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Complex Adaptive Systems and Computational Intelligence

We are a research group at Indiana University and the Instituto Gulbenkian de Ciencia working on complex systems. We are particularly interested in the informational properties of natural and artificial systems which enable them to adapt and evolve. This means both understanding how information is fundamental for the evolutionary capabilities of natural systems, as well as abstracting principles from natural systems to produce adaptive information technology.

Our research projects (see below) are on computational and systems biology, complex networks, text and literature mining, evolutionary systems, adaptive search and recommendation, cognitive science, artificial life, and biosemiotics. Additional information available on Luis Rocha’s Website and our group page at the Instituto Gulbenkian de Ciencia.

For information on joining our group see our Academics page. As a group, we are seriously interconnected with other research groups and networks: The Center for Complex Networks and Systems (CNets), Alife@IU, Biocomplexity Institute, Cognitive Science Program, Complex Systems & Networks, FLAD Computational Biology Collaboratorium, InfoVis Lab, Instituto Gulbenkian de Ciencia, Networks an Agents (NAN).

You are welcome to join our mailing list CASCI-L by either:

  • sending an e-mail to with subscribe CASCI-L in the body (with no subject), or
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CASCI projects

Literature Mining

Biomedical Literature Mining

Collective Dynamics in Complex Biochemical Networks

Collective Dynamics in Complex Biochemical Networks

Models of RNA Editing

Models of RNA Editing

Artificial Immune Systems

 Semi-metric Network Analysis

Network Analysis of Weighted and Fuzzy Graphs

 The Adaptive Web and Bio-inspired designs for Recommendation Systems

The Adaptive Web

Microarray Analysis

Genomic Multivariate Analysis

 Biosemiotics: interplay between self-organization and selection


Agent-based modeling

Agent-based modeling

Uncertainty and Generalized Information Theory

Uncertainty and Generalized Information Theory