1 — This paper presents a new design automation tool based on a modified genetic algorithm kernel, in order to increase efficiency on the analog circuit and system design cycle. ...
We present a theory of a modeler's problem decomposition skills in the context of optimal reasonzng -- the use of qualitative modeling to strategically guide numerical explor...
Many real-world search and optimization problems naturally involve constraint handling. Recently, quite a few heuristic methods were proposed to solve the nonlinear constrained op...
This paper describes two experiments exploring the potential of the Kriging methodology for constrained simulation optimization. Both experiments study an (s, S) inventory system ...
William E. Biles, Jack P. C. Kleijnen, Wim C. M. V...
Abstract—A number of population based optimization algorithms have been proposed in recent years to solve unconstrained and constrained single and multi-objective optimization pr...
Hemant K. Singh, Amitay Isaacs, Trung Thanh Nguyen...
Biogeography-based optimization (BBO) is a new evolutionary algorithm based on the science of biogeography. We propose two extensions to BBO. First, we propose blended migration. ...
A parameter-less adaptive penalty scheme for steady-state genetic algorithms applied to constrained optimization problems is proposed. For each constraint, a penalty parameter is a...
In this paper, we propose a differential evolution algorithm to solve constrained optimization problems. Our approach uses three simple selection criteria based on feasibility to g...
Estimation of distribution algorithms (EDAs) are population-based heuristic search methods that use probabilistic models of good solutions to guide their search. When applied to co...
During the optimization of a constrained problem using evolutionary algorithms (EAs), an individual in the population can be described using three important properties, i.e., obje...