Pub. online:1 Jan 1995Type:Research ArticleOpen Access
Volume 6, Issue 3 (1995), pp. 289–298
We compare two alternative ways to use the Bayesian approach in heuristic optimization. The “no-learning” way means that we optimize the randomization parameters for each problem separately. The “learning” way means that we optimize the randomization parameters for some “learning” set of problems. We use those parameters later on for a family of related problems.
We define the learning efficiency as a non-uniformity of optimal parameters while solving a set of randomly generated problems. We show that for flow-shop problems the non-uniformity of optimal parameters is significant. It means that the Bayesian learning is efficient in those problems.
Pub. online:1 Jan 1994Type:Research ArticleOpen Access
Volume 5, Issues 3-4 (1994), pp. 338–350
In this paper we consider the numerical solution of the large system of nonlinear differential equations. We assume that the system simulates the semiconductor circuit. We apply the well known event driven techniques to get some approximation of solution fast. We extend those techniques by considering pairs of nodes, instead of single nodes, as usual. The “pairwise” solution is more efficient in tightly coupled circuits. The improvement of efficiency of solution is important optimizing the parameters of circuits. In many cases we need the global optimization of equation parameters. Then, the modeling speed could be the main factor of successful application.