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Week 6 - AI - Case Study 2 Flashcards | StudBud
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Week 6 - AI - Case Study 2
Week 6 - AI - Case Study 2
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1
Choose the suitable genetic algorithm for genotype
Real world behaviour
Fitness evaluation
Encoded solution
Iterative improve of solutions
2
How does the organism functions and interacts with its environment?
DNA
Phenotype
Genotype
3D structure
3
What roles do proteins play in the expression of genetic information? Select all that apply:
Proteins are formed directly from DNA without any intermediaries.
Proteins fold into 3D structures that determine traits and behavior.
Proteins are only used in muscle and tissue, not in behavior.
Proteins are built from amino acids translated from DNA codons.
4
Randomly flipping bits in offspring to introduce variation is called __________.
encoding
mutation
crossover
reproduction
5
What are the steps in the genetic algorithm process?
Init → Crossover → Repeat → Eval → Mutation
Init → Eval → Select → Crossover → Mutation
Init → Crossover → Select → Eval → Mutation
Eval → Crossover → Select → Init → Mutation
6
What is an example of selection pressure
A math test in school
Climbing a hill to get food
Reading a book quietly
A game of chess
7
Select true statement:
Frank Rosenblatt - Focus on coordination systems
John Holland - Credited with formalizing genetic algorithm
Norbert Wiener - Father of cybernetics
Chris Langton - Inventor of Perceptron
8
What does "Phenotype" represent in evolving morphology?
The actual 3D physical creature
A fitness score
The graph of ideas
A random mutation
9
What is an example of computational parallelism in genetic algorithms?
Freezing the genotype of one parent
Running the GA on paper
Evaluating the fitness of multiple individuals simultaneously
Performing crossover and mutation in isolation
10
Which is considered as a major subfield of AI?
Machine Learning
Robotics
Optimization
Development
11
Choose the right word quited by Chris Langton and Nils Aall Barricelli on the core idea of atificial life.
"Every problem is solved by yet another indirection."
"Life as it could be."
"Pride makes us artificial and humility makes us real"
"No one can stop progress of artificial intellligence"
12
How does the population adapt to the environment?
Mutation
Evolves
Crossover
Inheritable
13
Describe the term for variation
Difference among individuals in a population
The ability to pass traits to offspring
Encoded genetic information
Success of a phenotype in a given environment
14
What is "Evolving Morphology"?
Changing colors of animals
Manually building robot shapes
Using genetic algorithms to evolve physical forms
Using AI to draw images
15
What does it mean for having implicit parallelism?
they can be parallelized on multiple processors or threads.
Use the roulette wheel selection process.
Keep genetics diversity and allow multiple "bloodline" to co-exist.
maintaining and evolve multiple solutions at once.
16
Why repeated evolution was interative in improving population over the time?
It explores more solutions and gets closer to optimal options.
All of the above
GAs rely on improvement via selection, crossover, and mutation.
Each generation's mutations help enhance fitness.
17
Genotype is represent by using?
Nodes
Body parts
Connection between parts
Edges
18
What did John Holland achieved?
Father of cybernetics
Pioneer of artificial life movement
Credited with formalizing genetic algorithms
Inventor of the perceptron
19
Which following answer is correct regarding the breakthrough of DeepMind's AI system AlphaFold?
Encoded genetic information
Ability to pass traits to offsprings.
AI Model capable of predicting 3D protein structure from DNA.
Key Milestone in bio-AI convergence.
20
Who coined the term "cybernetics"?
Frank Rosenblatt
Chris Langton
Nobert Wiener
John Holland
21
In Roulete Wheel Selection, Each individual is given a slice of virtual wheel ______________________
Based on the genertic diversity among the communities.
Follow its mutation rate.
randomly assigned regardless to fitness.
Proportional to Fitness.
22
What is the definition for search space complexity?
Real world problems often exist in high dimensional spaces
Search across the entire space
GAs preserve and recombine good schemas across dimensional
Sampling sections across multiple space
23
What is the purpose of bio-inspired computing?
