Andreu Sancho Homepage

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...

Imagine we have a set of computers running simulations. Those computers can be described by a group of features, for example its power consumption per hour and the total number of processes it can run per hour. From our R+D personnel, a series of programs are send to these computers so we have to perform some schedule to run the applications, but for our profit we wish to minimise the total consumption of the set of machines. Let's show some more detail: Computer 1 has a power consumption of 100 watts per hour and can run 10  high cpu-demanding processes per hour and 7 low cpu-demanding processes per hour at the same time. Computer 2 has a power consumption of 350 watts per hour and can run 60 high cpu-demanding processes per hour and 20 low cpu-demanding processes per hour at the same time. From the R+D area there are a set of at least 3125 high cpu-demanding processes and at least 710 low cpu-demanding processes to be processed. How many hours should o...