Evolutionary Computation Introduction

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Evolutionary ComputationIntroduction

• Peter Andras
• peter.andras_at_ncl.ac.uk
• www.staff.ncl.ac.uk/peter.andras/lectures

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Overview

1. Biological inspiration
2. Artificial genes
3. Learning by evolution
4. Artificial evolution
5. Learning by artificial evolution

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Biological inspiration

• Evolution
• Darwin
• from bacteria to sponges, insects, fishes, and
  mammals
• from simple organs to complex ones
• from randomly spread neurons to highly organized
  large brains

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Biological inspiration

• Foundations
• nucleic acids adenin, citozin, guanin, timin,
  uracil
• DNA
• chromosomes
• genes
• RNA, proteins, cells

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Biological inspiration

• Adaptation by evolution
• ecological niche a set of ecological
  conditions (e.g., food resources, predators,
  other environmental risks, threats and
  opportunities)
• conquering new ecological niches (e.g., islands)
• development of new species that are able to use
  the opportunities provided by a new niche and
  avoid the related dangers

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Biological inspiration

• Adaptation
• development of new behaviours and organs
• new cells and cell behaviours
• new proteins
• new genes

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Artificial genes

• Idea
• copying natural evolution by emulating genes and
  their evolution
• Objective
• developing adaptive solutions of some problems

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Artificial genes

• Artificial world
• world of problems
• Artificial individuals
• solutions of the problems
• genes encode features of the problem solutions

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Artificial genes

• Discrete feature encoding
• e.g., 0 and 1 for the presence or absence of the
  features
• chromosomes 001110101110
• the genes do not represent necessarily full
  features

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Artificial genes

• Continuous type feature encoding
• e.g., features encoded by real numbers
• chromosomes multi-dimensional real vectors
• usually genes directly encode features

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Learning by evolution

• Learning
• learning adaptation
• adaptation optimisation
• optimisation criteria fitness in the given
  environmental conditions

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Learning by evolution

• Exchanging and combining genes
• sexual crossover

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Learning by evolution

• Mutation
• random changes of the genes

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Learning by evolution

• Inheritance
• the offspring inherits the properties of their
  parents
• some combinations are lethal
• the inherited properties range from similar
  proteins to similar behaviours

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Learning by evolution

• New species
• slow evolution
• accumulating minor changes
• modifications of organ functionality
• selection of some variants of standard features
  (e.g., feather colours)
• emergence of new behaviours, organs

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Learning by evolution

• Mating success
• features that better fit the environmental niche
  increase the chance of the individual to get
  mates and reproduce
• individuals with higher fitness have more
  offspring
• the genes of the successful individuals spread
  within the population and become dominant
• genes that cause evolutionary advantage in
  mutated individuals become general

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Learning by evolution

• Evolutionary optimisation
• increased fitness in the ecological niche
• mutation is responsible for new genes (proteins,
  cells, organs, behaviours)
• crossover is responsible for passing over the
  new genes
• fitness based mating success is responsible for
  the emergence of domination of genes that
  increase fitness

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Artificial evolution

• Evolution of a population of problem solutions
• individuals are the problem solution
• each solution is characterized by its features
  encoded by the genes
• evolution by genetic operators and offspring
  generation

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Artificial evolution

• Mutation operator
• randomly change the genes encoding the solution
  features
• e.g., changing a 0 into a 1 and inversely
• e.g., minor modification of a feature encoded by
  a real number

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Artificial evolution

• Crossover operator
• defines how to select exchanged parts of the
  genetic material
• e.g., randomly selecting a chromosome splitting
  position

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Artificial evolution

• Directed operators
• preferential selection of some genes for
  mutation or some segments of the chromosome for
  crossover
• the preferential selection is based on
  monitoring, which components of the solution
  contribute to bad or good performance

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Artificial evolution

• Constrained operators
• mutation constraints some simultaneous
  mutations are not allowed, others are enforced
• crossover constraints some chromosome segments
  are allowed to be exchanged only for some
  chromosome segments with specified location

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Artificial evolution

• Optimisation energy function
• fitness measure problem solving performance
• problem solving performance of the individuals
  are evaluated with a random sample of the
  potential problems

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Artificial evolution

• Mating potential
• it is based on the problem solving performance
• the number of the offspring of the individuals
  depends on their mating potential
• high fitness individuals have many offspring
  that inherit at partly their features

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Artificial evolution

• Many parent mating
• the crossover applies to the mix of all parents

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Learning by artificial evolution

• Problem solving performance optimisation
• the average performance of the population
  increases
• the best performing individuals represent very
  good solutions after long enough evolution

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Learning by artificial evolution

• Key features
• proper feature coding
• proper evolutionary operators
• proper fitness evaluation
• proper mating selection

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Learning by artificial evolution

• Feature coding
• the important solution features should be
  encoded
• if it is not clear what is important and what is
  not, better to encode more features than less
  features
• the feature coding and the decoding of the code
  should not be ambiguous

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Learning by artificial evolution

• Evolutionary operators
• the result of applying evolutionary operators
  should be meaningful
• the crossover should result individuals that
  inherit their parents properties

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Learning by artificial evolution

• Fitness evaluation
• the fitness function should be closely related
  to the effective problem solving performance

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Learning by artificial evolution

• Mating potential determination
• the more fit individuals should have more
  offspring
• the drastic elimination of less fit individuals
  may lead to the elimination of genes that are
  sleeping but may become important for the
  achievement of very high performance

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Learning by artificial evolution

• Problems
• too narrow spread of performances it is likely
  that there is little genetic variation in the
  population
• too large spread of the performances it is
  possible that the encoding of features or the
  genetic operators are not functioning properly
• too slow increase of the average performance it
  is possible that the encoding of features or the
  genetic operators are not functioning properly

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Summary

• evolution leads to niche adapted new species
• the basis of evolution are the genes
• new genes may lead to new proteins, cells,
  organs, behaviours, which may increase the
  fitness of the biological organism
• evolutionary adaptations spread by mating and by
  higher mating success of those who are more fit
  to the ecological niche
• evolutionary learning means optimisation of the
  fitness

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Summary

• artificial genes encode features of solutions of
  some problems, the encoding can be discrete or
  continuous
• artificial evolution works by genetic operators
• genetic operators mutation, crossover, directed
  operators, constrained operators
• mating potential depends on problem solving
  performance
• having appropriate feature encoding,
  evolutionary operators, fitness function and
  mating potential determination, the artificial
  evolution leads to high performance solutions of
  the problem