OPPOSITE LEARNING STRATEGIES FOR IMPROVING THE SEARCH PROCESS OF ANT-BASED ALGORITHMS

For this work we have implemented three algorithms: algorithm A, for the estimation of the global alignment with k differences, based on dynamic programming; algorithm B, for

The search for a global solution using evolutionary algorithms based on population to converge at the optimum, it includes the Ant Colony Optimization (ACO), Genetic

A set of performance metrics is designed to define the profile of a user, and based on those profiles, we run and validate some clustering algorithms to obtain groups of users

The goal of our experimentation is four-fold. First, we are demonstrating that evolutionary algorithms achieve better re- sults than particle filters for the MMOCAP problem. Second,

These new algorithms optimize the search of the communities using different fitness func- tions, which combine distance metrics based on features of the nodes, with measures related

All this is shown throughout this thesis by solving hard combina- torial optimization problems, such as quasigroup completion, social golfers, optimal Golomb rulers, using a variety

A new two-tier inverse technique for characterizing coaxial to waveguide transitions based on the use of genetic algorithms and the gradient descent method has been described and

A new two-tier inverse technique for characterizing jointly two coaxial to waveguide transitions and a device under test based on the use of genetic algorithms and