TRACER Web Sites are a network of thematic mini-web sites on specialized reseach domains. The groups of researchers in TRACER are continuously developing public information to contribute to the dissemination of knowlegde and results in their many branches of expertise. The present list of thematic sites is as follows:

1. Cellular Evolutionary Algorithms Site. This site describes cEAs, including technical information, links and detailed bibliography. Cellular EAs use a population of individuals arranged in a toroidal grid (usually) and provide researchers with powerful tools to perform numerically efficient search.

2. Minimum Linear Arrangement Problem Site. This website is about a combinatorial optimization problem called Minimum Linear Arrangement problem (MinLA), also known as the optimal linear ordering, the edge sum problem or the minimum 1-sum. The site describes the problem and provides a detailed bibliography.

3. MOSET. System Identification (SI) tries to find a parametric model of dynamical systems from its I/O measured values. In the MOSET site, several strategies are used to estimate the parametric polynomial ARMAX model of Time Series.

4. SIRVA. The main goal of this site is the development of image processing services via Internet by means of algorithms implemented by software and reconfigurable hardware (FPGAs).

5. TIDESI. This site is aimed at predicting tide's behaviour by means of Time Series. A database containing the level water in the Venice Lagoon measured each hour along the years 1980-1989 and 1990-1995 is used.

6. Interactive Evolutionary Computation. This site describes IEC techniques, including technical information, links and detailed bibliography. IEC brings human evaluation knowledge to the most common Evolutionary techniques. Is an interesting field for theoretical research and it has a large number of powerful tools and applications.

7. Time Series. This site presents some advanced techniques based on evolutionary and neural computation for

time series prediction. A repository of an interesting collection of data is included.8. Particle Swarm Optimization. PSO is a population-based, bio-inspired optimization method. It was originally inspired in the way crowds of individuals move towards predefined objectives, but it is better viewed using a social metaphor. Individuals in the population try to move towards the fittest position known to them and to their informants, that is, the set of individuals that are their social circle. The objective is to maximize or minimize a fitness function.