Evolutionary algorithms based on game theory and cellular automata with coalitions

Cellular genetic algorithms (cGAs) are a kind of genetic algorithms
(GAs) with decentralized population in which interactions among individuals are
restricted to the closest ones. The use of decentralized populations in GAs allows to
keep the population diversity for longer, usually resulting in a better exploration of
the search space and, therefore in a better performance of the algorithm. However,
the use of decentralized populations supposes the need of several new parameters
that have a major impact on the behavior of the algorithm. In the case of cGAs,
these parameters are the population and neighborhood shapes. Hence, in this work
we propose a new adaptive technique based in Cellular Automata, Game Theory
and Coalitions that allow to manage dynamic neighborhoods. As a result, the new
adaptive cGAs (EACO) with coalitions outperform the compared cGA with fixed
neighborhood for the selected benchmark of combinatorial optimization problems.