The city of Tel-Aviv originates in the late 1880s, with the first move by Jews outside the walls of Jaffa, then a small port town connected by a developed road system to other cities in the region.
In an attempt to create modern neighborhoods without the need of the defensive system of the town walls, which no longer offered protection in advanced warfare techniques.
RSM Design Solutions is Facade Consultant Specializing in all Facade Element Treatments. Founded in 2008, our design and consulting services
for state-of-the-art building envelope systems. RSM Design Solutions operates as a 100% Employee-owned and operated organization.
Management of the Company has come from within the ranks of the staff, with design professionals who are completely and solely dedicated to
design, engineering, and consulting for the building envelope. Our engineers participate with the whole design team to generate a balanced design
which satisfies a wide range of performance and aesthetic requirements. Facades form the envelope between the internal and external
environments and the design fundamentally affects the performance of the whole building. Good Facade design contributes to optimized
performance, reduced energy costs and improved comfort levels. Our goal is to integrate the thermal, acoustic, fire and ventilation performance
requirements, while honoring the aesthetic intent. All these objectives are to be delivered within the context of environmental sustain ability and
economic viability.
Our extensive experience, technical expertise and professional focus ensures our clients get the best possible service. Our policy is to form long
term associations with our clients and working closely in partnership throughout the period of the project. We endeavor to fully understand the
client’s objectives and utilize our knowledge and expertise to congregate their goals.
As a professional services firm, we provide design and engineering for some of the most well-known Facade cladding manufacturers, providing
design support for established and tested systems, creating new systems, and assisting in development of innovative new approaches to cladding
execution.
We work closely with Building Owners &Developers,Architects,PMCand Specialist Facade Contractors
For Building Owners&Developers ...We develop systems to guide you selecting appropriate&economical system for your project. .........
ForArchitects&PMC ......We assist Architects in making FacadeDetails, Tender andProject Specification. ............................
For Specialist Facade Contractor We provide complete Design-Developmentwith Project planning. ...............
Our Mission
To provide the efficient, cost effective and innovative design & engineering solutions that deal with the latest developments in the facade technology,
raising the quality standards and persistently setting a new benchmark in the facade engineering industry.
OurVision
To be a prominent global provider of facade consultancy, design and engineering services and to play an significant role in the development of our
industry.
Photogrammetry for Architecture and ConstructionDat Lien
Part of the North America Revit technology Conference in Arizona in 2016, this presentation focuses on using drones and other vehicles combined with different payloads to acquire visual data that can be converted to 3d point clouds and ortho mosaics that can then be used as part of a Building Information Modeling (BIM) workflow in such applications as Autodesk Revit, Navisworks and 3ds Max for design and construction.
The city of Tel-Aviv originates in the late 1880s, with the first move by Jews outside the walls of Jaffa, then a small port town connected by a developed road system to other cities in the region.
In an attempt to create modern neighborhoods without the need of the defensive system of the town walls, which no longer offered protection in advanced warfare techniques.
RSM Design Solutions is Facade Consultant Specializing in all Facade Element Treatments. Founded in 2008, our design and consulting services
for state-of-the-art building envelope systems. RSM Design Solutions operates as a 100% Employee-owned and operated organization.
Management of the Company has come from within the ranks of the staff, with design professionals who are completely and solely dedicated to
design, engineering, and consulting for the building envelope. Our engineers participate with the whole design team to generate a balanced design
which satisfies a wide range of performance and aesthetic requirements. Facades form the envelope between the internal and external
environments and the design fundamentally affects the performance of the whole building. Good Facade design contributes to optimized
performance, reduced energy costs and improved comfort levels. Our goal is to integrate the thermal, acoustic, fire and ventilation performance
requirements, while honoring the aesthetic intent. All these objectives are to be delivered within the context of environmental sustain ability and
economic viability.
Our extensive experience, technical expertise and professional focus ensures our clients get the best possible service. Our policy is to form long
term associations with our clients and working closely in partnership throughout the period of the project. We endeavor to fully understand the
client’s objectives and utilize our knowledge and expertise to congregate their goals.
