RESEARCH POSTER PRESENTATION DESIGN © 2012

www.PosterPresentations.com

QUICK DESIGN GUIDE (--THIS SECTION DOES NOT PRINT--)

This PowerPoint 2007 template produces an A0 size professional poster. It will

save you valuable time placing titles, subtitles, text, and graphics.

Use it to create your presentation. Then send it to PosterPresentations.com for

premium quality, same day affordable printing.

We provide a series of online tutorials that will guide you through the poster

design process and answer your poster production questions.

View our online tutorials at:

http://bit.ly/Poster_creation_help

(copy and paste the link into your web browser).

For assistance and to order your printed poster call PosterPresentations.com at

1.866.649.3004

Object Placeholders

Use the placeholders provided below to add new elements to your poster: Drag

a placeholder onto the poster area, size it, and click it to edit.

Section Header placeholder

Move this preformatted section header placeholder to the poster area to add

another section header. Use section headers to separate topics or concepts

within your presentation.

Text placeholder

Move this preformatted text placeholder to the poster to add a new body of

text.

Picture placeholder

Move this graphic placeholder onto your poster, size it first, and then click it to

add a picture to the poster.

Student discounts are available on our Facebook page.

Go to PosterPresentations.com and click on the FB icon.

QUICK TIPS

(--THIS SECTION DOES NOT PRINT--)

This PowerPoint template requires basic PowerPoint (version 2007 or newer)

skills. Below is a list of commonly asked questions specific to this template.

If you are using an older version of PowerPoint some template features may not

work properly.

Using the template

Verifying the quality of your graphics

Go to the VIEW menu and click on ZOOM to set your preferred magnification.

This template is at 100% the size of the final poster. All text and graphics will be

printed at 100% their size. To see what your poster will look like when printed,

set the zoom to 100% and evaluate the quality of all your graphics before you

submit your poster for printing.

Using the placeholders

To add text to this template click inside a placeholder and type in or paste your

text. To move a placeholder, click on it once (to select it), place your cursor on

its frame and your cursor will change to this symbol: Then, click once and

drag it to its new location where you can resize it as needed. Additional

placeholders can be found on the left side of this template.

Modifying the layout

This template has four different

column layouts. Right-click your

mouse on the background and

click on “Layout” to see the

layout options. The columns in

the provided layouts are fixed and cannot be moved but advanced users can

modify any layout by going to VIEW and then SLIDE MASTER.

Importing text and graphics from external sources

TEXT: Paste or type your text into a pre-existing placeholder or drag in a new

placeholder from the left side of the template. Move it anywhere as needed.

PHOTOS: Drag in a picture placeholder, size it first, click in it and insert a photo

from the menu.

TABLES: You can copy and paste a table from an external document onto this

poster template. To adjust the way the text fits within the cells of a table that

has been pasted, right-click on the table, click FORMAT SHAPE then click on

TEXT BOX and change the INTERNAL MARGIN values to 0.25

Modifying the color scheme

To change the color scheme of this template go to the “Design” menu and click

on “Colors”. You can choose from the provide color combinations or you can

create your own.

© 2012 PosterPresentations.com 2117 Fourth Street , Unit C Berkeley CA 94710 posterpresenter@gmail.com

Authors: T. Ong, R. Saunders, J. Keyser, J. Leggett

Sample Paper: “Terrain generation using genetic algorithms”

Genotype: List of transform operations for control points on a height-map. Operations include translate, rotate,

and raise/lower.

Fitness Evaluation: Similarity measure between a candidate and the initial population.

Parent Selection: Not stated.

Seeding: Initial population made of sample terrains from geospatial height-map data.

Crossover: One-point crossover of the operator lists of two parents.

Mutation: Add or change a transform operation of a candidate.

Approach Fitness Evaluation Refinement Variety Control Game Integration Ideal Use

Ong et al. Compared to example terrains. High Low Medium Low Simulated natural terrain. Could be used in games such as flight simulators that need large, natural terrain.

Ashlock et al. Compared to idealized terrain. Medium Low Medium Low Where single feature terrains and fractal terrains are applicable. Simulation applications.

Walsh and Gade Interactive evolution. High Low High Low Evolutionary art where a single screen capture is more desirable then a playable game.

Frade et al. Interactive evolution. Accessibility metric. Obstacle length metric.

Medium High Low Medium Early approaches for evolutionary art or games with eccentric terrain. Later approaches for games that require predominately flat terrain.

Togelius et al. Multiobjective evolution for base and resource distances and asymmetry of terrain.

High Medium Low High Real-time strategy games that use player bases and collectable resources.

Raffe et al. Two-levelled interactive evolution. High Medium High Medium As a development aid for game maps of all sizes.

RMIT University, Australia {william.raffe , fabio.zambetta , xiaodong.li} @rmit.edu.au

William Raffe, Fabio Zambetta, Xiaodong Li

A SURVEY OF PROCEDURAL TERRAIN GENERATION TECHNIQUES USING EVOLUTIONARY ALGORITHMS

Authors: D. Ashlock, S. Gent, K. Bryden

Sample Paper: “Evolution of l-systems for compact virtual landscape generation”

Genotype: 1) A list of L-system symbol replacement rules. Each symbol is replaced by 4 others in a replacement

and therefore grows two dimensionally. 2) A list of displacement values for each symbol in the grammar that is

used to convert the L-System into height-map data.