To simulate emotions in AI
Natural processes for computational tasks like optimization and learning
To model algorithms using social behavior
To enhance cybersecurity systems through brute-force learning
24
What is the definition of Selection in Genetic Algorithm Process?
Randomly flip bits in offspring to add variation
Randomly generate initial population of solutions
Combine parts of two parents to create offspring
Choose individuals for breeding
25
Conputational Parallelism can improve which part of the threads?
Reducing data transfer between storage devices.
Correctly predict and coordinate the timing of thread execution.
Increasing an amount of data stored in memory.
Improves computational and execution speed.
26
Choose the correct answer that is True about 3 main stages of Evolutionary cycles.
Different head and leg sizes show variation and heredity.
New individuals help assess fitness in a population.
Selection & breeding combine best parents to produce offspring.
Testing randomly alters individuals to add diversity.
27
Which schema is true pattern in a genotype in Schema Theorem?
1**0*
**0
All of the above
01**0*
28
Choose the correct sequence of steps in a genetic algorithm.
vi, ii, i, iii, iv, v
iii, v, iv, i, vi, ii
v, i, iii, iv, ii, vi
vi, i, ii, v, iii, iv
29
In the evolutionary cycle, what happens during the “Testing (Selection Pressure)” stage?
DNA mutations are introduced.
Offspring are produced by asexual reproduction.
Individuals are evaluated based on performance in a fitness challenge.
Organisms are randomly selected for reproduction.
30
What are the component of the Neural-like Network control system?
Inputs
Action
Network
Outputs
31
What is the definition of Mutation in Genetic Algorithm Process?
Convert genotypes -> phenotypes, evaluate fitness
Randomly flip bits in offspring to add variation
Continue through multiple generations until a stopping condition is met
Randomly generate initial population of solutions
32
Which following are connected to the application to Genetic Algorithm?
Statics rules based expert system with observable traits.
Data encryption using quantum mechanics.
Fitness evaluation and real-world behaviour modeling.
Traditional sequencial programming that process without equation.
33
The purpose of hyperplane sampling
Explain how useful genetic structures
Allow combining partial solutions across different genotypes
Understand the problem of limited search in simple algorithm
Multiple evolving subpopulations maintain diversity
34
A genetic algorithm is a __________ search algorithm inspired by natural evolution.
heuristic
probabilistic
deterministic
recursive
35
The role of evolving morphology
Allows AI to design its own physical form
Enables recursive, modular body generation
Set the foundation for modern morphological AI and evolutionary robotics
Evolved network that enables locomotion
36
How many dimensional spaces complexity that often happned in real-world?
10000000
Depend on the situation.
1 billion
100+
37
What is the description of inputs?
Emerges from interaction between control system and morphology
Motor command to actuators
A directed graph
Sensors
38
Who is Father of cybernetics?
Norbert Wiener
Chirs Langton
John Holland
W.Ross Ashby
39
What is the definition of Crossover in Genetic Algorithm Process?
Randomly generate initial population of solutions
Convert genotypes -> phenotypes, evaluate fitness
Combine parts of two parents to create offspring
Continue through multiple generations until a stopping condition is met
40
Hyperplane Sampling often involving:
i, ii
i, iv
iii, iv
ii, iv
41
What was Frank Rosenblatt's contribution in late 1960s?
He introduced convolutional neural networks for image processing.
Developed backpropagation algorithm used in training dnn
He designed the first computer to defeat a human in chess.
He invented the perceptron, an early model of a neural network.
42
What is the main purpose of the Evolution Theory Crash Course in relation to Genetic Algorithms?
To analyze protein structures for medical research
To explain how to build DNA models in a lab
To introduce biological evolution concepts that inspire GA in computing
To teach the anatomy of living organisms
43
What are the specification of Search Space Complexity?
Non-linear
Irregular
Full of local maxima
All
44
What was the name of Karl Sims’ 1994 paper?
