Andreu Sancho Homepage

The ever-increasing integration of technology in the different areas of science and industry has fostered practitioners the design of applications that generate stupendous amounts of data on-line (e.g., smart sensors or network monitoring just to mention two real cases). Extracting information from these data is key, in order to gain a better understanding of the processes that the data are describing. However, learning from these data poses new challenges to traditional data mining techniques, which are not designed to deal with data streams: data in which concepts and noise vary over time. In this regard, I am proud to present the supervised neural constructivist system ( SNCS ), a neural Michigan-style Learning Classifier System  that has been designed to provide a fast reaction capacity and adaptability to the distinct possible changes in concept (concept drifts) with varying noisy inputs.  Inheriting the intrinsically on-line fashion of Michigan-style learning clas...

In the last post we acknowledged that competent GAs are those that automatically identify building blocks (BBs) and exchange these BBs without disrupting them. As a simple example, the compact genetic algorithm (cGA) was presented for competently solving the trap-n functions. In spite of the good results obtained by cGA in these environments, this algorithm is way too simple for truly tough problems : the m-concatenated trap-n problems, as depicted in Figure 1 . In these kinds of problems, deceptive trap-n functions are concatenated into a single, bigger problem.   cGA’s probability vector representation cannot detect the complicated combinations of BBs so, again, a new strategy to tackle this challenging environments is required: we need an order-n probabilistic optimization algorithm (in contrast cGA is of order-1). Figure 1: the 4-concatenated trap-3: It is composed of four trap-3 functions and the objective is to find  111111111111 , but the problem is deceptive and ...

We endorse the term Competent Genetic Algorithms to those GAs that solve hard problems quickly, accurately and reliably [1]. We already know that GAs process building blocks (BB): low order ---few specific bits---and low length ---small distance between specific bits---schema with above average fitness. However, crossover may disturb these BB. Ideally, crossover should identify the fundamental BB of the problem at hand and mix them well, but in the real world this phenomenon scarcely happens. In order to tackle this issue a radical approach is required: remove the classic selecto-recombinative operators out of the GA loop and develop strategies that automatically identify BBs ensuring that these are not disrupted. Researchers call this strategy as Linkage Learning [2]. Estimation of Distribution Algorithms (EDAs) use probabilistic models that perform the task. They learn a probabilistic model and then build new solutions by sampling candidates from the model. One of the sim...