Fitness Evaluation: A similarity measure between a candidate and an idealized target terrain.

Parent Selection: Size 7 Tournament Selection. Replaces the least fit candidates.

Seeding: None (Random generation of initial population).

Crossover: Two-point crossover for both genotypes.

Mutation: Change one symbol replacement rule in a candidate by randomly changing the resulting symbol.

Change one displacement value in a candidate by stochastic stepping.

Authors: P. Walsh, P. Gade

Sample Paper: “Terrain generation using an interactive genetic algorithm”

Genotype: Parameter values in the form of 8-bit strings. Parameters include feature scale, spikiness, water

level, sun angle, and cloud coverage.

Fitness Evaluation: Interactive evolution.

Parent Selection: Tournament Selection with higher probability given to candidates that the user selected

through Interactive Evolution.

Seeding: A base terrain is provided and parameter changes applied to it to create candidates.

Crossover: One-point Crossover of two parents.

Mutation: Flipping a single bit in one or more of a child’s 8-bit parameter strings.

Authors: M. Frade, F. F. de Vega, C. Cotta

Sample Paper: “Evolution of artificial terrains for video games based on obstacles edge length”

Genotype: A tree of numerical and trigonometric operators with noise functions as terminal functions (leaf

nodes). Resulting program is applied to each height-map vertex coordinate to get a height value.

Fitness Evaluation: Interactive evolution. Accessibility measure. Obstacle length measure.

Parent Selection: Size 7 Tournament Selection.

Seeding: None (random generation of initial population).

Crossover: Sub-tree crossover – A random node from each parent is chosen and their respective sub-trees are

swapped.

Mutation: Addition or substitution of a tree node. Removal of a tree node and it’s sub-tree.

Authors: J. Togelius, M. Preuss, G. Yannakakis

Sample Paper: “Towards multiobjective procedural map generation”

Genotype: Each mountain created by a Gaussian curve on a height-map. Each peak/ridge has 5 parameter

values: 2 for standard deviation of a Gaussian distribution, 2 for the (x,y) coordinates of a mountain peak, and 1

for the height of the peak. Player bases and game resources have similar coordinate parameters.

Fitness Evaluation: Multiobjective evaluation of fitness measures that ensure dispersal of player bases on the

terrain, fair access to game resources from each base, and traversable paths between bases.

Parent Selection: Fittest candidate always chosen as parents.

Seeding: None (random generation of initial population).

Crossover: Simulated binary crossover.

Mutation: Probability based mutation of terrain parameters and base/resource coordinates.

Authors: W. Raffe, F. Zambetta, X. Li

Sample Paper: “Evolving patch-based games for use in video games”

Genotype: NxN grid of identifiers. Each identifier is associated with a unique, pre-made patch of height-map

terrain data.

Fitness Evaluation: Interactive evolution.

Parent Selection: Parents chosen solely through interactive evolution.

Seeding: Pre-made terrains are provided to program which are decomposed into smaller patches of height-map

data and given unique identifiers. These are also used as the initial population.

Crossover: Uniform crossover - Each patch identifier of each parent has a probability of appearing in a child.

Mutation: Each patch identifier in a new child given a probability to mutate. A randomly selected patch replaces

the existing patch.

Summary

Procedurally generated terrains have been successfully used in numerous applications over the last three decades.

They are predominantly utilized in media applications, such as video games, animations, and simulations, where they

are used either as designer aids, to provide expansive virtual environments to navigate, or to provide a believable

backdrop to an existing scene.

Recently, the use of Evolutionary Algorithms (EA) during the procedural terrain generation process has been proposed

and is now a growing field. The use of EA allows for more control to be exerted on the terrain generation process. Most

existing procedural terrain generation systems are designed for a specific application, however the inclusion of EA has

the potential result in algorithms that can produce not only a higher variety of terrains but also terrains that can be

sculpted at a finer resolution.

This poster displays the six primary approaches that have been published thus far. A summary of the EA structure used

in each is provided as well as an accompanying image from the respective papers. A table of comparisons is also

provided, analyzing each technique with the specific purpose of generating terrain to be used in video games.

Acknowledgments

All images belong to their respective authors and those included in the conference

proceedings are done so with consent of the primary author of each paper.

Each one of the approaches shown here has its own advantages and disadvantages and each of them provides new

ideas and highlights new challenges to overcome. By reviewing them it has been shown that the focus of this research

field in the future needs to be directed towards:

• Finding a robust genotype representation. A superior genotype should allow for a variety of terrains to be generated

so that it can be used for multiple applications, allow for a high level of refinement to produce detailed terrain, and

provide enough control such that terrain can be created to meet a user’s requirements.

• Finding strong fitness evaluation methods that can be used to score each candidate for the purposes of playability in

video games, believability in animations and simulations, and aesthetics in art.

• Investigating and agreeing upon a standardised metric that can be used to evaluate the performance of these types

of evolutionary procedural terrain generation algorithms.

Conclusions