| 000 -LEADER |
|---|
| fixed length control field |
03344nam a2200301Ia 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
77112 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
ft6068 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251124105702.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
190321n 000 0 eng d |
| 040 ## - CATALOGING SOURCE |
|---|
| Description conventions |
rda |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA76.9 Ar3 2016 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
. |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Eryck Luis R. Arceo and Mark Jason S. Escopete. |
| 245 #0 - TITLE STATEMENT |
|---|
| Title |
An enhancement of the neuroevolutionary algorithm applied in Super Mario Bros Game |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
2016 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Other physical details |
Undergraduate Thesis: (BSCS major in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2016. |
| 336 ## - CONTENT TYPE |
|---|
| Content type code |
. |
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Materials specified |
0 |
| Media type code |
. |
| Media type term |
unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Materials specified |
0 |
| Carrier type code |
. |
| Carrier type term |
volume |
| Source |
rdacarrier |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
ABSTRACT: NeuroEvolutionary is an algorithm that uses evolution algorithm to train artificial neutral networks to be presented for the people to have an enhanced working Artificial Intelligence for solving a stage in Super Mario Bros. The research involves determining the problems of the existing algorithm. This has been done by examining past related studies and literatures and pinpointing the common problems encountered. This also involves proving that the NeuroRevolutionary algorithm is the best algorithm to be used in applications with network topology by comparing different types of evolutionary algorithms. The research also discusses the methods and procedures it underwent. A survey was conducted to outline the performance of the test, simulate and calibrate to attain the best possible result. The anticipated outcome of the research is the improved initialization, minimizing network and reducing the output which was presented during the simulation and the data gathering. This has been done by comparing and interpreting the results of the existing and enhanced algorithm. Upon the completion of the research, the researchers provided recommendations which may be highly recommended for other aspiring researchers to improve. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
5 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
ABSTRACT: NeuroEvolutionary is an algorithm that uses evolution algorithm to train artificial neutral networks to be presented for the people to have an enhanced working Artificial Intelligence for solving a stage in Super Mario Bros. The research involves determining the problems of the existing algorithm. This has been done by examining past related studies and literatures and pinpointing the common problems encountered. This also involves proving that the NeuroRevolutionary algorithm is the best algorithm to be used in applications with network topology by comparing different types of evolutionary algorithms. The research also discusses the methods and procedures it underwent. A survey was conducted to outline the performance of the test, simulate and calibrate to attain the best possible result. The anticipated outcome of the research is the improved initialization, minimizing network and reducing the output which was presented during the simulation and the data gathering. This has been done by comparing and interpreting the results of the existing and enhanced algorithm. Upon the completion of the research, the researchers provided recommendations which may be highly recommended for other aspiring researchers to improve. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
|---|
| Classification |
Filipiniana |
| 540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE |
|---|
| Terms governing use and reproduction |
5 |
| 655 ## - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
|---|
| Institution code [OBSOLETE] |
lcc |
| Item type |
Archival materials |
| Source of classification or shelving scheme |
|