosome .gene-sequence .gene {
display: inline-block;
background-color: #DDD;
width: 0.95em;
padding: 2px;
margin: 0px -2px;
text-align: center;
}
.chromosome .gene-sequence .gene .value {
font-weight: bold;
}
.chromosome .gene-sequence .gene.correct {
border-bottom: 4px solid #0c6918;
}
.chromosome .gene-sequence .gene.wrong {
border-bottom: 4px solid #ff0000;
opacity: 0.3;
}
.chromosome .gene-sequence .gene.cross-over-point {
border-left: 4px solid #000;
opacity: 0.8;
}
.chromosome .fitness {
display: inline-block;
background-color: #9db5c4;
color: #2b2828;
width: 3em;
padding: 1px;
border-radius: 0.25em;
height: 100%;
text-align: center;
z-index: 900;
}
</style>
<style>
html, body {
margin: 0px;
height: 100%;
width: 100%;
overflow-x: hidden;
overflow-y: auto;
}
#config-panel {
width: 100%;
margin: 4px;
padding: 8px;
}
#config-panel table {
width: 100%;
}
#config-panel thead tr td {
text-align: center;
font-weight: bold;
border-bottom: 2px solid #333;
background-color: #4197d7;
color: #fff;
}
#config-panel table tbody {
background-color: #eee;
}
#config-panel table tr td {
padding: 4px 6px;
}
#config-panel .config-row .config-label, #config-panel .config-row .config-field {
display: inline-block;
margin: 0px 4px;
}
#config-panel table tbody tr td.info {
font-size: small;
font-style: italic;
}
#config-panel table tbody tr.inactive {
background-color: #aaa;
display: none;
}
#config-panel table tbody tr.active {
background-color: #ddd;
display: table-row;
}
#config-panel table tr.last-row td {
border-top: 2px solid #333;
text-align: center;
padding: 8px 12px;
}
.splitter {
width: 49%;
margin: 0px 0.25%;
padding: 0px;
display: inline-block;
height: 100%;
float: left;
}
.panel {
margin: 4px;
padding: 8px;
width: 100%;
}
.panel .title {
width: 100%;
text-align: center;
font-weight: bold;
border-bottom: 2px solid #333;
background-color: #4197d7;
color: #fff;
padding: 4px 0px;
}
.info-area, .info-item {
display: inline-block;
}
.info-area {
width: 100%;
background-color: #eee;
}
.info-item {
text-align: center;
margin: 6px 3px;
border: 1px solid #666;
border-radius: 6px;
float: right;
}
.info-item.medium {
width: 7em;
}
.info-item.long {
width: 10em;
}
.info-label {
padding: 4px;
font-size: 0.70em;
font-style: italic;
letter-spacing: 2px;
font-weight: 100;
background-color: #333;
color: #eee;
}
.info-value {
font-size: 1.25em;
border-bottom: 1px solid #333;
padding: 2px;
background-color: #5b5656;
color: #FFF;
}
.blink-box {
width: 0.5em;
height: 0.80em;
background-color: #808080;
border-radius: 0.5em;
display: inline-block;
}
.blink-box.active {
background-color: #4cff00;
opacity: 1;
}
.blink-box.inactive {
background-color: #ffd800;
opacity: 0.25;
}
</style>
<style>
#graphs-qos-container, #graph-qos {
}
#graphs-qos-container, #graphs-map-container {
width: 100%;
height: 44%;
}
#graph-qos, #graph-map {
width: 98%;
height: 90%;
padding: 8px;
background-color: #333;
}
.maximize, .restore {
display: inline-block;
float: right;
margin: 0em 1em;
cursor: pointer;
}
</style>
</head>
<body>
<div class="splitter left">
<div id="config-panel">
<table>
<thead>
<tr>
<td colspan="4">Genetic Algorithm Configuration Panel</td>
</tr>
</thead>
<tbody data-bind="visible: !Locked() ">
<tr>
<td colspan="2">Target String</td>
<td><input type="text" data-bind="value: TargetString" /></td>
<td class="info">The final string that the algorithm is searching for</td>
</tr>
<tr>
<td colspan="2">Fitness function</td>
<td>
<select data-bind="value: FitnessType">
<option value="1">Squared distance</option>
<option value="2">Binary distance</option>
</select>