As a professional services firm, we provide design and engineering for some of the most well-known Facade cladding manufacturers, providing
design support for established and tested systems, creating new systems, and assisting in development of innovative new approaches to cladding
execution.
We work closely with Building Owners &Developers,Architects,PMCand Specialist Facade Contractors
For Building Owners&Developers ...We develop systems to guide you selecting appropriate&economical system for your project. .........
ForArchitects&PMC ......We assist Architects in making FacadeDetails, Tender andProject Specification. ............................
For Specialist Facade Contractor We provide complete Design-Developmentwith Project planning. ...............
Our Mission
To provide the efficient, cost effective and innovative design & engineering solutions that deal with the latest developments in the facade technology,
raising the quality standards and persistently setting a new benchmark in the facade engineering industry.
OurVision
To be a prominent global provider of facade consultancy, design and engineering services and to play an significant role in the development of our
industry.
Photogrammetry for Architecture and ConstructionDat Lien
Part of the North America Revit technology Conference in Arizona in 2016, this presentation focuses on using drones and other vehicles combined with different payloads to acquire visual data that can be converted to 3d point clouds and ortho mosaics that can then be used as part of a Building Information Modeling (BIM) workflow in such applications as Autodesk Revit, Navisworks and 3ds Max for design and construction.
Meydan District One is one of Dubai's most incredible residential developments. In this presentation we do a deep dive into the location, developer, master-plan, future developments and especially the District One Residences - luxury apartment buildings which are currently under-construction.
Report on the First Knowledge Graph Reasoning Challenge 2018 -Toward the eXp...KnowledgeGraph
JIST2019: The 9th Joint International Semantic Technology Conference
The premium Asian forum on Semantic Web, Knowledge Graph, Linked Data and AI on the Web. Nov. 25-27, 2019, Hangzhou, China.
http://jist2019.openkg.cn/
over view about Persian architecture, arabesque motifs ,calligraphy ,carpets and kilim ,Persian expressions and animals meaning in Persian architecture.
Meydan District One is one of Dubai's most incredible residential developments. In this presentation we do a deep dive into the location, developer, master-plan, future developments and especially the District One Residences - luxury apartment buildings which are currently under-construction.
Report on the First Knowledge Graph Reasoning Challenge 2018 -Toward the eXp...KnowledgeGraph
JIST2019: The 9th Joint International Semantic Technology Conference
The premium Asian forum on Semantic Web, Knowledge Graph, Linked Data and AI on the Web. Nov. 25-27, 2019, Hangzhou, China.
http://jist2019.openkg.cn/
over view about Persian architecture, arabesque motifs ,calligraphy ,carpets and kilim ,Persian expressions and animals meaning in Persian architecture.
Lecture on “Aerodynamic design of Aircraft” in University of Tokyo 21st December, 2015. Optimization techniques, data-visualization and their applications are inclusive.
1. Text reference, Chapter 6
2. Special case of the general factorial design; k factors, all at two levels
3. The two levels are usually called low and high (they could be either quantitative or qualitative)
4. Very widely used in industrial experimentation
5. Form a basic “building block” for other very useful experimental designs (DNA)
6. Special (short-cut) methods for analysis
7. We will make use of Design-Expert
United Kingdom: +44-1143520021
India: +044 3318-2000
Email: info@statswork.com
Website: www.statswork.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Analysis and Design of Algorithms (ADA): An In-depth Exploration
Introduction:
The field of computer science is heavily reliant on algorithms to solve complex problems efficiently. The analysis and design of algorithms (ADA) is a fundamental area of study that focuses on understanding and creating efficient algorithms. This comprehensive overview will delve into the various aspects of ADA, including its importance, key concepts, techniques, and applications.
Importance of ADA:
Efficient algorithms play a critical role in various domains, including software development, data analysis, artificial intelligence, and optimization. ADA provides the tools and techniques necessary to design algorithms that are both correct and efficient. By analyzing the performance characteristics of algorithms, ADA enables computer scientists and engineers to develop solutions that save time, resources, and computational power.
Key Concepts in ADA:
Correctness: ADA emphasizes the importance of designing algorithms that produce correct outputs for all possible inputs. Techniques like mathematical proofs and induction are used to establish the correctness of algorithms.
