Genetische Algorithmen in der Praxis
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Bachelorarbeit aus dem Jahr 2009 im Fachbereich Informatik - Programmierung, Note: 1, Fachhochschule Technikum Wien (Informations- und Kommunikationssysteme), Sprache: Deutsch, Abstract: Genetische Algorithmen und Evolutionsstrategien sind heuristische Optimierungsmethoden, die ihr Vorbild in der natürlichen Evolution haben. In dieser Arbeit wird diese Methode benutzt, um die Parameter des Stellenbewertungsmoduls des Brettspielprogramms "Qubic" (eine dreidimensionale Variante von Tic-Tac-Toe) zu optimieren. Es werden insgesamt 28 Parameter definiert, die zusammen das Chromosom eines Spielers bilden. In verschiedenen Versuchsreihen müssen die Spieler durch das Spiel gegen genormte Standardspieler ihre absolute Spielstärke unter Beweis stellen. In einer dieser Reihen wird auch eine relative Spielstärke durch direkten Vergleich der Spieler untereinander ermittelt. Nachkommen werden entweder durch Mutation des Genoms eines Spielers, oder durch Zusammenfügen von Teilen der Gene zweier Elternteile ("Crossover") erzeugt. Diese Nachkommen nehmen entweder als eine neue Generation am Selektionsprozess teil, oder ersetzen innerhalb der Population den jeweils zu diesem Zeitpunkt schlechtesten Spieler. Je nach Versuchsreihe entscheidet die absolute oder die relative Spielstärke darüber, mit welcher Wahrscheinlichkeit jedes Individuum seine Gene an Nachkommen weitergeben darf. Die Methode, strikt voneinander getrennte Generationen zu verwenden, wird mit einem kontinuierlichen Austausch von Individuen verglichen. Dabei schneidet die kontinuierliche Verbesserung überraschend gut ab, was das Entwicklungstempo der Population betrifft. Bestimmte Überlegungen legen nahe, dass die Chromosomen guter Spieler eine gewisse Struktur aufweisen müssen. Durch den Vergleich einer Versuchsreihe, die ausschlie lich solche strukturierten Chromosomen verwendet, mit einer Reihe, die sich nicht an diese Vorgaben halten muss, kann gezeigt werden, dass die Chromosomen guter Spieler tatsächlich einige Ei
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