Lecture 05 - Genetic Algorithms Flashcards by Rikard Donnelly

Genetic Algorithms

Who were the main 3 communities developing genetic algorithms?

Biologists, computer scientists/engineers, artificial-life researchers

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Genetic Algorithms

Why is there terminology overlap within evolutionary computing?

Competing communities of research hasn’t properly converged on terminology.

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Genetic Algorithms

What is an individual in GA?

One data point with an attached fitness, which can evolve into new offspring.

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Genetic Algorithms

What is a population in GA?

A collection of individuals to be evaluated.

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Genetic Algorithms

How are individuals represented internally?

Pretty much anything that can be evaluated with a fitness function.

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Genetic Algorithms

What is direct coding?

Some specific representation, like a final image. Genotype == phenotype in this case.

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Genetic Algorithms

What is indirect coding?

Some abstract, recipe-y way of making the individuals.

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Genetic Algorithms

What is a genotype?

Genotypes are the instruction for creating an individual.

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Genetic Algorithms

What is a phenotype?

A phenotype is a manifested version of a genotype. Humans are phenotypes; genes are genotypes.

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Genetic Algorithms

Why do we use the genotype/phenotype distinction in GAs? (3)

To resemble nature
To ease manipulation
To allow reuse, hence enabling actual usage of EC

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Genetic Algorithms

What is the concept of “limited resources” in GA?

The population size is capped; not all individuals can survive.

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Genetic Algorithms

What’s the difference between an overlapping and non-overlapping generational model?

Overlapping model: Both parents and offspring can survive.
Non-overlapping model: Only offspring survive.

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Genetic Algorithms

What are some problems with the overlapping model?

Parents can survive, so what is a “new generation?”

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Genetic Algorithms

When using an overlapping model, how do you measure time flowing?

Track the number of births. A new generation happens at “#m births”.

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Genetic Algorithms

Name the most common fitness selection methods. (FRUTT)

Fitness-proportional selection
Rank-proportional selection
Uniform (neutral) selection
Truncation selection
Tournament selection

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Genetic Algorithms

Describe Fitness-proportional selection.

1) Given the numerical fitness of each individual
2) Randomly pick one individual with probability proportional to the fitness (the better, the larger probability)

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Genetic Algorithms

Describe Rank-proportional selection.

1) Given the rank of each individual in a fitness-based ranking
2) Randomly pick one individual with probability proportional to the rank (the better, the larger probability)

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Genetic Algorithms

Describe uniform selection.

Pick randomly an individual (with uniform probability)

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Genetic Algorithms

Describe Truncation selection

Pick the best individual(s?) (elitism)

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Genetic Algorithms

Describe Tournament selection

Given a parameter n_size (size of the tournament):
1) Randomly (with uniform probability) pick n_size individuals
2) From them, choose the one with the best fitness

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Genetic Algorithms

How do you determine if criterion A better than criterion B?

Hypothesis testing

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Genetic Algorithms

Does uniform selection prefer fit or unfit individuals?

No preferences for either.

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Genetic Algorithms

Does truncation selection prefer fit or unfit individuals?

Strong preference for fit individuals

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Genetic Algorithms

Does tournament selection prefer fit or unfit individuals?

Depends on n_size.
If 1: No preference.
As n_size grows -> stronger preference for fit individuals.

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# Genetic Algorithms What does a strong preference for fit individuals lead to?

- Converges to fit individuals. - Evolutions concentrates on improving promising solutions (exploitation). - Risk of falling into local minima.

# Genetic Algorithms What does a weak preference for fit individuals lead to?

- Population includes unfit individuals. - Many different, possibly not promising solutions, are explored (exploration). - Risk of not finding good solutions.

# Genetic Algorithms What are the two most common genetic operators

Crossover and mutation.

# Genetic Algorithms What is n-point crossover?

See the image. Select n points and flip which parent the genes come from.

# Genetic Algorithms What is variable-length crossover?

Offspring can have variable length output which are concatenations of various parts of the parents.

# Genetic Algorithms How does mutation work on trees?

Replace a random subtree with another randomly generated subtree.

# Genetic Algorithms How does crossover work for trees?

Select two parents, then swap a random subtree.

# Genetic Algorithms How are genetic operators constrained for trees?

Usually by depth.

# Genetic Algorithms What is gaussian mutation?

Add parameterized gaussian noise.

# Genetic Algorithms What is geometric crossover/crossover for real-valued vectors?

c = p1 + C*(p1 - p2), for C ~ U(0, 1)

# Genetic Algorithms What's the drawback of geometric crossover?

It lacks the ability to explore outside of the hyperrectangle enclosing the population

# Genetic Algorithms What's the hypothesized role of mutation and crossover?

- Mutation = exploitation - Crossover = exploration (Debated)

# Genetic Algorithms What techniques can you use to solve multiobjective problems?

- Linearization - Lexicographical order - Pareto dominance

# Genetic Algorithms What is linearization for multiobjective problems?

Weight and sum each objective value.

# Genetic Algorithms What is lexicographical order for multiobjective problems?

Check each pair of objectives in turn. 1) Check f1(i1) > f1(i2). Select winner or advance to (2). 2) Check f2(i1) > f2(i2). Select winner or advance to (3). ...

# Genetic Algorithms What is the drawback of too little diversity?

Too much exploitation → local minimum

# Genetic Algorithms What is the drawback of too much diversity?

No exploitation, just coarse exploration (random walk)

# Genetic Algorithms What is Expressiveness?

Whether good solutions can be expressed as phenotypes, e.g. the number of dimensions.

# Genetic Algorithms What's the problem with Low expressiveness?

Good/optimal solution might not be representable, or might not be reachable

# Genetic Algorithms What's the problem with Large expressiveness?

Large search space → very long or infiniti convergence time

# Genetic Algorithms What is Grammatical Evolution?

Evolutions of strings given a grammar. See the image for an example.

# Genetic Algorithms What does the Φ operator do?

Maps a genotype to a phenotype.

# Genetic Algorithms What is ⊥?

An invalid solution.

# Genetic Algorithms What is an invalid solution?

An illegal phenotype.

# Genetic Algorithms What is f(i_j)?

The fitness function applied to the j-th individual.

# Genetic Algorithms What is ≺?

A partial ordering of fitness values, i.e. fitness values can be compared and sorted.

# Genetic Algorithms What is the distance dG?

A distance measure between two genotypes. (See image)

# Genetic Algorithms What is the distance dP?

The distance measure between two phenotypes. (See image)

# Genetic Algorithms What is invalidity?

The proportion of genotypes that are mapped to invalid phenotypes.

# Genetic Algorithms What is Degeneracy?

The ratio of genotypes that are mapped to the same phenotype.

# Genetic Algorithms What's a synonym for degeneracy?

Redundancy

# Genetic Algorithms What is Uniformity of degeneracy?

The degree to which the sizes of different sets of genotypes mapping to the same phenotype differ:

# Genetic Algorithms What is Redundancy?

Parts of the genotype might not "agree" when being mapped to phenotypes, e.g. redundant genes might map to the same phenotype.

# Genetic Algorithms What is locality?

The degree to which close genotypes are mapped to close phenotype

# Genetic Algorithms What is evolvability?

The likelihood of obtaining a better individual after the application of a genetic operator

# Genetic Algorithms Describe pareto dominance

1) Start with a graph with points. 2) While the graph has unselected points, repeat 3-6. 3) Select one of the outermost points on the graph that doesn't belong to a pareto frontier. 4) Sweep a (hyper)plane around that point. 5) If you hit a point, select that as the next point. 6) If you hit an axis, assign a new pareto frontier to the selected points and move to step 2.