Complexity Analysis: ADA seeks to analyze the efficiency of algorithms by examining their time and space complexity. Time complexity measures the amount of time required by an algorithm to execute, while space complexity measures the amount of memory consumed.
Asymptotic Notations: ADA employs asymptotic notations, such as Big O, Omega, and Theta, to express the growth rates of functions and classify the efficiency of algorithms. These notations allow for a concise comparison of algorithmic performance.
Algorithm Design Paradigms: ADA explores various design paradigms, including divide and conquer, dynamic programming, greedy algorithms, and backtracking. Each paradigm offers a systematic approach to solving problems efficiently.
Techniques in ADA:
Divide and Conquer: This technique involves breaking down a problem into smaller subproblems, solving them independently, and combining the solutions to obtain the final result. Well-known algorithms like Merge Sort and Quick Sort utilize the divide and conquer approach.
Dynamic Programming: Dynamic programming breaks down a complex problem into a series of overlapping subproblems and solves them in a bottom-up manner. This technique optimizes efficiency by storing and reusing intermediate results. The Fibonacci sequence calculation is a classic example of dynamic programming.
Greedy Algorithms: Greedy algorithms make locally optimal choices at each step, with the hope of achieving a global optimal solution. These algorithms are efficient but may not always yield the best overall solution. The Huffman coding algorithm for data compression is a widely used example of a greedy algorithm.
Backtracking: Backtracking involves searching for a solution to a problem by incrementally building a solution and undoing the choices that lead to dead-ends.
Algorithmic Techniques for Parametric Model RecoveryCurvSurf
A complete description of algorithmic techniques for automatic feature extraction from point cloud. The orthogonal distance fitting, an art of maximum liklihood estimation, plays the main role. Differential geometry determines the type of object surface.
SP18 Generative Design - Week 7 - GD case studiesDanil Nagy
Lecture from Generative Design course at Columbia University Graduate School of Architecture, Planning, and Preservation.
All work depicted (c) The Living, an Autodesk Studio
Data Mining the City - A (practical) introduction to Machine LearningDanil Nagy
Slides from a lecture given on October 14, 2015 for the Data Mining the City class at Columbia University's Graduate School of Architecture, Planning, and Preservation (GSAPP)
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
2. Session A - review
Columbia University GSAPP
ARCH A4845: Generative design
3. Elements of generative design
1. Generate - create a ‘design space’ of all possible designs
2. Evaluate - develop measures to judge each design’s performance
3. Evolve - search through design space to find unique high-performing designs
Columbia University GSAPP
ARCH A4845: Generative design
4. Design space model
OPTIMIZATION
Design parameters
(genotype)
Design geometry
(morphogenesis)
Design measures
(phenotype)
Optimization
(evolution)
Columbia University GSAPP
ARCH A4845: Generative design
5. Session B - design optimization
Columbia University GSAPP
ARCH A4845: Generative design
6. Optimization frameworkNature-Insp
ired Metaheuristi
cAlgorithms
Sec
ond Edition (20
10)
Xin-She Yang
c Luniver Press
tain objectives or to optimize something such as profit, quality and time.
As resources, time and money are always limited in real-world applica-
tions, we have to find solutions to optimally use these valuable resources
under various constraints. Mathematical optimization or programming is
the study of such planning and design problems using mathematical tools.
Nowadays, computer simulations become an indispensable tool for solving
such optimization problems with various efficient search algorithms.
1.1 OPTIMIZATION
Mathematically speaking, it is possible to write most optimization problems
in the generic form
minimize
x∈ n fi(x), (i = 1, 2, ..., M), (1.1)
subject to hj(x) = 0, (j = 1, 2, ..., J), (1.2)
gk(x) ≤ 0, (k = 1, 2, ..., K), (1.3)
where fi(x), hj(x) and gk(x) are functions of the design vector
x = (x1, x2, ..., xn)T
. (1.4)
Here the components xi of x are called design or decision variables, and
they can be real continuous, discrete or the mixed of these two.