</td>
<td class="info">The fitness function to be used for ranking the chromosomes</td>
</tr>
<tr>
<td colspan="2">Has mutations?</td>
<td>
<input type="checkbox" data-bind="checked: HasMutations" />
</td>
<td class="info">Check if mutations are allowed</td>
</tr>
<tr data-bind="css: (HasMutations() ? 'active': 'inactive')">
<td></td>
<td>Mutation rate</td>
<td>
<input type="number" data-bind="value: MutationRate" />
</td>
<td class="info">The mutation rate used. {0, 1}</td>
</tr>
<tr>
<td colspan="2">Cross-over type</td>
<td>
<select data-bind="value: CrossoverType">
<option value="1">Fixed point cross-over</option>
<option value="2">Range cross-over</option>
</select>
</td>
<td class="info">The cross over type to be used while mating</td>
</tr>
<tr data-bind="css: (CrossoverType() == 1 ? 'active': 'inactive')">
<td></td>
<td>Cross-over point</td>
<td>
<select data-bind="options: AvailableCrossoverPoints,
value: CrossoverPoint"></select>
</td>
<td class="info">The fixed point at which cross-over occurs</td>
</tr>
<tr data-bind="css: (CrossoverType() == 2 ? 'active': 'inactive')">
<td></td>
<td>Cross-over point range</td>
<td>
<span>Between</span>
<select data-bind="options: AvailableCrossoverStartPoints, value: CrossoverRangeStart"></select>
<span>and</span>
<select data-bind="options: AvailableCrossoverEndPoints, value: CrossoverRangeEnd"></select>
</td>
<td class="info">The range of points at which cross-over can occur</td>
</tr>
<tr>
<td colspan="2">Starting population size</td>
<td><input type="number" data-bind="value: StartingPopulationSize" /></td>
<td class="info">The number of chromosomes in the starting population</td>
</tr>
<tr>
<td colspan="2">Running population size</td>
<td><input type="number" data-bind="value: RunningPopulationSize" /></td>
<td class="info">The number of chromosomes in the running population</td>
</tr>
<tr>
<td colspan="2">Limit by generation?</td>
<td>
<input type="checkbox" data-bind="checked: LimitedByGenerations" />
</td>
<td class="info">Should the algorithm terminate after a certain number of generations</td>
</tr>
<tr data-bind="css: (LimitedByGenerations() ? 'active': 'inactive')">
<td></td>
<td>Generation limit</td>
<td><input type="number" data-bind="value: GenerationLimit" /></td>
<td class="info">The maximum number of generations before termination</td>
</tr>
</tbody>
<tr>
<td colspan="3">
<span>Target string - </span><span style="font-weight:bold" data-bind="text: TargetString"></span>
</td>
<td style="text-align: right" data-bind="visible: Locked()">
<button data-bind="click: Release"> Release</button>
</td>
</tr>
<tr class="last-row" data-bind="visible: !Locked()">
<td colspan="4">
<button data-bind="click: Start">Start</button>
<button>Reset</button>
<button>Clear</button>
</td>
</tr>
</table>
</div>
<div class="panel" id="solution-quality-panel">
<div class="title">Visualization: Quality of Solutions</div>
<div class="info-area">
<div class="info-item long">
<div class="info-value">
<span class="blink-box" data-bind="css: (CurrentStatus() == 0 ? 'active': 'inactive')"></span>
<span class="blink-box" data-bind="css: (CurrentStatus() == 1 ? 'active': 'inactive')"></span>
<span class="blink-box" data-bind="css: (CurrentStatus() == 2 ? 'active': 'inactive')"></span>
<span class="blink-box" data-bind="css: (CurrentStatus() == 3 ? 'active': 'inactive')"></span>
<span class="blink-box" data-bind="css: (CurrentStatus() == 4 ? 'active': 'inactive')"></span>
<span class="blink-box" data-bind="css: (CurrentStatus() == 5 ? 'active': 'inactive')"></span>
