Selected Topics in Evolutionary Algorithms I

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Selected Topics in Evolutionary Algorithms I

• Pavel Petrovic
• Department of Applied Informatics,
• Faculty of Mathematics, Physics and Informatics
• ppetrovic_at_acm.org
• July 4th 2008

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Riddle

• Theorem 1 10 cent
• Proof
• We know that 1 100 cents, divide both sides by
  100
• 1/100 100/100 cents
• 1/100 1 cent
• Take square root both side
• sqrt(1/100) sqrt (1 cent)
• 1/10 1 cent
• Multiply both side by 10
• 1 10 cent

How many robots does it take to screw in a light
bulb? Three one to hold the bulb and two to
turn the ladder.
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Robots in Everyday Life

• Rescue, Patrol, Safety, Security
• Assistance at Home and in Public
• Maintenance and Services
• Monitoring and Data Collection
• Production, Construction, Mining
• Transport, Shipping, Storehouses
• Education and Entertainment
• Space, Marine, Polar, Extreme Conditions

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Robotics Multidisciplinary Efforts
Computer Science
Biology
Psychology
Mechanical Engineering

• Robotics

Physics
Material Science
Electrical Engineering
Communication Technology
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Robotics and Computer Science

• Signal and Data Processing and Analysis
• Prediction and Estimation
• Optimization, Scheduling, Planning, Search
• Image Processing and Pattern Recognition
• Machine Vision
• Simulation and Modelling
• Knowledge Representation and Machine Learning
• Human-Computer Interaction

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Robotics and Computer Science (2)?

• Robotics applied
• engineering field

Computer Science theoretical field

• Methods
• Algorithms

• Real-world tasks
• Commercial products

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Robotics Challenges

• Robotic applications in unpredictable, dynamic,
  non-deterministic environments
• Require real-time algorithms and reactive
  architectures that allow adaptation, learning,
  behavior plasticity
• Resulting systems exhibit features of
  intelligence

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Long-term goal and efforts

• Building mobile robots capable of autonomous
  execution of complex tasks in realistic, dynamic,
  non-deterministic, unpredictable environments
• Require suitable sensors, actuators, morphologies
  and controllers
• Important challenge organization of controller
  architecture and its design, i.e. how a robot is
  trained for the target task, how it can
  generate, revise and execute plans

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Approaches to Robotics

• Industrial Robotics
• Focused on working solutions, manufacturing
  robots, control theory, deterministic
  environments, repetitive operations

Artificial Intelligence Intersection of
Philosophy, and Psychology, spiced with Biology
parasiting on Computer Science Set to answer
questions of the fundamental principles of
intelligence, knowledge acquisition, organization
and representation Dreams about discovering
methods and algorithms that can be useful in
applications
Artificial Life Studies principles of
generalized life mechanisms Needs/attempts for
physical systems
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Search

• Space of possible solutions
• Search criterion
• Determines what is the best solution and which
  of any two solutions is better
• Example
• 4 people trying
  to cross the bridge at night
• Max. two at the same time
• Take different time 1,2,5,10
• Must use flashlight
• What is the fastest strategy?

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Search

• Deterministic search
• Systematically exhausting
• Depth-first search
• Breadth-first search
• Iterative deepening
• Heuristic search
• Greedy search
• A search optimal
• Stochastic search
• Monte-carlo
• Simulated annealing
• Evolutionary algorithms
• TABU search

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Example Search for shortest path
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Example Search for shortest path
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Example Search for shortest path
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Example Search for shortest path
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Example of greedy search Knight tour

• The knight is to visit every location exactly
  once
• Heuristic visit the location with lowest of DOF

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Example of heuristic search Game15

• Sliding numbered stones until target
  configuration is achieved (about 1013 possible
  states)
• Can you find the correct heuristic?

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Example of heuristic search Game15

• A algorithm
• Admissible heuristic
• the number of misplaced tiles (admissible,
  because an out of place tile requires at least
  one move to get to the right place).
• the sum of the Manhattan distances of each tile
  from its proper place (admissible because each
  move can only move a tile one step closer).
• Comparison for the eight-puzzle (branching factor
  is around 3, sample runs at a depth of 12)
• Iterative-deepening expanded 3,644,035 nodes
• A with the first heuristic expanded 227 nodes
• A with the second heuristic expanded 73 nodes

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Problem solving types of problems

• Easy polynomial-time solution exists (class
  P)?
• Difficult only non-deterministic polynomial-time
  solution exists (class NP), or not even that...
• particular class NP-complete
• Difficult problems require exponential time aN
  problems of realistic sizes cannot be solved
  using deterministic algorithms!
• Stochastic methods find some good solution,
  instead of the best one optimization

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Stochastic methods Monte Carlo

• Determine the area of a particular shape

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Stochastic methods Simulated Annealing

• Navigating in the search space using local
  neighborhood

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Principles of Natural Evolution

• Individuals have information encoded in genotypes
  that consist of genes, alleles
• The more successful individuals have higher
  chance of survival and therefore also higher
  chance of having descendants
• The overall population of individuals adapts to
  the changing conditions so that the more fit
  individuals prevail in the population
• Changes in the genotype are introduced through
  mutations and recombination

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EA Concepts

• genotype and phenotype
• fitness landscape
• diversity, genetic drift
• premature convergence
• exploration vs. exploitation
• selection methods roulette wheel (fit.prop.),
  tournament, truncation, rank, elitist
• selection pressure
• direct vs. indirect representations
• fitness space

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Genotype and Phenotype

• Genotype all genetic material of a particular
  individual (genes)?
• Phenotype the real features of that individual

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Fitness landscape

• Genotype space difficulty of the problem
  shape of fitness landscape, neighborhood function

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Population diversity

• Must be kept high for the evolution to advance

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Premature convergence

• important building blocks are lost early in the
  evolutionary run

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Premature convergence
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Genetic drift

• Loosing the population distribution due to the
  sampling error

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Exploration vs. Exploitation

• Exploration phase localize promising areas
• Exploitation phase fine-tune the solution

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Selection methods

• roulette wheel (fitness proportionate selection),
• tournament selection
• truncation selection
• rank selection
• elitist strategies

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Selection pressure

• Influenced by the problem
• Relates to evolutionary operators

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Direct vs. Indirect Representations
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Fitness Space (Floreano)?

• Functional vs. behavioral
• Explicit vs. implicit
• External vs. internal

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Evolutionary Robotics

• Solution Robots controller

• Fitness how well the robot performs
• Simulation or real robot

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Fitness Influenced by

• Environment difficulty

• Task difficulty

• Robots abilities (sensors, actuators)?

T

• Controller abilities

• Robot Morphology

Incremental change during evolution
Incremental Evolution
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Evolvable Tasks

• Wall following
• Obstacle avoidance
• Docking and recharging
• Artificial ant following
• Box pushing
• Lawn mowing
• Legged walking
• T-maze navigation

• Foraging strategies
• Trash collection
• Vision discrimination and classification tasks
• Target tracking and navigation
• Pursuit-evasion behaviors
• Soccer playing
• Navigation tasks

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Neuroevolution through augmenting topologies

• The most successful method for evolution of
  artificial neural networks
• Sharing fitness
• Starting with simple solutions
• Global counter
• i.e. Topological crossover very important for
  preserving evolved structures

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