The functions fi(x) where i = 1, 2, ..., M are called the objective func-
tions or simply cost functions, and in the case of M = 1, there is only a
single objective. The space spanned by the decision variables is called the
design space or search space n
, while the space formed by the objective
function values is called the solution space or response space. The equali-
ties for hj and inequalities for gk are called constraints. It is worth pointing
Xin-She Yang. Nature-Inspired Metaheuristic Algorithms (2008) Columbia University GSAPP
ARCH A4845: Generative design
7. Optimization framework - components
1. Input parameters - set of variables that can be adjusted
• discrete / categorical - whole number
• continuous - decimal number
• permutation / ordering - whole number sequence
2. Objectives - functions representing goals of the problem
• minimize value
• maximize value
3. Constraints - functions representing conditions that make a valid solution
• must be equal to certain value
• must be smaller than or greater than certain value
Columbia University GSAPP
ARCH A4845: Generative design
8. How do we optimize?
A. Deterministic methods
1. Direct analysis - linear and quadratic programming
2. Gradient descent
3. Exhaustive search
4. Heuristic
B. Stochastic methods
5. Monte Carlo (MC) - completely random
6. Metaheuristic
Columbia University GSAPP
ARCH A4845: Generative design
9. 1. Direct analysis
Solution of optimization problem by linear programming
Columbia University GSAPP
ARCH A4845: Generative design
10. 2. Gradient descent
X (input)
Y(objective)
Columbia University GSAPP
ARCH A4845: Generative design
19. Given a list of cities and the
distances between each pair of
cities, what is the shortest
possible route that visits each
city exactly once and returns to
the origin city?
https://en.wikipedia.org/wiki/Travelling_salesman_problem
Travelling salesman problem (TSP)
Columbia University GSAPP
ARCH A4845: Generative design
20. 3. Exhaustive search
n = number of cities
number of solutions = (n-1)!
10! = 10 * 9 * 8 * 7 * 6 * 5 * 4 * 3 * 2 * 1 = 3,628,800
20! = 2.432902e+18
30! = 2.652529e+32
Columbia University GSAPP
ARCH A4845: Generative design
34. 6. Metaheuristic search
The Shortest Route Between All the Pubs in the UK
[http://bigthink.com/strange-maps/the-shortest-route-between-all-the-pubs-in-the-uk]
Columbia University GSAPP
ARCH A4845: Generative design
35. Metaheuristic search algorithms
CONTENTS
Preface to the Second Edition v
Preface to the First Edition vi
1 Introduction 1
1.1 Optimization 1
1.2 Search for Optimality 2
1.3 Nature-Inspired Metaheuristics 4
1.4 A Brief History of Metaheuristics 5
2 Random Walks and L´evy Flights 11
2.1 Random Variables 11
2.2 Random Walks 12
2.3 L´evy Distribution and L´evy Flights 14
2.4 Optimization as Markov Chains 17
i
ii CONTENTS
3 Simulated Annealing 21
3.1 Annealing and Boltzmann Distribution 21
3.2 Parameters 22
3.3 SA Algorithm 23
3.4 Unconstrained Optimization 24
3.5 Stochastic Tunneling 26
4 How to Deal With Constraints 29
4.1 Method of Lagrange Multipliers 29
4.2 Penalty Method 32
4.3 Step Size in Random Walks 33
4.4 Welded Beam Design 34
4.5 SA Implementation 35
5 Genetic Algorithms 41
5.1 Introduction 41
5.2 Genetic Algorithms 42
5.3 Choice of Parameters 43
6 Differential Evolution 47
6.1 Introduction 47
6.2 Differential Evolution 47
6.3 Variants 50
6.4 Implementation 50
7 Ant and Bee Algorithms 53
7.1 Ant Algorithms 53
7.1.1 Behaviour of Ants 53
7.1.2 Ant Colony Optimization 54
7.1.3 Double Bridge Problem 56
7.1.4 Virtual Ant Algorithm 57
7.2 Bee-inspired Algorithms 57
7.2.1 Behavior of Honeybees 57
7.2.2 Bee Algorithms 58
7.2.3 Honeybee Algorithm 59
7.2.4 Virtual Bee Algorithm 60
7.2.5 Artificial Bee Colony Optimization 61
Xin-She Yang. Nature-Inspired Metaheuristic Algorithms (2008) Columbia University GSAPP
ARCH A4845: Generative design
36. Genetic Algorithm (GA)
Alan Turing (1950)
Computing Machinery and Intelligence
John Holland (1975)
Adaptation in Natural and Artificial Systems
Columbia University GSAPP
ARCH A4845: Generative design
37. t
32 :
33 : !