<span class="blink-box" data-bind="css: (CurrentStatus() == 6 ? 'active': 'inactive')"></span>
<span class="blink-box" data-bind="css: (CurrentStatus() == 7 ? 'active': 'inactive')"></span>
<span class="blink-box" data-bind="css: (CurrentStatus() == 8 ? 'active': 'inactive')"></span>
<span class="blink-box" data-bind="css: (CurrentStatus() == 9 ? 'active': 'inactive')"></span>
</div>
<div class="info-label" data-bind="text: CurrentStatusText">Current Activity</div>
</div>
<div class="info-item">
<div class="info-value" data-bind="text: MutationsCount"></div>
<div class="info-label">Mutations</div>
</div>
<div class="info-item medium">
<div class="info-value " data-bind="text: MeanFitness"></div>
<div class="info-label">Mean fitness</div>
</div>
<div class="info-item medium">
<div class="info-value" data-bind="text: WorstFitness"></div>
<div class="info-label">Worst fitness</div>
</div>
<div class="info-item medium">
<div class="info-value" data-bind="text: BestFitness"></div>
<div class="info-label">Best fitness</div>
</div>
<div class="info-item">
<div class="info-value" data-bind="text: CurrentGeneration"></div>
<div class="info-label">Generation</div>
</div>
</div>
</div>
<div class="panel" id="algorithm-operations-panel">
<div class="title">Visualization: Chromosomes [Algorithm Operations]</div>
<div>
<!-- ko foreach: Population -->
<div class="chromosome">
<div class="index" data-bind="text: $index() + 1"></div>
<div class="gene-sequence" data-bind="css: (ViabilityStatus() ==0 ? 'undecided' : (ViabilityStatus() == -1? 'non-viable' : 'viable'))">
<!-- ko foreach: Genes -->
<div class="gene" data-bind="css: { 'correct' : IsCorrect() , 'wrong': !IsCorrect(), 'cross-over-point': IsCrossoverPoint()}">
<span class="value" data-bind="text: String.fromCharCode(Value())"></span>
</div>
<!-- /ko -->
<div class="fitness" data-bind="text: Fitness"></div>
</div>
</div>
<!-- /ko -->
</div>
</div>
</div>
<div class="splitter right">
<div class="panel" id="graphs-qos-container">
<div class="title">
Visualization: Quality of Solutions Graphs
<!--<div class="maximize">Maximize</div>
<div class="restore">Restore</div>-->
</div>
<div id="graph-qos">
</div>
</div>
<div class="panel" id="graphs-map-container">
<div class="title">Visualization: 3D Fitness Graph</div>
<div id="graph-map">
</div>
</div>
</div>
<script defer>
</script>
<script>
var testSample1 = [[83, 69, 103, 87, 106, 71, 81, 108, 89, 76, 71, 92, 115, 77, 109, 120, 99], [85, 105, 96, 109, 81, 86, 119, 120, 79, 118, 67, 89, 92, 105, 99, 89, 98],
[117, 88, 96, 76, 71, 103, 68, 92, 112, 94, 115, 84, 89, 88, 114, 115, 93], [75, 120, 94, 116, 97, 84, 70, 68, 86, 79, 107, 74, 110, 76, 122, 92, 109],
[75, 122, 66, 80, 110, 86, 114, 78, 100, 79, 106, 105, 70, 68, 75, 83, 71], [110, 97, 121, 106, 106, 108, 105, 86, 115, 121, 122, 90, 76, 109, 99, 105, 98],
[81, 96, 83, 107, 107, 79, 110, 65, 114, 106, 106, 112, 113, 112, 87, 111, 117], [102, 73, 92, 122, 114, 98, 110, 101, 108, 88, 118, 66, 75, 106, 112, 75, 84],
[80, 111, 111, 80, 89, 79, 121, 112, 105, 88, 101, 82, 117, 82, 67, 91, 65], [70, 78, 87, 108, 119, 84, 73, 106, 112, 110, 89, 112, 92, 68, 115, 101, 113],
[82, 83, 117, 117, 118, 79, 116, 73, 79, 66, 119, 110, 110, 67, 87, 116, 122], [109, 73, 100, 119, 120, 95, 75, 119, 95, 79, 81, 89, 78, 99, 84, 95, 82],
[81, 87, 67, 115, 88, 74, 96, 81, 80, 120, 122, 107, 119, 83, 121, 90, 119], [80, 85, 94, 106, 107, 67, 95, 88, 68, 96, 118, 92, 76, 65, 110, 117, 92],
[96, 68, 116, 75, 69, 88, 99, 72, 82, 108, 118, 79, 84, 106, 110, 117, 70], [102, 76, 112, 105, 83, 75, 81, 93, 81, 69, 82, 99, 106, 72, 84, 76, 100],
