Genetic AlgorithmsGenetic Algorithms

Logical and Artificial Intelligence in Games

Lecture 14

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Genetic AlgorithmsGenetic Algorithms

What are they?

Evolutionary algorithms that make use of operations like mutation, recombination, and selection

Uses?

Difficult search problems

Optimization problems

Machine learning

Adaptive rule-bases

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Theory of EvolutionTheory of Evolution

Every organism has unique attributes that can be transmitted to its offspring

Offspring are unique and have attributes from each parent

Selective breeding can be used to manage changes from one generation to the next

Nature applies certain pressures that cause individuals to evolve over time

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Evolutionary PressuresEvolutionary Pressures

Environment

Creatures must work to survive by finding resources like food and water

Competition

Creatures within the same species compete with each other on similar tasks (e.g. finding a mate)

Rivalry

Different species affect each other by direct confrontation (e.g. hunting) or indirectly by fighting for the same resources

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Natural SelectionNatural Selection

Creatures that are not good at completing tasks like hunting or mating have fewer chances of having offspring

Creatures that are successful in completing basic tasks are more likely to transmit their attributes to the next generation since there will be more creatures born that can survive and pass on these attributes

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GeneticsGenetics

Genome 基因組 (class)

Sequence of genes describing the overall structure of the genetic for a particular species

Genomics

Study of the meaning of the genes for a particular species

Alleles 等位基因 Values that can be assigned to a given gene (individual creature)

Genotype (instance)

Sequence of alleles

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Physical PropertiesPhysical Properties

Phenetics 表型學 Study of physical properties and morphology 形態 of creatures independent of genetic information

PhenomeGeneral structure of creatures body and attributes

Phenotype 表現某一顯性特徵之生物個體 Particular instance of phenome realized as a unique creature

Product of genotype and environment forces

ExamplesPhenome: Hair color, skin tone, height

Phenotype: Black, Dark, 1.75 m

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ConversionsConversions

In real-world mapping between genotypes and phenotypes is hard

In AI work it can be done by defining a convenient function or even designing encodings by hand

It is often easier to adapt genetic operators to work with the evolutionary data structure used to represent the phenotype than to encode and decode phenotypes

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Genetic Algorithmic ProcessGenetic Algorithmic Process

Potential solution for problem domains are encoded using machine representation (e.g. bit strings) that supports variation and selection operations

Mating and mutation operations produce new generation of solutions from parent encodings

Fitness function judges the individuals that are “best” suited (e.g. most appropriate problem solution) for “survival”

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Genetic Algorithm Evolutionary Genetic Algorithm Evolutionary ProcessProcessHere’s the generic outline of the evolutionary process in Genetic Algorithm

Initialization – A population of individuals is created

Selection – Parents are picked from the population

Crossover – Their genetic code is combined together to form the child.

Mutation – A few genes of the offspring are changed arbitrarily.

Replacement – The offspring is potentially reinserted into the population

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InitializationInitialization

Initial population must be a representative sample of the search space

Random initialization can be a good idea (if the sample is large enough)

Random number generator can not be biased

Can reuse or seed population with existing genotypes based on algorithms or expert opinion or previous evolutionary cycles

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EvaluationEvaluation

Each member of the population can be seen as candidate solution to a problem

The fitness function determines the quality of each solution

The fitness function takes a phenotype and returns a floating point number as its score

It is problem dependent so can be very simple

It can be a bottleneck if it is not carefully thought out (there are no magic ways to create them)

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SelectionSelection

Want to to give preference to “better” individuals to add to mating pool

Mating can be sexual or asexual

If entire population ends up being selected it may be desirable to conduct a tournament to order individuals in population

Would like to keep the best in the mating pool and drop the worst (elitism)

Elitism is trade-off with search space completeness

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Crossover - 1Crossover - 1

In sexual reproduction the genetic codes of both parents are combined to create offspring

Asexual crossover has no impact on the mating pool

Would like to keep 60/40 split between parent contributions

95/5 splits negate the benefits of crossover (too much like asexual reproduction)

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Crossover - 2Crossover - 2

If we have selected two strings

A = 11111 and B = 00000

We might choose a uniformly random site (e.g. position 3) and trade bits

This would create two new strings

A’ =11100 and B’ = 00011

These new strings might then be added to the mating pool if they are “fit”

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MutationMutation

Mutations happen at the genome level (rarely and not good) and the genotype level (better for the GA process)

Mutation is important for maintaining diversity in the genetic code

In humans, mutation was responsible for the evolution of intelleigence

Example: The occasional (low probably) alteration of a bit position in a string

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OperatorsOperators

Selection and mutation

When used together give us a genetic algorithm equivalent of to parallel, noise tolerant, hill climbing algorithm

Selection, crossover, and mutation

Provide an insurance policy against losing population diversity and avoiding some of the pitfalls of ordinary “hill climbing”

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ReplacementReplacement

Determine when to insert new offspring into the mating pool and which individuals to drop out based on fitness

Steady state evolution calls for the same number of individuals in the population, so each new offspring processed one at a time so fit individuals can remain a long time

In generational evolution, the offspring are placed into a new population with all other offspring (genetic code only survives in kids)

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Genetic AlgorithmGenetic Algorithm

Set time t = 0

Initialize population P(t)

While termination condition not met

Evaluate fitness of each member of P(t)

Select members from P(t) based on fitness

Produce offspring form the selected pairs

Replace members of P(t) with better offspring

Set time t = t + 1

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Why use genetic algorithms?Why use genetic algorithms?

They can solve hard problems

Easy to interface genetic algorithms to existing simulations and models

GA’s are extensible

GA’s are easy to hybridize

GA’s work by sampling, so populations can be sized to detect differences with specified error rates

Use little problem specific code

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GA StrengthsGA Strengths

Do well at avoiding local minima and can often times find near optimal solutions since search is not restricted to small search areas

Easy to extend by creating custom operators

Perform well for global optimizations

Work required to to choose representations and conversion routines is acceptable

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GA WeaknessesGA Weaknesses

Do not take advantage of domain knowledge

Not very efficient at local optimization (fine tuning solutions)

Randomness inherent in GA make them hard to predict (solutions can take a long time to stumble upon)

Require entire populations to work (takes lots of time and memory) and may not work well for real-time applications