Flibs'NLogo - An elementary form of evolutionary cognition 1.1.0
Flibs’NLogo implements in NetLogo modelling environment, a genetic algorithm whose purpose is evolving a perfect predictor from a pool of digital creatures constituted by finite automata or flibs (finite living blobs) that are the agents of the model. The project is based on the structure described by Alexander K. Dewdney in “Exploring the field of genetic algorithms in a primordial computer sea full of flibs” from the vintage Scientific American column “Computer Recreations”.
As Dewdney summarized: “Flibs […] attempt to predict changes in their environment. In the primordial computer soup, during each generation, the best predictor crosses chromosomes with a randomly selected flib. Increasingly accurate predictors evolve until a perfect one emerges. A flib […] has a finite number of states, and for each signal it receives (a 0 or a 1) it sends a signal and enters a new state. The signal sent by a flib during each cycle of operation is its prediction of the next signal to be received from the environment”
Release Notes
- Upgrading to NetLogo 7.
- The model provides a second way to initialise flib chromosomes, allowing them to start with just a single state. Over time, mutations can increase the number of states, up to a user-defined limit.
- The mate-rate calibration has been improved.
- The mutation-rate calibration has been enhanced to make it proportional to both the number of flibs and the number of chromosomal loci.
- It is possible to manage flibs populations reproducing both through cloning and conjugation.
- Genomic Entropy Tracking: integration of classical ecological diversity indices to monitor the breadth of the search space as Shannon-Wiener Index or Simpson’s Evenness.
- Phenotypic Simpleness Detection: implementation of a new diagnostic algorithm to evaluate the “expressed phenotype” (active states), identifying agents with reduced logic to ≤ 2 states.
