A technique is presented that is suitable for function
optimization in high-dimensional binary domains. The method allows
an efficient parallel implementation and is based on the combination
of genetic algorithms and reinforcement learning schemes. More
specifically, a population of probability vectors is considered, each
member corresponding to a reinforcement learning optimizer. Each
probability vector represents the adaptable parameters of a team of
stochastic units whose binary outputs provide a point of the function
state space. At each step of the proposed technique the population
members are updated according to a reinforcement learning rule and
then recombined in a manner analogous to traditional genetic
algorithm operation. Special care is devoted to ensuring the
desirable properties of sustained exploration capability and
sustained population diversity. The efficiency of the method is
tested on two deceptive problems that constitute typical benchmarks
yielding very promising results.