In this paper, we propose an Evolutionary Algorithm (EA) with a deterministic mutation operator which is a combination of EA with the Broyden, Fletcher, Goldfarb and Shanno (BFGS) method. The advantages of both optimization algorithms are retained and interconnected. The proposed algorithm shows...

We describe a system for bilateral negotiations in which artificial agents aregenerated by an evolutionary algorithm (EA). The negotiations are governed bya finite-horizon version of the alternating-offers protocol. Several issuesare negotiated simulataneously. We first analyse and validate the...

This paper studies the dynamic and equilibrium-selecting behavior of a multi-agent system consisting of adaptive bargaining agents. We model an adaptive agent as a collection of strategies which is optimized by an evolutionary algorithm (EA). EAs are stochastic search methods based upon the...

The ultimatum game is (in)famous for its `anomalies': The outcomes of laboratory experiments are very different from the results generated by traditional game theory. This paper aims to find to what extent these discrepancies between theory and experiments can be explained by the effects of...

We show how genetic algorithms can be used to evolve strategies in oligopolistic markets characterized by asymmetric competition. The approach is illustrated using scanner tracking data of brand actions in a real market. An asymmetric market-share model and a category-volume model are combined...

Genetic algorithms are a class of probabilistic optimization techniques that have proved useful in a wide variety of problem domains. This paper offers an introduction and overview to genetic algorithms and examines some of the finance-related applications to which the technique has been applied.