1 Evolutionary Growth of Genomes for the Development and Replication of Multi-Cellular Organisms with Indirect Encodings Stefano Nichele and Gunnar Tufte ICES Orlando (USA) December 9-12, 2014 Stefano Nichele, 2014

2 Genomes of biological organisms are not fixed in size They evolved and diverged into different species acquiring new genes and thus having different lengths LUA (Last Universal Ancestor) ~ 3.5 / 3.8 billion years ago Gene duplication: redundant gene with less selection pressure Larger genome: genetic novelty, potential for innovation Complexification: incremental elaboration ~ 38% Homo Sapiens genome due to gene duplication

3 Artificial EvoDevo systems often have static size genomes System designer choice: Trial & error Estimation / heuristics Fixed maximum complexity (vs. open-ended in nature)

4 Goal Evolutionary growth of genomes (with indirect encodings) –Initialize genomes with a single gene (low dimensionality) –Allow gene duplications (add new degrees of freedom) Evo-Devo System based on Cellular Automata –Abstract model of development –Morphogenesis –Replication Evolve compact and effective genomes –Compare genome size and success rate –Fully specified genome (complete CA transition tables) vs growing genome

5 EvoDevo Mappings Direct Redundant (neutrality) Indirect (generative/developmental) –Full specification of representation –von Neumann replicator: 29 5 Fixed length (subset) Variable length Complexification –NEAT (Stanley & Miikkulainen): good for evolving modular structures with direct encodings

6 CA as EvoDevo systems CA can be considered as a developmental system, in which an organism can develop (e.g. grow) from a zygote to a multi-cellular organism (phenotype) according to specific local rules, represented by a genome (genotype). The behavior of the CA is represented by the emergent phenotype, which is subject to shape and size modification, along the developmental process.

7 Traditional CA dev. model Example CA with 4 cell states and 5 neighbors: Search space = 4^4^5 = = ~ 3.23 x

8 Previous work Evolutionary growth of genomes –CA transition tables –abstract measures of complexity: trajectory / attractor length Scalability –Search space, number of cell states, geomerty size (phenotypic resources) Current: Different target: phenotypic structures of different complexity Different mapping: IBD (Instruction-Based Development) –Not bounded (evolve from one instruction to program)

9 Evolutionary Growth A genetic algorithm (details in paper) with 4 regulation mechanisms to control gene duplication: –Upper bound, total number of genes –Duplication rate –Optimization time (before new duplication can occur) –Elitism Weighted fitness: –80% actual fitness –20% innovation parameter Rewards larger genomes New genes most likely fitness-neutral or negative

10 CA – IBD (Bidlo and Skarvada 2008, Bidlo and Vasicek 2012) U LCR D Inst. Code Op1Op2 gene

11 Benchmark structures

12 Development problem Operands: U = 0, R = 1, D = 2, L = 3, C = 4. Inst. Code Op1Op2

13 Development - results

14 Replication problem time

15 Replication - results

16 Replication of ”French” Flag

17 Success rate Table-based Evolution Success Rate % Genotype Size (# genes) Max Avg Min StDev Generations Avg. StDev. A B C D Instruction-based Growing Evolution Success Rate % Genotype Size (# genes) Max Avg Min StDev Generations Avg. StDev. A B C D Table-based Evolution Success Rate % Genotype Size (# genes) Max Avg Min StDev Generations Avg. StDev. A C D E Instruction-based Growing Evolution Success Rate % Genotype Size (# genes) Max Avg Min StDev Generations Avg. StDev. A C D E Development (avg. 100 runs)Replication (avg. 100 runs)

18 Conclusion Evolutionary growth of genome Initialize with single gene, allow duplication and speciation (regulation mechanisms) Traditional CA mapping vs instruction based development (unbounded) Development and replication problems Compact and effective genotype solutions (not designed a priori) Better success rate

19 Future work Investigate a major challenge in EvoDevo: –Development of complex morphologies and structures (potentially at levels of complexity found in nature) True complexification –Allow growth of available cell states (unbounded state space) Optimization of instructions and instruction set More benchmarks and tasks (example: circuit design) Introduce self-modifying instructions –Allow diversification of programs

20 Stefano Nichele