Robotic AI Forms
The Evolution Machine
Evolving Virtual Creatures
Morph AI Simulation
45
Biological processes such as:
Evolution
Self-regulation
Genetices
Neural Activity
46
In the airplane wing design example, how many possible wing designs exist with a 9-bit genotype?
512
256
1024
128
47
According to the Swarm and Evolutionary Computation journal review, what year was bio-inspired computing part of AI?
2019
2020
2013
2015
48
What is the main goal of a genetic algorithm
To draw pictures
to copy human DNA
To delete random data
to find the best or nearly best solution in a large search space
49
What does 'fitness' mean in genetic algorithm
how fast you can change
How much DNA you have
How well you do in a test
How long you live
50
What is the definition of Evaluation in Genetic Algorithm Process?
Randomly generate initial population of solutions
Choose individuals for breeding
Combine parts of two parents to create offspring
Convert genotypes -> phenotypes, evaluate fitness
51
What was the name of the supercomputer used in Karl Sims’ 1994 work?
Titan-X
IBM Watson
Connection Machine CM-5
Deep Blue
52
In 1994, __ is developed on being the fastest supercomputer at the time
IBM
DeepFive
Connection Machine CM-5
Agent57
53
Which of the following is NOT listed as a biological process that inspires bio-inspired computing?
Natural selection
Evolution
Self-regulation
Neural activity
54
According to schema theory, what is a schema in genetic algorithms?
A specific solution that is optimal
A pattern in a genotype (e.g., 1**0*)
A high-level overview of population fitness
A hardware-accelerated algorithm
55
What was the purposes of the studies Evolving Virtual Creatures (1994)?
Creating interaction between morphology and control systems.
Using machine learning to create 3D virtual environments.
How morphology, control, fitness & mutation work in computation.
Showing how fit patterns evolve to be preserved and amplified.
56
What is the meaning for fitness?
Find optimal or near optimal solution
Decoded solution
Encoded string representation
Evaluation of the phenotype
57
In the airplane wing example, what does the fitness test measure ?
the color of the wing
how long the wing is
how much lift the wing creates
the shape of the wing
58
The mutation often prevent premature convengergence and maintains _________________
rapid convergence to local optima and propotional to fitness.
homogeneity among solution
Overfitting in training model
Genetic Diversity
59
The genetic algorithm continues evolving the population until a __________ is met.
perfect genotype
fixed phenotype
random mutation
stopping condition
60
What is the definition of Repeat in Genetic Algorithm Process?
Combine parts of two parents to create offspring
Convert genotypes -> phenotypes, evaluate fitness
Randomly flip bits in offspring to add variation
Continue through multiple generations until a stopping condition is met
61
What is the 3 main stages of evolutionary cycle?
Testing (Selection Pressure)
Variation & Heredity
Selection & Breeding
DNA
62
What is the difference between genotype and phenotype?
Genotype is DNA; phenotype is observable traits result of DNA & env
Genotype is behavior; phenotype is genetic code
Genotype is observable traits; phenotype is environmental data
Genotype changes frequently; phenotype is constant
63
According to Goldberg & Holland, a Genetic Algorithm is best described as:
A deterministic algorithm for small datasets
A manual search process for model tuning
A rule-based system for logic inference
A probabilistic search procedure for large spaces using strings
64
Which of the following is not a fitness function used in the simulation?
Walking distance
Jumping height
Swimming efficiency
Light-seeking
65
What is a key limitation of simple search algorithms like hill-climbing?
They get stuck in local maxima
They require large amounts of memory
They always converge to global optima
They do not work with binary data
66
What happens during the final step of the Genetic Algorithm process?
Randomly generate initial population of solutions
Continue through multiple generations until a stop condition is met
Choose individuals for breeding
Randomly flip bits in offspring to add variation
67
What type of graph supports recursive, modular body design?
Directed graph
Flowchart
Circular graph
Pie chart
68
What are causes of differences between offspring and their parents in GA?
