Evolutionary Task Force

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Minutes Converence Call, November 17 - 18 Nov 2011 10:48

Global Task:

  • Each leader writes a paragraph on the progress before Sunday night.

Synchronize the experiments in each subtask so that people do comparable experiments

  • Every group holds a private skype when necesarry
  • Important thing is that experiments are comparable

Subtask 1: Morphogenesis

  • Wenguo has improved his representation to include different connection rotations and heterogeneity
  • Yao: done some experiments in its simulator; working with Ronny to integrate his findings.


  • Michele: Before sunday evening, paragraph summarizing progress.
  • Wenguo: Create mutation, crossover. He will send them to Christopher who will help make them good

Subtask2: Organism Control

  • Until November 30 everyone can use their own simulator
  • By December 15 the solution should be running in Robot3D
  • We postpone the decision until December 15th, as we need to be able to compare them and for that they need to run in the same simulator
  • Everyone needs to be careful that 2 weeks to port your code may be short, so you should look into porting it sooner.


  • Juergen: Before sunday evening, paragraph summarizing progress.
    • Everyone: Send a short text (max 5 - 10 lines) on the progress you made
  • Juergen has an implementation in his own simulator and will implement On-Line On-board evolution there first, and then move on to Robot3D
  • Evert has an implementation in Wiibots and will first implement the inclusion of sensors there, and then move on to Robot3D
  • Florian does not have his own simulator and will work in Robot3D immediately
  • Yao has an implementation in his own simulator and will move toward Robot3D
  • Ronny has an implementation in his own simulator

Subtask3: Internal Reward

  • They will adapt an implementation of the QI to create a Static version of QI.


  • Evert: Implement (he plan to do that before the week-end)
  • There will be an internal discussion after the skype.
  • Evert: Before sunday evening, paragraph summarizing progress.

Subtask 4: Simulator

  • There are still some bugs that Lutz and Vojta are working on.
    • For instance the plugin threads are currently faulty
  • The target platform for now will be Ubuntu 11.04.
    • Any bugs reported should be from this platform
  • Report any bugs to Vojta
  • We will organize a number of tutorials on using Robot3D
    • First tutorial will be somewhere next week.
    • Topics will include
      • Getting, Compiling and Running Robot3D
      • Where to write your controller code, and how to use it in the simulator
      • Some examples
  • Libor and crew have created a component which measures how fast/slow the simulation is compared to real time
  • Several scenarios have been conceptualized and will be implemented
  • First results somewhere next week (Wednesday?)
  • Libor will investigate whether a publication can be made on the simulator
    • The simulator may be a legacy of the project with desirable characteristics


  • Berend: Before sunday evening, paragraph summarizing progress
  • Berend: Confer with Anne and Vojta about bug-reporting and tracking!

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Minutes Converence Call - 10 Nov 2011 15:51



To test the feasibility of the representation.


  • Michele (INRIA)


  • Wenguo: explicit representation, task: create and test evolvability
      • Christopher will help Wenguo with building operators for the explicit representation.
  • Ronny: we have a genome which is evolvable and can build an organism. Tested in simulation.
      • Yao will work with Ronny
  • Inria, implicit representation, task: test evolvability


What a good representation requires, depends on the size of the experiment/organism.

To bootstrap the evolution, we may need an alphabet of shapes; I, H, X, L, T.

  • Ronny: A predefined alphabet may make it hard for evolution to find other shapes.

Initialization phase

For each representation we need to:

  • Know which percentage of individuals is viable, when the genome is initialized randomly
  • How long it takes to complete a shape
  • Define crossover and mutation
  • Know which percentage of offspring viable after crossover/mutation
      • The means for:
      • Explicit: Is the expression well-formed?
      • Implicit: does it converge

The upper-bound on the size of organism is: 4-10.


Everyone creates an experiment in their own simulator and sends their plan to Michele. Michele compiles this into a workplan?



The goal is to compare CFG and AHHS and GRN (Yao-yao).

Task: walk, recognize the walls.


  • Juergen.


  • Juergen (AHHS)
  • Evert (CPG),
  • Florian (CPG)
  • Yao-yao (GRN)


Evert sends an e-mail and organizes things.



The goal is to re-calibrate the controller when a new shape is created; this recalibration is a nested optimization problem (on-line learning).

In order to optimize, we cannot use a external measure, so what is then criterion?

  • Distance
  • QI from the traces
  • evolved QI (learning weights on the sensori-motor states) ** this might be too ambitious **


  • Evert


  • Evert
  • Michele
  • Christopher


Evert sends an e-mail and organizes discussion



Benchmark the simulator to get a feel for how large an experiment we can run in what time.


  • Berend


  • Berend
  • Anne
  • Lutz
  • Libor


  • Test#3: loose modules wandering, organisms moving randomly. Empty space with walls.
  • Anne: appropriate sensors;


Christopher: Do we keep all sensors on everywhere, or do we turn off sensors that are useless in simulator?

  • For now we keep them all on, this is probably needed for AHHS anyway.

Berend: We will also simulate computational effort of crossover and mutation, to make the test as realistic as possible.


