We use an expected utility framework to integrate the hedge funds survival uncertainty into an asset allocation optimizartion model. The addition of investment constraints complicates the resolution of the optimal allocation problem. It is solved using a genetic algorithm that mimics the...

We investigate the ability of a genetic algorithm to design cellular automata that perform computations. The computational strategies of the resulting cellular automata can be understood using a framework in which "particles" embedded in space-time configurations carry information and...

Technological change at the firm level has commonly been modeled as random sampling from a fixed distribution o f possibilities. Such models, however, typically ignore empirically important aspects of the firm's search process, notably the observation that the present state of the firm guides...

Nowadays railway companies are confronted with high infrastructure maintenance costs. Therefore good strategies are needed to carry out these maintenance activities in a most cost effective way. In this paper we solve the preventive maintenance scheduling problem (PMSP) using genetic algorithms,...

This paper addresses the problem of scheduling economic lots in a multi-product single machine environment. A mixed integer non-linear programming formulation is developed which finds the optimal sequence and economic lots. The model takes explicit account of initial inventories, setup times,...

Social insects---ants, bees, termites and wasps---exhibit a collective problem-solving ability (Deneubourg and Goss, 1989; Bonabeau et al., 1997). In particular, several ant species are capable of selecting the shortest pathway, among a set of alternative pathways, from their nest to a food source...