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Bio-AI > Lecture 3 - Luke > Flashcards

Lecture 3 - Luke Flashcards

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Biology 101 flashcards
1
Q

Three properties of biological systems which make them key for inspiration

A

Robust
Complex
Adaptive

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2
Q

What is the use of evolutionary computation?

A

To copy the process of natural evolution to automatically find solutions for complex problems.

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3
Q

Define a population

A

Group of several individuals

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4
Q

Define diversity

A

Different characteristics between individuals

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5
Q

Define heredity

A

Characteristics passed down through generations

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6
Q

Define Natural selection

A

Survival of the fittest. Those with beneficial characteristics are more likely to survive and reproduce and thus make more offspring.

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7
Q

What is a genotype?

A

Genetic material that makes up the organism.

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8
Q

How is a genotype transmitted and what affects it?

A

Transmitted through reproduction and it is affected by mutations. It is not however directly affected by selection

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9
Q

What is a phenotype?

A

The manifestation of the organism, behaviour and appearance for example.

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10
Q

Genotype codes for..

A

Phenotype

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11
Q

What is DNA

A

Long molecule, double helix structure composed of the binding of four nucleotides.

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12
Q

What are the four nucleotides in DNA?

A

Adenine, cytosine, guanine and thymine.

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13
Q

What does the sequence of nucleotides code for?

A

Different forms of protein, mediated by mRNA.

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14
Q

What are the two regions of a gene?

A

Regulatory and Coding

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15
Q

Two types of cell replication, and a brief description.

A

Mitosis: Growth of cell
Meiosis: Production of sex cells

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16
Q

In what cells can mutations affect evolution?

A

SEX

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17
Q

Brief explanation of artificial evolution

A

Automatic generation of solutions to hard problems

18
Q

Similarities between natural and artificial evolution

A 
Genotype
Phenotype (computer program, object shape, electronic circuit, robot, etc.) Population
Diversity
Selection
Inheritance
19
Q

Differences between natural and artificial evolution

A

With artificial there is an expected improvement between initial and final solution.
Selection in artificial is determined by a predefined measure, whereas in nature it is primarily survival and reproduction.

20
Q

Approach to an evolutionary algorithm

A 
Make a genetic representation
Build a population
Decide on a specific fitness function
Choose selection method
Choose crossover and mutation
Choose a data analysis method
21
Q

Three conditions for stopping evolutionary algorithm

A

Max fitness value found
solution found is good enough
no fitness improvement (stagnation)

22
Q

Operators and Operands involved in tree-based representations?

A

Operators - function set (multiplication, if-then, log etc)

Operands - terminal set (constants, variables, sensor readings etc.)

23
Q

What is sufficiency with regards to tree based representations

A

The elements in the function and terminal sets must be sufficient to generate a program that solves the problem

24
Q

What is closure with regards to tree based representations

A

All functions must accept all terminals in Terminal set and outputs of all functions in Function set

25
Q

How do you decide on initial populations?

A

Large enough to cover the problem space, but not too large in terms of evaluation costs

26
Q

Describe the initial populations for trees

A 
Trees are built recursively starting from root: root is randomly chosen from
function set; for every branch, randomly choose among all elements of function set and of terminal set; if terminal is chosen, it becomes leaf; set maximum depth of tree.
27
Q

What is the role of the fitness function

A

Evaluates the performance of a phenotype with a numerical score
(You get what you evaluate!)

28
Q

What is subjective fitness?

A

It is used to select the phenotype by visual inspection. Used when properties are not quantifiable, such as the aesthetic properties.

29
Q

What is the difference between explicit and implicit fitness functions?

A

Explicit fitness functions evaluate every detail and implicit is more general

30
Q

What is the purpose of the selection element of artificial evolution?

A

To make sure that better individuals make comparatively more offspring

31
Q

Selection pressure is inversely proportional to what?

A

Number of individuals selected

32
Q

What does a high selection pressure suggest in terms of diversity?

A

A loss of diversity, and premature convergence

33
Q

What are the problems of proportional selection?

A

If there are uniform fitness values, this results in a random search.
If a few individuals have high fitness values then this leads to a high selection pressure

34
Q

Five types of selection

A 
Proportional 
rank based
tournament 
truncated rank based
replacement selection
35
Q

How does truncated rank based work

A

Only the best x individuals are allowed to make offspring and each of them makes the same number of offspring: N/x, where N is the population size.

36
Q

How does the size of the tournament affect selection pressure?

A

Larger K higher selection pressure

37
Q

Three types of replacement selection

A 
Generation replacement (old population entirely replaced by offspring)
Elitism (maintain n best individuals from previous generation to prevent loss of best individuals by effects of mutations or   sub-optimal fitness evaluations)
Generation rollover (insert offspring at the place of worst individuals)
38
Q

Three types of crossover

A

Single point
Uniform
arithmetic

39
Q

What is a fitness landscape

A

PLot of fitness values associated to all genotypes

40
Q

How to estimate the ruggedness of real landscape?

A

Sample random genotypes: if flat, use large populations
Explore surroundings of individual by applying genetic operators in
sequence for fixed number of times: the larger the fitness improvement the easier is to evolve

41
Q

What can the stagnation of fitness function suggest?

A

Best solution found or premature convergence

42
Q

Applications of evolutionary algorithms

A 
Evolution of Neural Networks
Evolution of Cellular Automata
Evolution of hardware (tree based encoding)
Evolution of Controllers
Evolution of laws of physics

Bio-AI (10 decks)

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