Michal Joachimczak

Learn More
We present a model of multicellular development controlled by a gene network in which the connectivity is determined by the proximity of sequences in N -dimensional space. Thus the sequences of individual genes can be visualised as points in space which approach or move away from one another as the genomes evolve. The genotype-phenotype (morphology) mapping(More)
We use a genetic algorithm to obtain artificial gene regulatory networks (GRNs) controlling real time behaviour of artificial agents (animats) that gather food resources in a 2D environment. We build a system in which evolving GRNs are encoded in linear genomes. The encoding allows to determine which transcriptional factors (TFs) interact with which(More)
We present a model of three-dimensional artificial embryogenesis in which a multicellular embryo develops controlled by a continuous regulatory network encoded in a linear genome. Development takes place in a continuous space, with spherical cells of variable size, and is controlled by simulated physics. We apply a genetic algorithm to the problem of the(More)
We present a platform that allows for co-evolution of development and motion control of soft-bodied, multicellular animats in a 2-dimensional fluid-like environment. Artificial gene regulatory networks (GRNs) with real-valued expression levels control cell division and differentiation in multicellular embryos. Embryos develop in a simulated physics(More)
This paper deals with a synthesis of Pulsed Para-Neural Networks (PPNN) in a 3-D Cellular Automata space. In its essence, PPNN is a set of simple processing units that change their states only in certain discrete moments of time denoted t, t+1, t+2, ... The inlets to and outlets from the units are located in such a way that a given unit may (but does not(More)
In this paper we extend our artificial life platorm, called GReaNs (for Genetic Regulatory evolving artificial Networks) to allow evolution of spiking neural networks performign simple computational tasks. GReaNs has been previously used to model evolution of gene regulatory networks for processing signals, and also for controling the behaviour of(More)