[93, 87, 122, 87, 97, 93, 74, 114, 99, 94, 95, 68, 74, 108, 117, 67, 80], [88, 114, 102, 114, 98, 81, 114, 80, 120, 89, 81, 83, 99, 79, 74, 116, 71],
[110, 109, 113, 99, 92, 98, 105, 68, 86, 87, 79, 72, 78, 88, 78, 105, 83], [89, 109, 71, 73, 91, 88, 96, 79, 119, 120, 107, 98, 74, 91, 99, 97, 71],
[100, 114, 100, 76, 87, 67, 95, 101, 110, 89, 113, 120, 87, 92, 115, 90, 113], [71, 77, 85, 67, 115, 105, 114, 95, 80, 107, 95, 119, 71, 86, 102, 70, 78],
[107, 113, 75, 101, 107, 121, 105, 107, 100, 66, 79, 80, 103, 67, 92, 106, 115], [112, 66, 103, 94, 108, 74, 91, 79, 97, 73, 80, 122, 87, 71, 119, 75, 119],
[65, 99, 69, 81, 99, 68, 121, 84, 95, 71, 103, 98, 84, 119, 118, 99, 70], [95, 80, 114, 71, 102, 74, 66, 87, 103, 74, 84, 108, 105, 96, 103, 102, 100],
[98, 109, 92, 117, 88, 88, 66, 122, 116, 114, 94, 76, 65, 96, 116, 78, 120], [69, 122, 105, 77, 114, 80, 98, 86, 121, 108, 76, 115, 105, 110, 118, 77, 105],
[84, 90, 65, 73, 92, 84, 78, 77, 72, 109, 94, 110, 119, 65, 94, 107, 98], [71, 101, 100, 112, 107, 81, 94, 104, 70, 83, 114, 112, 84, 121, 73, 104, 94],
[93, 71, 122, 98, 83, 103, 105, 98, 107, 115, 85, 72, 88, 93, 107, 88, 107], [94, 121, 115, 75, 84, 120, 78, 106, 87, 84, 73, 83, 114, 90, 122, 67, 111]];
var testSample2 = [[83, 69, 97, 110, 100, 72, 101, 117, 114, 105, 115, 116, 105, 99, 95, 65, 73], [85, 105, 96, 109, 81, 86, 119, 120, 79, 118, 67, 89, 92, 105, 99, 89, 98],
[117, 88, 96, 76, 71, 103, 68, 92, 112, 94, 115, 84, 89, 88, 114, 115, 93], [75, 120, 94, 116, 97, 84, 70, 68, 86, 79, 107, 74, 110, 76, 122, 92, 109],
[75, 122, 66, 80, 110, 86, 114, 78, 100, 79, 106, 105, 70, 68, 75, 83, 71], [110, 97, 121, 106, 106, 108, 105, 86, 115, 121, 122, 90, 76, 109, 99, 105, 98],
[81, 96, 83, 107, 107, 79, 110, 65, 114, 106, 106, 112, 113, 112, 87, 111, 117], [102, 73, 92, 122, 114, 98, 110, 101, 108, 88, 118, 66, 75, 106, 112, 75, 84],
[80, 111, 111, 80, 89, 79, 121, 112, 105, 88, 101, 82, 117, 82, 67, 91, 65], [70, 78, 87, 108, 119, 84, 73, 106, 112, 110, 89, 112, 92, 68, 115, 101, 113],
[82, 83, 117, 117, 118, 79, 116, 73, 79, 66, 119, 110, 110, 67, 87, 116, 122], [109, 73, 100, 119, 120, 95, 75, 119, 95, 79, 81, 89, 78, 99, 84, 95, 82],
[81, 87, 67, 115, 88, 74, 96, 81, 80, 120, 122, 107, 119, 83, 121, 90, 119], [80, 85, 94, 106, 107, 67, 95, 88, 68, 96, 118, 92, 76, 65, 110, 117, 92],
[96, 68, 116, 75, 69, 88, 99, 72, 82, 108, 118, 79, 84, 106, 110, 117, 70], [102, 76, 112, 105, 83, 75, 81, 93, 81, 69, 82, 99, 106, 72, 84, 76, 100],
[93, 87, 122, 87, 97, 93, 74, 114, 99, 94, 95, 68, 74, 108, 117, 67, 80], [88, 114, 102, 114, 98, 81, 114, 80, 120, 89, 81, 83, 99, 79, 74, 116, 71],
[110, 109, 113, 99, 92, 98, 105, 68, 86, 87, 79, 72, 78, 88, 78, 105, 83], [89, 109, 71, 73, 91, 88, 96, 79, 119, 120, 107, 98, 74, 91, 99, 97, 71],
[100, 114, 100, 76, 87, 67, 95, 101, 110, 89, 113, 120, 87, 92, 115, 90, 113], [71, 77, 85, 67, 115, 105, 114, 95, 80, 107, 95, 119, 71, 86, 102, 70, 78],
[107, 113, 75, 101, 107, 121, 105, 107, 100, 66, 79, 80, 103, 67, 92, 106, 115], [112, 66, 103, 94, 108, 74, 91, 79, 97, 73, 80, 122, 87, 71, 119, 75, 119],
[65, 99, 69, 81, 99, 68, 121, 84, 95, 71, 103, 98, 84, 119, 118, 99, 70], [95, 80, 114, 71, 102, 74, 66, 87, 103, 74, 84, 108, 105, 96, 103, 102, 100],
[98, 109, 92, 117, 88, 88, 66, 122, 116, 114, 94, 76, 65, 96, 116, 78, 120], [69, 122, 105, 77, 114, 80, 98, 86, 121, 108, 76, 115, 105, 110, 118, 77, 105],
[84, 90, 65, 73, 92, 84, 78, 77, 72, 109, 94, 110, 119, 65, 94, 107, 98], [71, 101, 100, 112, 107, 81, 94, 104, 70, 83, 114, 112, 84, 121, 73, 104, 94],
