Genetic approach to strategic games

graduate thesis

Črt Kristl (Author), Gašper Fijavž (Mentor), Andrej Brodnik (Co-mentor)

Abstract

In this thesis we develop and implement a genetic algorithm to optimize a set of talents, equipment and sub-attributes of characters in the game Warcraft III and its modification The Kingdom of Kaliron. Finding the optimal set where a character performs the best in fights against enemies is a combinatorial problem for which we use a genetic algorithm to solve. To be able to evaluate a character, we implemented a simulation that required deep knowledge of game mechanics and programming principles of Warcraft III. We also used reverse engineering as a tool. We ensured convergence of a genetic algorithm with the use of population islands, which are disjoint subpopulations with weak mutual interactions, and with careful choosing of genetic algorithm parameters. We also implemented genetic algorithm memory, which helps create better initial individuals when creating new populations. Finally, we used parallelization to reduce the running time of the algorithm.

Keywords

genetic algorithm;computer games;optimization;parallelization;simulation;computer science;computer and information science;diploma;

Data

Language:	English
Year of publishing:	2016
Typology:	2.11 - Undergraduate Thesis
Organization:	UL FRI - Faculty of Computer and Information Science
Publisher:	[Č. Kristl]
UDC:	004(043.2)
COBISS:	1536802243
Views:	2139
Downloads:	324
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary title:	Genetski pristop k strateškim igram
Secondary abstract:	V delu razvijemo in implementiramo genetski algoritem s katerim optimiziramo nabor znanj, opreme in lastnosti akcijskih junakov v strateški igri Warcraft III, oziroma njeni različici The Kingdom of Kaliron. Izbiro optimalnega nabora, takega pri katerem je akcijski junak kar se da uspešen v bojevanju z nasprotniki, zapišemo kot problem kombinatorične optimizacije, za njegovo reševanje pa uporabimo pristop z genetskim algoritmom. Za oceno sposobnosti junaka smo implementirali simulacijo, za katero smo potrebovali natančno poznavanje mehaničnih in programskih principov igre Warcraft III. Med drugim smo uporabljali metode vzvratnega inženirstva. Konvergenco genetskega algoritma smo zagotovili z uporabo otokov, ločenih podpopulacij s šibko medsebojno interakcijo, in s pazljivo izbiro parametrov genetskega algoritma. Poleg tega smo genetskemu algoritmu dodali spomin, ki pri ustvarjanju novih populacij pripomore k boljšem začetnem stanju osebkov. Ustrezno časovno učinkovitost pa smo pridelali s paralelizacijo metode.
Secondary keywords:	genetski algoritem;računalniške igre;optimizacija;paralelizacija;simulacija;računalništvo;visokošolski strokovni študij;računalništvo in informatika;diplomske naloge;
File type:	application/pdf
Type (COBISS):	Bachelor thesis/paper
Study programme:	1000470
Embargo end date (OpenAIRE):	1970-01-01
Thesis comment:	Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages:	48 str.
ID:	9124186