[Neuroscience] Complexity through Development and Self-Organizing Representations, at GECCO in Seattle on July 8-12, 2006 (2nd CFP)

Ivan Garibay igaribay at cs.ucf.edu
Mon Mar 13 14:40:00 EST 2006


WORKSHOP ON COMPLEXITY THROUGH DEVELOPMENT AND SELF-ORGANIZING REPRESENTATIONS

                              (CODESOAR-2006)
                      http://codesoar.research.ucf.edu

                          to be held as part of the

         2006 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO-2006)

                   July 8-12, 2006 (Saturday-Wednesday)
                        Renaissance Seattle Hotel
                        Seattle, Washington, USA
                        Organized by ACM SIG-EVO
                        www.sigevo.org/GECCO-2006

PAPER SUBMISSION DEADLINE FOR WORKSHOP:   31 MARCH, 2006

AUTHOR NOTIFICATION: 12 APRIL, 2006

CAMERA-READY DEADLINE: 19 APRIL, 2006

Chairs 

 Ivan Garibay*, Sanjeev Kumar**, Julian Miller***, Ozlem Garibay*, Kivanc Oner*

*Evolutionary Computation Laboratory, University of Central Florida 
**Sibley School of Mech. and Aerospace Eng. , Cornell University 
***Department of Electronics , University of York 

BACKGROUND:

This workshop follows on from the successful workshops on self-organization in 
representations in evolutionary algorithms, and scalable, evolvable, emergent 
developmental systems at previous GECCO conferences. This year's workshop is a 
unified workshop covering both closely related areas. It promises to be an 
exciting, thought provoking, and successful workshop. 

Evolutionary algorithms (EAs) have been applied to an ever increasing variety 
of problem domains, for which they have achieved human competitive results on 
small evolutionary design problems. The application of EAs to tasks of ever 
increasing difficulty is fraught with problems, namely: stagnation of search 
in large search spaces, negative epistatic effects, disruption of large 
building blocks, and scalability, amongst others. Recently, the problem of 
scalability has attracted much attention, and deservedly so, as its resolution
 is linked to other critical and demanding open research problems such as: 
development, evolvability, and modularity. In order to improve the scalability 
of such systems fundamental research must be undertaken to discover how to 
evolve increasingly more complex designs.

For this we look at the two systems that have achieved scalability: human 
engineering and natural systems. Manually constructed systems have achieved 
such things as aircraft with over a million parts, software with tens of 
millions of lines of code and over a hundred million transistors in 
microprocessors, suggesting that we can improve the scalability of automated 
design by using principles of engineering. Similarly, natural evolution and 
developmental biology have produced adaptable and self-repairing systems of 
even greater complexity using principles of self-organization.

Self-organization is fundamental to the developmental process at all levels: 
molecular, genetic, and cellular. Nature evolves instructions in the form of 
genes that are used to specify the construction of organisms during the 
process of development. With reports of the number of genes in the human 
genome being revised downwards, the role of self-organization in complex webs
 of gene regulation is all the more salient. Given these new findings, perhaps 
the self-organization of genotypic instructions and biological structure from 
cells during multicellular development is a key missing ingredient from EAs? 
To this end, it is anticipated that models of biological cells and 
multicellular development represent a valuable source of knowledge that will 
aid us in designing EAs with emergent phenomena such as: adaptability, 
scale-free-ness, evolvability, and robustness. Regardless of the developmental 
model or generative representation chosen -- cellular automata, genetic 
regulatory networks, L-systems, etc - we must understand exactly what gives 
such systems their computational power and exactly how they affect evolvability.

This workshop will focus on domain-independent methods for representing complex 
solutions with self-organizable building blocks, and on developmental 
principles for specifying the construction of complex systems. The workshop 
welcomes multidisciplinary work, including submissions from biologists on 
relevant biology that may help shed more light on developmental, self-organizing 
principles for evolutionary computation.

TOPICS OF INTEREST:

- Models of complexity building using self-organization 
- Emergent behavior in representations 
- Methods of design and evaluation of self-organizable representational building 
  blocks 
- Scalability of self-organizational processes to high complexities 
- Self-organization theoretical approaches: complexity, chaos, synergetics, 
  self-organized criticality, non-equilibrium thermodynamics, etc. 
- Self-organized development 
- Genotype-phenotype mappings for self-organization and single & multicellular 
  development 
- Pattern formation, morphogenesis, cellular differentiation, and growth 
- Models of genetic regulatory networks, modularity, segmentation, and 
  compartmentalization 
- Scalability & Evolvability of developmental processes 
- Robustness, self-repair and regeneration in developmental processes 
- Real world applications of developmental principles

SUBMISSION PROCEDURE:

Please submit proposed contributions via email to: 

igaribay at cs.ucf.edu

in PS or PDF format by March 31st. 

Contributions can vary from one-page position statements up to full twelve-page 
camera-ready papers. Accepted contributions will be published in the GECCO-2006 
proceedings.



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