Genetic Algorithms

Genetic Algorithms (GAs) are robust probabilistic algorithms for optimization, relying strongly on parallel computation. Their power comes from multi-point exploiting of the searching space, avoiding the stagnation in local optima. First we review the state of art in GA theory. Next, two illustrative original applications highlight the efficiency of GA on multi-parameter optimization tasks: on solving systems of fuzzy relational equations, and on optimizing the parameters involved in an economic forecasting task.