Mutation
All
Selection Pressure
Crossover
69
Which of the following are not definitions of bio-inspired computing?
Computational designs using natural metaphors
Interdisciplinary field combining biology and AI
Algorithms inspired by organisms like ants & bacteria
Method using biological hardware for computing
70
According to the notes, what does DeepMind's AlphaFold AI model predict
Protein folding structures from DNA sequences
Breeding success in artificial organisms
DNA mutations over generations
Behavior patterns based on environment
71
What does a "Genotype" represent in morphology?
A directed graph of body parts and connections
The sensor input system
The creature’s personality
A score of how well the creature walks
72
Graph is expressed by
body form
phscyological form
pyhsical form
graphical form
73
Richard Dawkins "Biomorphs" demonstrate the ___________________ via simples rules and based on user selection.
Morphology Representation
Evolution of Complexity
Fitness Function
Control System
74
According to the 2019 Swarm and Evolutionary Computation journal review, what has bio-inspired computing emerged as?
One of the most studied branches of AI
A tool for building mobile apps
A fading trend in AI history
A minor concept in genetic research
75
Who is credited with inventing the Perceptron, laying the foundation for modern deep learning?
Chris Langton
Frank Rosenblatt
John Holland
Norbert Wiener
76
What terms described the meaning of Phenotype?
Environmental test determining fitness.
Expressed traits depend on which living organism were tested.
Differences among individuals in a population.
The ability to pass traits to offspring.
77
What is the definition of Initialization in Genetic Algorithm Process?
Continue through multiple generations until a stopping condition is met
Convert genotypes -> phenotypes, evaluate fitness
Randomly flip bits in offspring to add variation
Randomly generate initial population of solutions
78
What distinguishes genotype from phenotype?
Genotype is behavior, phenotype is DNA
Genotype = DNA, phenotype = physical traits
Genotype changes over time, phenotype is fixed
Genotype is visible traits, phenotype is inherited
79
Choose correct bio sequence from DNA to phenotype use as inspiration in GA?
C → B → A → D
C → A → B → D
A → B → C → D
B → C → D → A
80
How does roulette wheel selection contribute to genetic diversity?
It gives weaker individuals a chance to reproduce
It randomly selects individuals without fitness consideration
It selects only the best-performing individuals
It eliminates weak individuals early
81
What task of fitness function that demonstrated the provimity to a light source for simulation?
Walking
Swimming
Competition
Light-seeking
82
What is the correct order of the three main stages in evolutionary cycle
Breeding → Fitness → Variation
Variation & Heredity -> Testing -> Selection & Breeding
testing -> selecting & breeding -> variation
Heredity → Mutation → Fitness
83
Which field is associated with simulating life-like systems such as self-replicating organisms?
Robotics
Artificial Life (A-Life)
Cybernetics
Genetic Algorithms
84
Which are components of the genotype → phenotype → fitness cycle in GA?
C → B → A
B → C → A
A → B → C
C → A → B
85
Why is implicit parallelism important in genetic algorithms? Select all that apply.
It ensures diversity in the population
It makes the algorithm deterministic
It allows hardware-level parallel computing
It enables multiple schemas to evolve at once
86
What does the "crossover" step do in genetic algorithms
Add random changes
Mix two parent solutions to make a new one
Choose the strongest solution
Change the fitness score
87
Which of the following is NOT one of the three main stages of the evolutionary cycle?
Variation & Heredity
Mutation Analysis
Testing
Selection & Breeding
88
The year Connection Machine CM-5 is created in?
1965
1994
2004
1984
89
What it means by computional parallelism?
Improve computional speed
Implementation level parallelism in hardware and software
Evaluating fitness of multiple individuals in a generation
GAs evaluation can be parallelized on multiple processors or threads
90
What does implicit parallelism mean?
GAs evaluation can be parallelized
Keep genetic diversity
GAs maintain and evolve multiple selections in parallel
Give weaker solutions a chance too