34 : "
35 : #
36 : $
37 : %
38 : &
39 : '
40 : (
41 : )
42 : *
43 : +
44 : ,
45 : -
46 : .
47 : /
o b e o r n o t t o b e
48 : 0
49 : 1
50 : 2
51 : 3
52 : 4
53 : 5
54 : 6
55 : 7
56 : 8
57 : 9
58 : :
59 : ;
60 : <
61 : =
62 : >
63 : ?
64 : @
65 : A
66 : B
67 : C
68 : D
69 : E
70 : F
71 : G
72 : H
73 : I
74 : J
75 : K
76 : L
77 : M
78 : N
79 : O
80 : P
81 : Q
82 : R
83 : S
84 : T
85 : U
86 : V
87 : W
88 : X
89 : Y
90 : Z
91 : [
92 :
93 : ]
94 : ^
95 : _
96 : `
97 : a
98 : b
99 : c
100 : d
101 : e
102 : f
103 : g
104 : h
105 : i
106 : j
107 : k
108 : l
109 : m
110 : n
111 : o
112 : p
113 : q
114 : r
115 : s
116 : t
117 : u
118 : v
119 : w
120 : x
121 : y
122 : z
123 : {
124 : |
125 : }
126 : ~
127 :
Genetic Algorithm (GA)
Daniel Shiffman - The Nature of Code, Chapter 9: The Evolution of Code (2012) Columbia University GSAPP
ARCH A4845: Generative design
38. 96 possibilities ^ 18 places
= 479,603,335,372,621,236,652,373,132,533,825,536
= 4.796 x 10^35 or 479.6 decillion possibilities
Genetic Algorithm (GA)
Daniel Shiffman - The Nature of Code, Chapter 9: The Evolution of Code (2012) Columbia University GSAPP
ARCH A4845: Generative design
39. With a basic Genetic Algorithm (GA)...
38 generations
1,000 designs / generation
38,000 designs computed
= 32 seconds
Daniel Shiffman - The Nature of Code, Chapter 9: The Evolution of Code (2012)
Genetic Algorithm (GA)
Columbia University GSAPP
ARCH A4845: Generative design
40. Genetic Algorithm (GA)
A Genetic Algorithm creates “generations” of solutions in such a way that
the solutions get better over time
Steps:
1. Generate initial population of solutions
2. Rank solutions based on their performance in objectives and constraints
3. Generate next generation by applying elitism, crossover, and mutation to
current generation
4. Repeat until termination criteria is met
Columbia University GSAPP
ARCH A4845: Generative design
42. 2. Ranking (single objective)
GW%M9{,vy-3{dZoUA⌂
rV:^st”#U. u`]f5i
Lg7]4#5ADB;GNfa}u|
0”{8c^h$S1BJ)=omy
‘ -gI^HoMRIN$YV%O
KoMQ%25”zHnGt1whXY
target =
to be or not to be
GW%M9{,vy-3{dZoUA⌂
0”{8c^h$S1BJ)=omy
KoMQ%25”zHnGt1whXY
“the mating pool”
All designs in the generation are evaluated based on the objectives and constraints of the
problem and sorted based on their performance.
Columbia University GSAPP
ARCH A4845: Generative design
51. ‘pareto optimal front’
‘utopia point’
“Pill problem”
Multi-objective optimization
Columbia University GSAPP
ARCH A4845: Generative design
52. In multi-objective optimization, a design’s relative performance is based on its:
1. dominance rank
2. crowding distance
3. feasibility
Multi-objective optimization
Columbia University GSAPP
ARCH A4845: Generative design
53. y2
y1
a
b
c
d
e
f
g
h
i
j
k
l
m
n
A. Konak, D. W. Coit, A. E. Smith - Multi-Objective Optimization Using Genetic Algorithms: A Tutorial (2006)
Dominance principle
A solution A dominates another solution B if A performs at least as well as B in every
objective, and better than B in at least one objective.
