This paper presents genetic algorithms for solving various reliability design problems, which include series systems, series-parallel systems and complex (bridge) systems. The objective is to maximize the system reliability, while maintaining feasibility with respect to three nonlinear constraints, namely, cost and weight constraints, and constraints on the products of volume and weight. In thi...

We apply a genetic algorithm (GA) to optimize a reliability design problem using a neural network as a reliability estimator for calculation of the objective function value during the search. The GA produces the lowest cost option for a required system reliability by selecting the appropriate parts and levels of redundancy. We show that the neural network estimator is effective and computationa...

It is expected that nano-scale devices and interconnections will introduce unprecedented level of defects in the substrates, and architectural designs need to accommodate the uncertainty inherent at such scales. This consideration motivates the search for new architectural paradigms based on redundancy based defect-tolerant designs. However, redundancy is not always a solution to the reliabilit...

Evolutionary computation algorithms are increasingly being used to solve optimization problems as they have many advantages over traditional optimization algorithms. In this paper we use evolutionary computation to study the trade-off between pleiotropy and redundancy in a client-server based network. Pleiotropy is a term used to describe components that perform multiple tasks, while redundancy...

Abstract: The rapid advancements in science and technology aggravate the need for the optimal design of modern systems, aiming to achieve the maximum system reliability while meeting some resource constraints (e.g., cost, weight, etc). In this paper, we consider the system reliability optimization for series-parallel systems subject to commoncause failures (CCF). Unlike traditional approaches t...