Lecture 3 - Luke Flashcards by Will Thomas

Three properties of biological systems which make them key for inspiration

Robust
Complex
Adaptive

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What is the use of evolutionary computation?

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

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Define a population

Group of several individuals

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Define diversity

Different characteristics between individuals

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Define heredity

Characteristics passed down through generations

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Define Natural selection

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

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What is a genotype?

Genetic material that makes up the organism.

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How is a genotype transmitted and what affects it?

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

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What is a phenotype?

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

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Genotype codes for..

Phenotype

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What is DNA

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

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What are the four nucleotides in DNA?

Adenine, cytosine, guanine and thymine.

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What does the sequence of nucleotides code for?

Different forms of protein, mediated by mRNA.

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What are the two regions of a gene?

Regulatory and Coding

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Two types of cell replication, and a brief description.

Mitosis: Growth of cell
Meiosis: Production of sex cells

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In what cells can mutations affect evolution?

SEX

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Brief explanation of artificial evolution

Automatic generation of solutions to hard problems

Similarities between natural and artificial evolution

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

Differences between natural and artificial evolution

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.

Approach to an evolutionary algorithm

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

Three conditions for stopping evolutionary algorithm

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

Operators and Operands involved in tree-based representations?

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

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

What is sufficiency with regards to tree based representations

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

What is closure with regards to tree based representations

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

How do you decide on initial populations?

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

Describe the initial populations for trees

``` 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. ```

What is the role of the fitness function

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

What is subjective fitness?

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

What is the difference between explicit and implicit fitness functions?

Explicit fitness functions evaluate every detail and implicit is more general

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

To make sure that better individuals make comparatively more offspring

Selection pressure is inversely proportional to what?

Number of individuals selected

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

A loss of diversity, and premature convergence

What are the problems of proportional selection?

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

Five types of selection

``` Proportional rank based tournament truncated rank based replacement selection ```

How does truncated rank based work

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.

How does the size of the tournament affect selection pressure?

Larger K higher selection pressure

Three types of replacement selection

``` 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) ```

Three types of crossover

Single point Uniform arithmetic

What is a fitness landscape

PLot of fitness values associated to all genotypes

How to estimate the ruggedness of real landscape?

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

What can the stagnation of fitness function suggest?

Best solution found or premature convergence

Applications of evolutionary algorithms

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