LitArk » Books » Computer

Bakunowicz - Computation in Architectural Design: Neural Networks and Genetic Algorithm: Architectural design and metamorphosis evolved by Neural Networks and Genetic Algorithm Python script.

Here you can read online Bakunowicz - Computation in Architectural Design: Neural Networks and Genetic Algorithm: Architectural design and metamorphosis evolved by Neural Networks and Genetic Algorithm Python script. full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2021, genre: Computer. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:

Romance novel Science fiction Adventure Detective Science History Home and family Prose Art Politics Computer Non-fiction Religion Business Children Humor

Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.

Book:
Computation in Architectural Design: Neural Networks and Genetic Algorithm: Architectural design and metamorphosis evolved by Neural Networks and Genetic Algorithm Python script.
Author:
Bakunowicz / Amiina
Genre:
Books / Computer
Year:
2021
Rating:
4 / 5
Favourites:
Add to favourites
Your mark:
- 80
- 1
- 2
- 3
- 4
- 5

Description
Author's other books
Similar books

Computation in Architectural Design: Neural Networks and Genetic Algorithm: Architectural design and metamorphosis evolved by Neural Networks and Genetic Algorithm Python script.: summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "Computation in Architectural Design: Neural Networks and Genetic Algorithm: Architectural design and metamorphosis evolved by Neural Networks and Genetic Algorithm Python script." wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

Bakunowicz: author's other books

Who wrote Computation in Architectural Design: Neural Networks and Genetic Algorithm: Architectural design and metamorphosis evolved by Neural Networks and Genetic Algorithm Python script.? Find out the surname, the name of the author of the book and a list of all author's works by series.

Computation in Architectural Design: Neural Networks and Genetic Algorithm: Architectural design and metamorphosis evolved by Neural Networks and Genetic Algorithm Python script. — read online for free the complete book (whole text) full work

Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Computation in Architectural Design: Neural Networks and Genetic Algorithm: Architectural design and metamorphosis evolved by Neural Networks and Genetic Algorithm Python script." online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.

Light

Font size:

↓

↑

Reset

Interval:

↓

↑

Bookmark:

Make

All rights reserved
The characters and events portrayed in this book are fictitious. Any similarity to real persons, living or dead, is coincidental and not intended by the author.
No part of this book may be reproduced, or stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without express written permission of the publisher.
ISBN-13: 9781234567890
ISBN-10: 1477123456
Cover design by: Art Painter
Library of Congress Control Number: 2018675309
Printed in the United States of America

Computation in Architectural Design: Neural Networks and Genetic Algorithm

Amiina Bakunowicz

ARB RIBA BS(Hons) MSc(1st)

This book researches and describes one of the algorithmic design methods of the architectural metamorphosis evolved by Neural Networks and Genetic Algorithm Python script. For a short demonstration of the code, please visit

https://youtu.be/ Va7kXuoIq-c

Amiina Bakunowicz is a founder, managing director, a researcher and a senior executive architect of ArchiLAB7 (www.archilab7.com), an architectural practice that specialises in Eco-Smart Wellness Architectural design.

Eco-Smart Wellness Architectural design uses unique techniques the incorporate:

Low-carbon footprint design principles;

Algorithmic (or computational design);

Biophilic architectural design;

Golden Section (Fibonacci) geometry.

It is an advanced approach to architectural design of buildings and outdoor spaces that enhances their occupiers both physical and emotional well-being via creating safe, visually appealing and comfortable internal and external environments (including BREEAM and Passivhaus accreditations).

Table of Contents:

Chapter 1. Preface

Chapter 2. Introduction

2.1 The New Aesthetics and the Ecology of Design

2.2 Coding vs Traditional Design Methods

Chapter 3. The Concept of the Architectural Problem

3.1 Architectural Scenario

3.2 Proposed Solution to the Architectural Problem

3.3 Other Ways of Solving Similar Problems?

3.3.1. Electrical Graph Approach

3.3.2. Physically based modelling

Chapter 4. Classic Genetic Algorithm

4.1 Traditional Genetic Algorithm

4.1.1. Brief Description

4.1.2. Evolutionary Embryogenies.

4.2 Classic GA for the Current Architectural Scenario

4.2.1 Description of the Code

4.2.1.1. Original CA Model and Body-Plan

4.2.1.2. The First Generation

4.2.1.3. The Fitness Function

4.2.1.4. The Process of Selection

4.2.1.5. Crossover and Mutation

4.2.2 Outcome of the Algorithm

4.3 Examples of GA Applications in Architecture

Chapter 5. Self-Organising Maps and Genetic Algorithm

5.1 Artificial Neural Networks and Self-Organising Maps

5.2 Introducing SOM to the Classic GA

5.2.1 Why Combining GA with SOM?

5.2.2 Managing the Expanded Choice

5.2.3 The Evaluation of the Aesthetics

5.3. The Evolution of the Proposed Code

5.3.1. Stage 1.Classic GA for the Current Architectural Scenario

5.3.2. Stage 2. Adding Golden Ratio to the Fitness Criteria

5.3.3. Stage 3. Introducing Self-Organising Maps to the Genetic Algorithm

5.3.4. Stage 4. Turning Chromosomes into Neurons and Back

5.3.5. Stage 5. Co-evolving Threshold of the Fitness Factor Components

5.3.6. Stage6. Optional Selection Method

5.4. The Final Algorithm

5.4.1. The Structure of the Algorithm

5.4.1.1. Original CA Model and Body-Plan

5.4.1.2. The First Generation

5.4.1.3. Introducing Self-Organising Maps

5.4.1.4. The Fitness Function

5.4.1.5. Selection Options

5.4.1.5.1. The final decision-making rules of the proposed algorithm

5.4.1.5.2. Selection Option 1: Optimised Random Selection by Clusters

5.4.1.5.3. Selection Option 2: Artificial Selection and Evaluation of Aesthetics

5.4.1.5.4. Selection Option 3: Goldberg Roulette

5.4.1.6. Crossover

5.4.1.7. Mutation

5.4.1.8. The following Generations

5.4.1.9. Visualisation of the Search Space by Clustering and Evolution of Colour

5.4.2. Main Results and Observations

5.5. Advantages and Disadvantages of GA-SOM

5.6. Further Research

5.6.1 Parameterisation of the Body-Plan

5.6.2 Swarms and SOM Pattern Recognition

5.6.3 Discovering the Potential of Clusters

5.6.4. Co-Evolution Instead of Fitness Criteria

5.6.5. Exploring Other Ways in Which CA Can Generate Body-Plans

5.6.6. Other Visualisation Techniques of the Search Space

5.6.7. Adapting Genetic Operators to Evolve the Evolvability

Chapter 6. Conclusion

Chapter 7. References

Chapter 8. FurtherReading

Chapter 9. Appendixes

Appendix 1. Traditional Genetic Algorithm: a Brief Description

Appendix 2. Comparing the Types of the Evolutionary Embryogenies

Appendix 3. Example of a SOM Algorithm Application in 3D Modelling and Its Applications

Appendix 4. Piasecki and Sean's Wundt Curve Experiment

Appendix 5. First Ever SOM for Multi-Objective Evolutionary Algorithm

Appendix 6. Brudaru's Cellular Genetic Algorithm with Communicating Grids and SOM

Appendix 7. Kita's Real-Coded Genetic Algorithm (RCGA) and SOM

Appendix 8. Optional Body-Plans for the Proposed Algorithm

Appendix 9. Proposed Algorithm (Python)

... Game did not display perfection to the outward eye. Rather, it guided the player

Herman Hesse, 1943 The Glass Bead Game

Chapter 1. Preface

Since the dawn of Computer Aided Design for a long time architects were coming up with digitally modelled projects that were very radical, but impossible to build. Later on with the development of the fabrication techniques a lot of the designs have materialised. Currently, architects started using computational methods to re-evaluate designs. In addition to that there always has been the urge for architectural significance and designs became very economic and performance- orientated. Thus the exploration of the alternative computational ways of the architectural morphogenesis was left behind.

I would like to propose a possible solution to the form-finding challenge in a given architectural context by scripting. This paper describes a design process that mostly uses code that combines Genetic Algorithm and Self-Organising Maps and evolves a predetermined Cellular Automata model. A computer forms a team with an architect to develop a design, where the solution is formed gradually during the process of running the algorithm. Together, the designer and the machine go through the architectural design procedure starting from the phase at which the appraisal is carried out, concepts are finalised and the basic mass study is done until both are satisfied with the algorithm's outcome. The eventual model can be then given back to the architect for final adjustments and detailing.

The proposed algorithm has two tasks. Firstly, based on the information available after the completed initial design stages, to develop a schematic body-plan of the architectural model and parameterised it. Secondly, applying the principles of the processes of natural evolution and biological neural networks, use the genetic algorithms (GA) and neural Self-Organising Maps (SOM) to evolve optimal solutions.

This paper will concentrate mostly on the second stage of the algorithm, where the self- organising mapping will be applied to each generation of the alternated body-plan in order to widen, classify, structure and exploit the search space of the GA. I believe that the algorithm can create and amplify the synthetic intuition and give a designer yet another powerful tool and a new skill to be applied in the process of the forming the matter around the architectural concept. Also by structuring and optimising the possibilities space I would like to test if SOM could help to speed up the process of finding the optimal solution and avoid premature convergence.

Light

Font size:

↓

↑

Reset

Interval:

↓

↑

Bookmark:

Make

Similar books «Computation in Architectural Design: Neural Networks and Genetic Algorithm: Architectural design and metamorphosis evolved by Neural Networks and Genetic Algorithm Python script.»

Look at similar books to Computation in Architectural Design: Neural Networks and Genetic Algorithm: Architectural design and metamorphosis evolved by Neural Networks and Genetic Algorithm Python script.. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.

Haiping Huang

Statistical Mechanics of Neural Networks

Albert Chun-Chen Liu

Artificial Intelligence Hardware Design: Challenges and Solutions

Alexandre Bergel

Agile Artificial Intelligence in Pharo: Implementing Neural Networks, Genetic Algorithms, and Neuroevolution

Abhishek Nandy

Neural Networks in Unity: C# Programming for Windows 10

Seth Weidman

Deep Learning from Scratch: Building with Python from First Principles

Yoav Goldberg

Neural Network Methods for Natural Language Processing

Niloy Purkait

Hands-On Neural Networks with Keras: Design and create neural networks using deep learning and artificial intelligence principles

Liyi Zhang

Blind Equalization in Neural Networks: Theory, Algorithms and Applications

Michael Taylor

Neural Networks: A Visual Introduction for Beginners

Balaji Venkateswaran

Neural Networks with R

Singh

C++ Neural Networks And Fuzzy Logic

Tariq Rashid

Make Your Own Neural Network

Reviews about «Computation in Architectural Design: Neural Networks and Genetic Algorithm: Architectural design and metamorphosis evolved by Neural Networks and Genetic Algorithm Python script.»

Discussion, reviews of the book Computation in Architectural Design: Neural Networks and Genetic Algorithm: Architectural design and metamorphosis evolved by Neural Networks and Genetic Algorithm Python script. and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.