This paper presents an algorithm for optimization of a multimodal scalar-argument function based on a team of learning stochastic automata with binary actions (outputs) 0 or 1. The action of the team of automata consists of a digital number which represents the environment input. The probability distribution associated with each automaton is adjusted using a modified version of the Bush-Mosteller reinforcement scheme with a continuous environment response and a time-varying correction factor. The asymptotic properties of this optimization algorithm are presented. An example illustrates the feasibility of this optimization algorithm. (C) 2004 Elsevier Ltd. All rights reserved.