[93, 71, 122, 98, 83, 103, 105, 98, 107, 115, 85, 72, 88, 93, 107, 88, 107], [94, 121, 115, 75, 84, 120, 78, 106, 87, 84, 73, 83, 114, 90, 122, 67, 111]];
</script>
<script defer>
function ConfigurationViewModel() {
var self = this;
this.Locked = ko.observable(false);
this.TargetString = ko.observable("SomeString");
this.TargetAlleles = null;
this.NumberOfGenesPerChromosome = 0;
this.FitnessType = ko.observable(1);
this.HasMutations = ko.observable(false);
this.MutationRate = ko.observable(0.1);
this.CrossoverType = ko.observable(1);
this.AvailableCrossoverPoints = ko.observableArray();
this.CrossoverPoint = ko.observable(0);
this.AvailableCrossoverStartPoints = ko.observableArray();
this.AvailableCrossoverEndPoints = ko.observableArray();
this.CrossoverRangeStart = ko.observable();
this.CrossoverRangeEnd = ko.observable();
this.MidRangeCrossover = function (range) {
var rangeHalf = Math.floor(range / 2);
var startFrom = Math.ceil(self.NumberOfGenesPerChromosome / 2);
console.log(rangeHalf, range);
self.CrossoverRangeStart(startFrom - rangeHalf);
self.CrossoverRangeEnd(self.CrossoverRangeStart() + range);
}
this.CrossoverFn = null;
self.CrossoverType.subscribe(function (s) {
// set the cross over fn accordingly
if (s == 1) {
self.CrossoverFn = function () {
return self.CrossoverPoint();
};
} else {
var possibleValues = Enumerable.Range(self.CrossoverRangeStart(), self.CrossoverRangeEnd() - self.CrossoverRangeStart()).ToArray();
self.CrossoverFn = function () {
var i = Math.floor(Math.random() * possibleValues.length);
return possibleValues[i];
};
}
});
this.StartingPopulationSize = ko.observable(0);
this.RunningPopulationSize = ko.observable(0);
this.GenerationSelectionSize = 0;
this.LimitedByGenerations = ko.observable(false);
this.GenerationLimit = ko.observable(10000);
this.Alleles = null;
var onStartCallback = null;
this.Attach = function (startCallback) {
onStartCallback = startCallback;
}
this.Start = function () {
self.Locked(true);
if (onStartCallback != null) onStartCallback();
}
this.Release = function () {
self.Locked(false);
}
this.Alleles = Enumerable.Range(65, (122 - 65 + 1)).ToArray(); // alleles are currently fixed
function recalculate() {
// calcuate the target alleles
self.TargetAlleles = Enumerable.From(self.TargetString()).Select(function (s) {
return s.charCodeAt();
}).ToArray();
// From the target string -> Length of each chromosome (number of genes)
self.NumberOfGenesPerChromosome = self.TargetString().length;
// -> single point crossover points
self.AvailableCrossoverPoints.splice(0); // clear the array
self.AvailableCrossoverStartPoints.splice(0); // clear the array
self.AvailableCrossoverEndPoints.splice(0); // clear the array
// each available point from 1 to length of target -1
for (var i = 1; i < self.NumberOfGenesPerChromosome; i++) {
self.AvailableCrossoverPoints.push(i);
// if range, then the start can be any but the last
self.AvailableCrossoverStartPoints.push(i);
self.AvailableCrossoverEndPoints.push(i);
}
// removing the last from the start points
self.AvailableCrossoverStartPoints.pop();
}
this.TotalAlleles = ko.computed(function () {
return self.NumberOfGenesPerChromosome * self.RunningPopulationSize();
});
this.RequiredMutations = ko.computed(function () {
return Math.round(self.MutationRate() * self.TotalAlleles());
});
// subscribe to the changes
self.RunningPopulationSize.subscribe(function (s) {
self.GenerationSelectionSize = Math.ceil(s / 2);
});
self.TargetString.subscribe(recalculate);
var lastEndPoint = 0;
self.CrossoverRangeStart.subscribe(function (s) {
lastEndPoint = self.CrossoverRangeEnd();
