Task Force on
Chair:: Efrén Mezura-Montes.
Artificial Intelligence Research Center, University of Veracruz, MEXICO (CIIA-UV)
Vice-Chair: Helio Barbosa.
original versions, evolutionary algorithms (EAs) and Swarm Intelligence
(SI) approaches lack a mechanism to handle the constraints of an
optimization problem. On the other hand, several real-world
optimization problems include different types of constraints (e.g.
linear, nonlinear, equality and inequality constraints). Therefore, a
considerable amount of research has been dedicated to develop
mechanisms to incorporate feasibility information into the fitness
function of an EA. The most popular technique is the use of penalty
functions. The aim is to decrease the fitness of those infeasible
solutions in order to favor feasible solutions in the selection
process. The main drawback of penalty functions is the careful
fine-tuning required by the penalty factors, which determine the
severity of the penalization.
Besides penalty functions, there are several constraint-handling mechanisms proposed in the specialized literature. However, it is an open problem how to choose the most adequate for a given search algorithm to solve a specific constrained optimization problem.
The mission of the task force is to encourage research efforts focused on the study and design of novel constraint-handling techniques, their synergies with different nature-inspired search algorithms and the solution of challenging real-world problems.Up
expected to be tackled by the activities of the Task Force are the