Here is a new genetic algorithm. It is built by randomly perturbing
a two operator crossover-selection scheme. Three conditions of biological
relevance are imposed on the crossover. A new selection mechanism is used,
which has the decisive advantage of preserving the diversity of the individuals
in the population. The attractors of the unperturbed process are particular
equifitness subsets of populations endowed with a rich structure. The random
vanishing perturbations are twofold: local perturbations of the individuals
(mutations) and loosening of the selection pressure. When the population size
is greater than a critical value which depends strongly on the optimization
problem, their delicate asymptotic interaction ensures the convergence
(possibly in finite time) of the population to the ideal attractor whose
populations contain all the maxima of the fitness function. The process
explores without respite the neighborhoods of the best points found so far
(instead of focusing on a particular point) and finds simultaneously all the
global maxima of the fitness function; it seems to be the first cooperative
search procedure of this kind.