// start from the start + 1 to length
self.AvailableCrossoverEndPoints.splice(0); // clear the array
for (var i = (s + 1) ; i < self.NumberOfGenesPerChromosome; i++) {
self.AvailableCrossoverEndPoints.push(i);
}
// set it back to the last end point if possible
if (lastEndPoint >= (s + 1) && lastEndPoint < self.NumberOfGenesPerChromosome) {
self.CrossoverRangeEnd(lastEndPoint);
}
});
}
function GeneViewModel(value, trueValue) {
var self = this;
this.TrueValue = trueValue;
this.Value = ko.observable(value);
this.IsCrossoverPoint = ko.observable(false);
this.IsCorrect = ko.observable(false);
// constructor check
checkCorrectness();
self.Value.subscribe(function (s) {
checkCorrectness();
});
this.Clone = function () {
var n = new GeneViewModel(self.Value(), self.TrueValue);
return n;
}
function checkCorrectness() {
self.IsCorrect(self.Value() == self.TrueValue);
}
}
function ChromosomeViewModel(config) {
var self = this;
var C = config;
var fitnessFunction = (C.FitnessType() == 1 ? squaredFitnessFunction : binaryFitnessFunction);
var geneLength = config.TargetString().length;
this.Genes = ko.observableArray();
this.Fitness = ko.observable(0);
this.ViabilityStatus = ko.observable(0); // 0 - not computed, 1 - Viable, -1 - Killed soon
this.CrossoverPosition = ko.observable();
this.IsParentStart = ko.observable(true);
// functions
this.CalculateFitness = function () {
self.Fitness(fitnessFunction());
}
self.CrossoverPosition.subscribe(function (s) {
// set the value for the appropriate gene
self.Genes()[s].IsCrossoverPoint(true);
});
self.ResetCrossoverPoint = function () {
self.Genes()[self.CrossoverPosition()].IsCrossoverPoint(false);
}
this.FromSample = function (sample, index) {
self.Genes.splice(0);
var currentSampleChromosome = sample[index];
for (var i = 0; i < C.NumberOfGenesPerChromosome; i++) {
self.Genes.push(new GeneViewModel(currentSampleChromosome[i], C.TargetAlleles[i]));
}
}
this.GetRandomAllele = function () {
var rnd = Math.floor(Math.random() * C.Alleles.length);
return C.Alleles[rnd];
}
this.FillRandomGenes = function () {
self.Genes.splice(0);
for (var i = 0; i < C.NumberOfGenesPerChromosome; i++) {
// pick a random allele
var ng = new GeneViewModel(self.GetRandomAllele(), C.TargetAlleles[i]);
self.Genes.push(ng);
}
}
this.Left = new Array();
this.Right = new Array();
this.Split = function () { // split at the cross-over point
self.Left.splice(0); self.Right.splice(0); // clear the splices
for (var i = 0; i < self.CrossoverPosition() ; i++) {
self.Left.push(self.Genes()[i]);
}
for (var i = self.CrossoverPosition() ; i < self.Genes().length ; i++) {
self.Right.push(self.Genes()[i]);
}
}
// hidden fitness functions
function squaredFitnessFunction() {
var fitness = Enumerable.From(self.Genes()).Select(function (s, index) { return Math.pow((s.Value() - C.TargetAlleles[index]), 2); }).Sum();
return fitness;
}
function binaryFitnessFunction() {
var fitness = Enumerable.From(self.Genes()).Select(function (s, index) { return s.Value() == C.TargetAlleles[index] ? 0 : 1 }).Sum();
return fitness;
}
}
function StatisticsViewModel() {
var self = this;
this.CurrentGeneration = ko.observable(0);
this.BestFitness = ko.observable(0);
this.WorstFitness = ko.observable(0);
this.MeanFitness = ko.observable(0);
this.MutationsCount = ko.observable(0);
// 0 - not started, 1 - Initializing, 2-Calculating fitness, 3-Ordering by fitness, 4-Selecting parents,5-Pruning population, 6-Crossing, 7-Mutating, 8-test for termination, 9-done
this.CurrentStatus = ko.observable(0);
var statusArray = ["Not started", "Creating starting population", "Calculating fitness",
