#Calabretta R, Nolfi S, Parisi D, Wagner GP. An artificial life model for investigating the evolution of modularity. To appear in: Bar-Yam Y (ed) Proceedings of the First International Conference on Complex Systems. Addison-Wesley, Reading, 1998b Home/Search Document Details and Download
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Cellular Automata in an Artificial Life Perspective - Calabretta (1998) (Correct)
....identical. In research developed in our Research Group on Artificial Life (GRAL; http: kant.irmkant.rm. cnr.it gral.html) we have examined some implications of a more biologically plausible genotype tophenotype mapping in genetic algorithms applied to populations of neural networks (e.g. 9] [10], 11] 12] In Calabretta et al. 9] the genotype and the phenotypical neural network are distinct entities and the genotype may exhibit diploidy, i.e. it may include two copies of each gene whose expression is governed by some dominance rules. We compared the performance of haploid and ....
....environments diploid populations exhibit the capacity to keep a sort of genetic memory of the past recorded in their non expressed genes (the shielding effect) in fact they recover faster and with a lesser decrease in fitness than haploids after environmental change. Q# DODEUHWWD#HW#DO##[10], 11] 12]#ZH have LQYHVWLJDWHG#WKH#UROH#RI#JHQHWLF RSHUDWRU#RI#JHQH#GXSOLFDWLRQ#IRU#WKH#HYROXWLRQ#RI#PRGXODULW ##9DULRXV#DXWKRUV#KDYH VWUHVVHG#WKH#UROH#RI#JHQHWLF#GXSOLFDWLRQ#IRU#WKH#HPHUJHQFH#RI#HYROXWLRQDU #QRYHOWLHV# ....
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#Calabretta R, Nolfi S, Parisi D, Wagner GP. An artificial life model for investigating the evolution of modularity. To appear in: Bar-Yam Y (ed) Proceedings of the First International Conference on Complex Systems. Addison-Wesley, Reading, 1998b
Evolutionary Connectionism and Mind/Brain Modularity - Calabretta, Parisi (2001) Self-citation (Calabretta Parisi) (Correct)
....than nonmodular ones and they construct a better internal representation of the task. Rueckl et al. 1989) hypothesize that this might be one of the reasons for the evolutionary emergence of the two distinct neural pathways in real organisms. In order to test this hypothesis, Di Ferdinando, Calabretta and Parisi (2000) repeated the experiment of Rueckl et al. 1989) by also allowing the evolution of the network architecture. In a first set of simulations, they used a genetic algorithm for evolving both the architecture and the connection weights of the neural networks. The results showed the inability of the ....
.... that a change affecting a gene will produce a positive effect is reduced with increased pleiotropy of that gene (i.e. by the number of phenotypical characters affected by that gene) A good mapping therefore should reduce pleiotropic effects among characters serving different functions (Calabretta et al. 2000b, p. 77) This kind of efficient mapping can be called modular. More generally, a modular structure of the genotype to phenotype mapping can increase the propensity to evolve (evolvability) of the system possessing this modular structure. However, one should add two considerations. The first ....
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Calabretta, R., Nolfi, S., Parisi, D. and Wagner, G. P. (2000a). An artificial life model for investigating the evolution of modularity. In Y. Bar-Yam, (ed.), Unifying Themes in Complex Systems, Perseus Books, Reading, MA.
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