Results from our optimization exercise clearly show the advantage of using the random search algorithms when we anticipate the search for the global optimum to be difficult. When the number of parameters in the model is relatively small (nine parameters) Differential Evolution performs better...

The paper is concerned with genetic algorithm learning in a cobweb economy. Besides discussing several specification details in the genetic operators, the model includes four different types of firm forecasting rules and subjects the demand side to serially correlated random shocks. The main...

This paper applies a floating-point genetic algorithm to an insurance model in which the principals who underwrite insurance contracts cannot observe the risk characteristics of the agents who sign those contracts. Under perfect rationality, the equilibria of this model are analogous to those in...

One way of thinking about research and development is to recognise that firms are trying to solve particular design problems. We often build these design problems into our models, but are forced to oversimplify them in order to make the models solvable. The approach taken in this paper is to...

Uncertainty contributes a major part in the accuracy of a decision-making process while its inconsistency is always difficult to be solved by existing decision-making tools. Entropy has been proved to be useful to evaluate the inconsistency of uncertainty among different respondents. The study...

The nearest correlation matrix problem is to find a valid (positive semidefinite) correlation matrix, R(m,m), that is nearest to a given invalid (negative semidefinite) or pseudo-correlation matrix, Q(m,m); m larger than 2. In the literature on this problem, 'nearest' is invariably defined in...

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...