"Ordering by fitness", "Testing for termination", "Selecting parents", "Killing non-viables", "Crossing/Mating", "Mutating", "Completed"];
this.CurrentStatusText = ko.computed(function () {
return statusArray[self.CurrentStatus()];
});
}
function Sequencer(startingWith) {
var self = this;
var startWith = startingWith;
var interval = 0;
var doneCallback = null;
this.Begin = function () {
// execute the start
setTimeout(executeNext, interval, startingWith);
}
function executeNext(current) {
var next = current();
if (next != null) {
setTimeout(executeNext, interval, next);
} else {
self.Done();
}
}
this.Done = function () {
console.log('Execution done..');
}
}
function EngineViewModel(config) {
var self = this;
var C = config;
this.Population = ko.observableArray();
var stat = new StatisticsViewModel();
this.Statistics = ko.observable(stat);
this.IsProcessing = ko.observable(false);
this.IsRunning = ko.observable(false);
this.IsReady = ko.observable(false);
var qChart;
var mChart;
this.IsQGraphMaximized = ko.observable(false);
this.IsMGraphMaximed = ko.observable(false);
this.MaximizeQ = function () {
self.IsQGraphMaximized(true);
}
this.RestoreQ = function () {
self.IsQGraphMaximized(false);
}
this.MaximizeM = function () {
self.IsQGraphMaximized(true);
}
this.RestoreM = function () {
self.IsQGraphMaximized(false);
}
var sequencer = null;
var generationCounter = 0;
var autoFocusOn = 20;
var lastExtremes = { min: 0, max: autoFocusOn };
var overallMaxFitness = -1;
this.Initialize = function () { // initializes and begins the sequencer
console.log("Initializing..");
sequencer = new Sequencer(self.GenerateStartingPopulation);
sequencer.Begin();
}
function SetCurrentStatus(value) {
self.Statistics().CurrentStatus(value);
}
this.GenerateStartingPopulation = function () {// generate the starting population
generationCounter = 0;
self.IsReady(false);
self.IsRunning(true);
self.IsProcessing(false);
stat = new StatisticsViewModel();
self.Statistics(stat) // reset statistics
self.Population.splice(0);
SetCurrentStatus(1);
// to generate the starting population, create that many chromosomes and fill them randomly
for (var i = 0; i < C.StartingPopulationSize() ; i++) {
var nc = new ChromosomeViewModel(C);
nc.FillRandomGenes();
//nc.FromSample(testSample1, i);
self.Population.push(nc);
}
// print to save
var storeString = "[" + Enumerable.From(self.Population()).Select(function (s) {
return "[" + Enumerable.From(s.Genes()).Select(function (a) {
return a.Value();
}).Aggregate(function (a0, a1) {
return a0 + "," + a1;
}) + "]";
}).Aggregate(function (a0, a1) {
return a0 + "," + a1;
}) + "]";
//console.log(storeString.replace(/["']/g, ""));
return self.CalculateFitness;
}
this.CalculateFitness = function () { // calcuate the fitness of the population
SetCurrentStatus(2);
// move to next generation
self.Statistics().CurrentGeneration(++generationCounter);
// calcuate the fitness on each chromosome
for (var i = 0; i < self.Population().length; i++) {
self.Population()[i].CalculateFitness();
}
return self.OrderByFitness;
}
this.OrderByFitness = function () { // order the current population b fitness
SetCurrentStatus(3);
self.Population.sort(function (a0, a1) {
return a0.Fitness() - a1.Fitness();
});
return self.TestForTermination;
}
this.SelectViables = function () { // select the top N/2 viable parents (fittest)
SetCurrentStatus(5);
for (var i = 0; i < self.Population().length; i++) {
self.Population()[i].ViabilityStatus(0);
}
// set the viability status
for (var i = 0; i < C.GenerationSelectionSize; i++) {
