developmental mechanism in genetic algorithms

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 include

* 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