  • Procreating requires moving around.
  • Egg receives 2 DNA; then becomes active and recruit others.
  • A cell is an egg or a free cell.
      • Whether a robot is an egg or a free cell is fixed at the start of the simulation
  • An organism has a maximum lifetime (to be set later).
  • An egg does not move

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Minutes Kick Off Meeting - 03 Nov 2011 16:21

Evolutionary Task Force

Amsterdam, november 2-3 2011


Michèle Sebag, Marc Schoenauer, Gusz Eiben, Evert Haasdijk, Juergen Stradner, Anne van Rossum, Berend Weel, Florian Schlachter, Wenguo Liu, Jean-Marc Montanier, Remco Tukker, Nicolas Bredeche


  • description of agenda for each task-force (deadline: Friday, nov. 11th)
  • weekly executive summary (by Gusz)


  • Every Thursday
  • At 11:00 CET
  • Skype or Mumble?


  • individual control parameters INACTIVE/IGNORED

SUB-TASK FORCE #1 : morphogenesis control parameters

  • Explicit (UWE+UT?) vs. Implicit (Graz and TAO) representation of the shape
  • First step: explorative
  • Sub-Task Force: UWE, INRIA, (+ Ronny@Graz)
  • Deadline: NOV. 30
Remarks on the current state:
Ronny Wenguo INRIA
evolvable + - .
tested on robot ? + .

SUB-TASK FORCE #2 : organism control parameters

  • First step
    • CPG(evert)
    • CPG(florian)
    • AHHS
  • we forget, for the moment, about the joint AHHS-CPG architecture (AHHS as sensor info manager)
  • This sub-task force: berend, juergen, florian, anne
  • Deadline NOV. 30
Remarks on the current state:
AHHS CPG(evert) CPG(florian)
online-evolv. - + -
sensor inputs + - +
tested + +? +

SUB-TASK FORCE #3 ''internal reward (during lifetime learning)''

  • people: VU, INRIA, UT?
  • investigation of curiosity as internal reward.
  • Deadline: NOV. 30 for exploration

SUB-TASK FORCE #4 ''benchmark of simulator''

  • people: Anne/almende, Berend
  • Benchmarking the simulator
    • nb and size of organisms, nb of cells, test scale up
    • test #1: swarm of cells
    • test #2.a: organism, snake
    • test #2.b: organism, H-shape
    • test #3: swarm of organisms and cells (simulate the expected content of video)
  • Deadline: 16/11 (2 weeks from now)

Decisions for DAY 1

General considerations

  • general:
    • there are 4 stages: egg, growth, organism (ie. static body shape), dying.
    • only organisms transmit genomes
    • only eggs that are not aggregated can receive genomes
    • open question:
        • no fitness? environment-driven evolutionary adaptation (no fitness function / reproductive advantage)
        • implicit fitness? curiosity-driven, no ground truth
  • swarm-mode:
      • there will be no evolving swarm of single robots
          1. we’re skipping tasks evolutionary swarm mode: all robots are either an egg or in search of an organism to join.
      • use pre-defined behaviors (random walk, red light tracking, …)
  • organism-mode
    • we start with small organisms
    • development process in 2D; organism is expected to ‘stand up’ and move in 3D
    • there will be no evolving swarm of organism — no direct mating among organisms. The only mating is through organism planting seed in unused eggs.

Decisions for DAY 2

  • Agreement on scenario 1
    • parameters, general: how many cells/eggs, % eggs, size of arena, communication range, egg's timeout-to-restart, lifetime
    • parameters, in organism mode: time/trial (=tau), #trials, duration of learning wrt. lifetime
    • unlimited energy
    • walls
    • fixed nb of free cells and eggs
    • initial shapes
  • Agreement on various issues:
    • an egg re-becomes an egg after organism death
    • body and mind evolve together
    • mind may adapt during lifetime (eventually by evolution/learning)
    • mind may be trasmitted in a lamarckian fashion… or not.
        • Lexicon: organism = body + mind/brain = shape + controller
        • controller
        • evolutionary timescale
            1. control parameters (''narrow sense'')
            2. lifetime adaptation mechanisms
                1. init points (inherited vs. random)
                2. hyper-parameters
                3. internal reward
                    • options: (1) distance (2) traces (2.a) QI (2.b) evolved-QI
                    • start simple: distance, even though may not be correlated with survival
  • Conceptual boxes:
    • box #1: build candidate shape
    • box #2: build candidate controller
    • box #3: morphogenesis
    • box #4: epigenetics learning

What can be shown in the demonstration video / Requirements

  • in simulator:
      • video is ON for all experiments, we record EVERYTHING
      • record logs, for each time steps:
          • random seed for each log
          • compilation version(s) of everything (simulator, controller, etc.)
          • position of each cells
          • input and output values
          • for every new organism: record the description of shape and controller

Looking at the demo / visual outcome and impact expected:

  • observed:
      • number of shapes
      • size
      • distances
      • when
  • tools
      • clustering
      • genealogy
      • behavior as in physical trajectories
      • behavior as in sensory-motor space
      • behavior as in response test
  • monitoring
      • show the robot physical trajectories
      • datamining the log

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