self.Population()[i].ViabilityStatus(1);
}
for (var i = C.GenerationSelectionSize; i < self.Population().length; i++) {
self.Population()[i].ViabilityStatus(-1);
}
return self.KillWeaklings;
}
this.KillWeaklings = function () {
SetCurrentStatus(6);
self.Population.splice(C.GenerationSelectionSize);
return self.IdentifyCrossoverPoints;
}
var currentCrossOverPoint = 0;
this.IdentifyCrossoverPoints = function () {
SetCurrentStatus(7);
currentCrossOverPoint = C.CrossoverFn(); // C.CrossoverFn();
for (var i = 0; i < self.Population().length; i++) {
self.Population()[i].CrossoverPosition(currentCrossOverPoint);
}
return self.SplitAtCrossoverPoint;
}
this.SplitAtCrossoverPoint = function () {
SetCurrentStatus(7);
for (var i = 0; i < self.Population().length; i++) {
self.Population()[i].Split();
}
return self.MateParents;
}
this.MateParents = function () {
SetCurrentStatus(7);
var pop = self.Population();
// take the paired parents and mate at crossover point
for (var i = 0; i < C.GenerationSelectionSize; i = i + 2) {
var parent1 = pop[i];
var parent2 = pop[i + 1];
var child1 = new ChromosomeViewModel(C);
var child2 = new ChromosomeViewModel(C);
for (var j = 0; j < parent1.Left.length ; j++) {
child1.Genes.push(parent1.Left[j].Clone());
}
for (var j = 0; j < parent2.Right.length ; j++) {
child1.Genes.push(parent2.Right[j].Clone());
}
for (var j = 0; j < parent2.Left.length ; j++) {
child2.Genes.push(parent2.Left[j].Clone());
}
for (var j = 0; j < parent1.Right.length ; j++) {
child2.Genes.push(parent1.Right[j].Clone());
}
parent1.ResetCrossoverPoint();
parent2.ResetCrossoverPoint();
//console.log(child1.Genes());
self.Population.push(child1);
self.Population.push(child2);
}
return self.Mutate;
}
this.Mutate = function () {
SetCurrentStatus(8); // set only if mutation is done
if (!C.HasMutations()) return self.CalculateFitness;
var currentMutationCount = self.Statistics().MutationsCount();
var requiredMutations = C.RequiredMutations();
var totalAlleles = C.TotalAlleles();
for (var i = 0; i < requiredMutations; i++) {
// randomly select an allele from the entire population
var rndIndex = Math.floor(Math.random() * totalAlleles);
var chromsomeIndex = Math.floor(rndIndex / C.NumberOfGenesPerChromosome);
var alleleIndex = rndIndex % C.NumberOfGenesPerChromosome;
// randomly select a new allele for the mutated new value
var chromosome = self.Population()[chromsomeIndex];
var newMutatedAllele = chromosome.GetRandomAllele();
// set the allele to new allele
//console.log(chromsomeIndex, alleleIndex, String.fromCharCode(chromosome.Genes()[alleleIndex].Value()), String.fromCharCode(newMutatedAllele));
chromosome.Genes()[alleleIndex].Value(newMutatedAllele);
self.Statistics().MutationsCount(++currentMutationCount);
}
return self.CalculateFitness;
}
this.TestForTermination = function () {
SetCurrentStatus(4);
calculateFitnessMeasuresForPopulation();
addStatsToChart();
// if completed, then terminate
// check for the top chromosomes fitness
var bestFitness = self.Population()[0].Fitness();
// set the termination reason before returning
if (bestFitness == 0) return self.Terminate;
// if required generations done, terminate
if (C.LimitedByGenerations() && generationCounter == C.GenerationLimit())
return self.Terminate;
// terminate if no diversity (all same or threshold same fitness)
// else continue
return self.SelectViables;
}
this.Terminate = function () {
self.IsReady(false);
self.IsRunning(false);
self.IsProcessing(true);
SetCurrentStatus(9);
calculateFitnessMeasuresForPopulation();
redrawChart();
self.IsReady(true);
self.IsRunning(false);