Columbia University GSAPP
ARCH A4845: Generative design
54. b dominates h (it performs better in
both objectives)
b does not dominate a (it performs
better only in objective y2
)
y2
y1
a
b
c
d
e
f
g
h
i
j
k
l
m
n
A. Konak, D. W. Coit, A. E. Smith - Multi-Objective Optimization Using Genetic Algorithms: A Tutorial (2006)
A solution A dominates another solution B if A performs at least as well as B in every
objective, and better than B in at least one objective.
Dominance principle
Columbia University GSAPP
ARCH A4845: Generative design
55. y2
y1
F1
a
b
c
d
e
f
g
h
i
j
k
l
m
n
(1) optimal rank
A. Konak, D. W. Coit, A. E. Smith - Multi-Objective Optimization Using Genetic Algorithms: A Tutorial (2006)
The Pareto optimal set is the collection of solutions which are not dominated by any other
solution in the set. Solutions in this set are assigned a rank of 1.
Columbia University GSAPP
ARCH A4845: Generative design
56. y2
y1
F2
F1
a
b
c
d
e
f
g
h
i
j
k
l
m
n
(1) optimal rank
A. Konak, D. W. Coit, A. E. Smith - Multi-Objective Optimization Using Genetic Algorithms: A Tutorial (2006)
By temporarily ignoringW the first optimal set, a second optimal set can be formed.
Solutions in this set are assigned a rank of 2.
Columbia University GSAPP
ARCH A4845: Generative design
57. y2
y1
F3
F4
F2
F1
a
b
c
d
e
f
g
h
i
j
k
l
n
m
(1) optimal rank
A. Konak, D. W. Coit, A. E. Smith - Multi-Objective Optimization Using Genetic Algorithms: A Tutorial (2006)
This procedure can be repeated to generate the ranking of all solutions.
Columbia University GSAPP
ARCH A4845: Generative design
58. y2
y1
F1
i+1
i-1
i
a
b
c
d
e
(2) crowding distance
Kalyanmoy Deb, et al. - A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II (2002)
To increase diversity in the population, solutions are also assigned a crowding distance
based on the distance between the two adjacent solutions in the same rank across all
objectives.
Columbia University GSAPP
ARCH A4845: Generative design
59. y2
y1
F1
i-1
i+1
i
a
b
c
d
e
(2) crowding distance
Solutions with a larger crowding distance are preferred because they represent less
explored areas of the design space.
b has a larger crowding distance
than d
Kalyanmoy Deb, et al. - A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II (2002)
Columbia University GSAPP
ARCH A4845: Generative design
61. Selection
Solutions are selected for crossover by participating in a tournament where the best of
two randomly chosen designs is selected according to the following rules:
Columbia University GSAPP
ARCH A4845: Generative design
68. Galapagos Octopus Discover
Multiple inputs X X X
Input type: float X X X
Input type: integer X
Input type: permutation X
Multiple objectives X X
Crowding distance - X
Constraints X
Optimization software feature comparison
Columbia University GSAPP
ARCH A4845: Generative design
70. Nagy, et al. - Mining the Evolutionary Optimization Process to Discover Novel Design Strategies (2017)
Multi-objective optimization
Columbia University GSAPP
ARCH A4845: Generative design
71. Nagy, et al. - Mining the Evolutionary Optimization Process to Discover Novel Design Strategies (2017)
Multi-objective optimization
Columbia University GSAPP
ARCH A4845: Generative design
72. Nagy, et al. - Mining the Evolutionary Optimization Process to Discover Novel Design Strategies (2017)
Multi-objective optimization
Columbia University GSAPP
ARCH A4845: Generative design
73. Nagy, et al. - Mining the Evolutionary Optimization Process to Discover Novel Design Strategies (2017)
Multi-objective optimization
Columbia University GSAPP
ARCH A4845: Generative design
74. Nagy, et al. - Mining the Evolutionary Optimization Process to Discover Novel Design Strategies (2017)
Multi-objective optimization
Columbia University GSAPP
ARCH A4845: Generative design