Context & Challenges for ER:
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ER is a field of research that applies evolutionary computation techniques
to evolve overall design OR controllers OR both
ER uses evolutionary computing techniques to automatically develop
some or all of following properties of a robot:
o Controller
o Body
o Sensor & motor properties
o Layout
Populations of artificial genomes encode properties of autonomous
robots required to carry out a particular task or to exhibit some set of
behaviors
o Genomes are mutated (mutation) & interbred (crossover) to
create new generations of robots (Darwinian scheme – fittest
individuals are more likely to produce offspring)
o Fitness is measured to tell how good robot is according to some
evaluation criteria
 Fitness is usually measured automatically, but can also be
based on the experimenter’s judgement
Applications of ER:
o Entertainment industries
o Exploration & navigation
o Technology in solving environmental issues
o Robotics (robustness & autonomy are fundamental requirements)
Alan Turing suggests that intelligent machines should be adaptive –
should learn & develop – programming by hand is infeasible – instead,
apply biological evolution
Brooks and others demonstrated methodologies and techniques to evolve
(not design) control systems
Key elements of ER approach:
o Method to measure fitness of a robot’s behavior generated from
genomes
o Method to apply selection and genetic operators to produce next
generation from current one
1. Initial population of genotypes & evaluate fitness
2. Select parents & breed
3. Create mutated offsprings
4. Evaluate offsprings
5. Replace members of population
6. (2)
Genetic encoding:
o Many parts of robot design (also controller) can be evolved
o Most popular controller is ANN (feedforward/recurrent)
 Properties of ANN (thresholds/biases) are fixed length
values
 Properties of ANN are evolved
o Evolutionary algorithms used to manipulate genetic encoding
vary:
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 Simple GAs
 More sophisticated GAs
 Specific GAs for evolving robot controllers
Fitness evaluation:
o Transfer genome to robot and measure resulting behavior
 Historically, EA was on host computer while genome was
tested on target robot
 Better to test on real robot than on simulator, because
simulators are not accurate enough – this was proven to be
false – this led to many successful simulationpbased
approaches (i.e. minimal simulation methodology [only
important to the desired behavior aspects of the
environment are modeled, masking everything else as a
{carefully structured} noise])
o Plastic controllers adapt through self-organization. Not connection
weights are evolved, but “Learning rules”, for adapting connection
strengths, are evolved -> controllers continually adapt to changes
in their evironment (Floreano, Husbands, and Nolfi [2008])
o Explicit fitness function
 Rewards specific behavior (i.e. traveling in straight line) –
shapes overall behavior from a set of specific (hardcoded)
behaviors
o Implicit fitness function
 More abstract level – points (fitness) are given for
completing some task, but are not tied to any specific,
hardcoded behavior
 Examples:
o Maintain energy levels
o Cover as much area as possible
o etc
o It is possible to define a fitness function that has both
explicit&implicit elements
Evolution can proceed in stages, where competences are built up in
incremental way (additions to current solution) – fitness function may
change as evolution progresses
Advantages:
o Explore designs that have many free variables (a set of possible
robots is defined rather than a specific robot -> fewer constraints)
o Fine tune parameters of already successful design
o Build on already existing solution
o Take into account multiple and/or conflicting criteria/constants
(i.e. [efficiency/cost/weight/power consumption ->
functional]/[robustness/reliability -> non-functional..]/etc)
o Possibly develop not intuitive and minimal design
ER specifies target behavior, but does not tell how this behavior should be
achieved
ER & Neuroscience:
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