Evulotionary Optimization Flashcards

1
Q

In evulotionary optimization the problem to be solved must be ___

A

well defined

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2
Q

The comparison between two or more candidate solutions are based on ___ of how well a proposed solution meets the ___ of the problem

A

quantitive measures

needs

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3
Q

Interactive evolutionary computation is when a human provides a judgement about ___
Here we usually find the use of ___

A

. the quality of proposed solutions

. qualitative or even fuzzy descriptors

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4
Q

In quantitative evolutionary optimization, is a ___ that operates on a potencial ___ and returns either a single real number or multiple numbers that describe the value of the solution

A
function 
solution
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5
Q

There are two forms of optimization problem:
1 - ___
2- ___

A

1- numeric

2- combinatoric

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6
Q
In a Global Numerical Optimization we:
1- \_\_\_ 
2- \_\_\_
3- \_\_\_ 
4- \_\_\_
5- \_\_\_
A
1- Initialize Population
2- Create Offspring
3- Score Everyone
4- Select new parents
5- repeat to 2 until done
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7
Q

In a Global Numerical Optimization we should stop if:
1- The best solution is below a ___
2- The best solution is not ___

A

1- pre-defined threshold

2- evolving for a pre-defined time (number of generations)

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8
Q

When we generate offsprings from mutation we apply a variation on each dimension and is based on only ___

A

one parent

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9
Q

When we generate offsprings from recombination, it is based on more ___ by using ___ and ___

A

. than one parent

. crossover and belnding

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10
Q

In one point crossover we basicly make a ___ on the parents ___

A

cut

vectors

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11
Q

In a multipoint crossover we basicly make several ___ on the parents ___

A

cuts

vectors

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12
Q

In a uniform crossover we select one ___ from either parent at ___ without regard to maintaining ___.

A

. component
. random
. continuous segment

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13
Q

In a Blending we can use for example a ___ to blend two parents

A

mean

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14
Q

If the problem at hand presents a smooth, convex, continuous landscape thengradient or related methods of optimization will be ___ in locatting the ___

A

. faster

. single optimum point

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15
Q

if the problem presents a landscape with

multiple local optima, then the gradient methods will likely fail to find the ___

A

global optimum

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16
Q

If the landscape is discontinuous and/or not
smooth, then gradient-based approaches
may be ___

A

inapplicable

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17
Q

STUDY SLIDE 19 to SLIDE 20 (inclusive)

A

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18
Q
Some mutation alternatives to create offsprings are:
1- \_\_\_
2- \_\_\_
3- \_\_\_
4- \_\_\_
A

1- Select and replace
2- Invert
3- Protect and randomize
4- Partially mapped crossover

19
Q

Convergence with probability 1 happens with an Elistic selection and a mutation operator that can ___

A

reach all possible states

20
Q

Premature convergence occurs when a population becomes ___ at a solution that is not the ___

A

. homogeneous

. the global optimum

21
Q

When selecting a representation it should:
1- Optimally provide ___ about the solution
2- Be ___ to ___ operators

A

1- immediate information

2- amenable / variation

22
Q

Selection describes either:
1- The process of ___ solutions from an existing ___
2- Or making proportionally more ___ from certain ___

A

1- selecting/ population

2- offspring / parents

23
Q
Some forms of selection are:
1- \_\_\_
2- \_\_\_
3- \_\_\_
4- \_\_\_
A

1- plus/comma
2- proportional
3- tournament
4- linear ranking

24
Q

Plus/Comma selection includes allowing ___ to ___ to a maximum of n generations

A

. parents

. survive

25
Proportional Selection or Roulette Wheel Selection picks parents for ___ in proportion to their relative ___
reproduction | fitness
26
In Plus selection both ___ and ___ are ___ of the next generation
parents and offsprings | parents
27
In Comma selection only ___ are considerer for parents in the next generation
offsprings
28
In Tournament Selection we selct a number of individuals to be ___
a member of the next generation
29
In Linear Ranking Selection we map individuals to selection ___ according to a ___
probabilities | prescribed formula
30
Elistic selection can be applied to ensure that the ___
best solution in a | population is retained in the next generation
31
The harder the selection, the faster the ___ can ___
better solutions | overtake the population
32
If we are using evolutionary algorithms that relie heavily on recombination, then ___ is required for the population to ___
diversification | search new solutions
33
Some types of variations are: 1- ___ Variation 2- ___ Operators 3- Variations on ___
1- Real-Valued 2- Multiparent Recombination 3- Variable-Length Structures
34
Real-Valued Variation uses ___ mutation operator
Gaussian
35
Some of the constraints of the problem are part of the ___, whereas some are part of the ___
objective | parameters of a solution
36
A Hard constraint is one that, if violated, makes the entire proposed solution ___
worthless
37
A Soft constraint is one that can be violated, but there is some imposed ___. This can increase with the degree of ___
penalty | violation
38
When thinking about constraint handling, it is helpfull to craft an objective function that involves the primary criteria of interest and also involves a ___ that treats constraint violations
penaly function
39
The 1/5 rule ilustrates that static parameters for variation operators are very unlikely to lead to the ___
best rates of progress
40
Meta-Evolution is a commom method that views the parameters that control the ___ as part of the ___
. evolutionary search | . evolutionary process
41
Meta-evolution can be extended to ___ that are not in the continuous domain.
support variation operators
42
Multiparent Recombination Operators (type of variation) recombines ___ or ___ of multiple solutions
elements or blend parameters
43
Variations on Variable-Length Structures (type of variation) represents the variation on ___
data structures of variable length