Multiobjective optimization strategy so-called Physical Programming allows controller designers a flexible way to express design preferences with a ’physical’ sense. For each objective (settling time, overshoot, disturbance rejection, etc.) preferences are established through categories as desirable, tolerable, unacceptable, etc. assigned to numerical ranges. The problem is translated into a un...

Self-modifying protocols (SMP) are protocols that can be modified at run time by the computers using them. Such protocols can be modified at run time so that they can adapt to the changing communicating environment and user requirements on the fly. Evolvable protocols are SMP designed using Genetic Algorithms (GA). The purpose of this article is to apply Genetic Algorithms (GA) to design an evo...

This article describes the design of highly complex physical instruments by using a canonical genetic algorithm GA . The procedure can be applied to all instrument designs where performance goals can be quantified. It is particularly suited to the optimization of instrument design where local optima in the performance figure of merit are prevalent. Here, a GA is used to evolve the design of the...

Genetic algorithms constitute a class of search algorithms especially suited to solving complex optimization problems in engineering. In addition to parameter optimization, genetic algorithms are also suggested for solving problems in creative design, such as combining components in a novel, creative way. Genetic algorithms (GA) transpose the notions of evolution in Nature to computers and imit...

The design of a gravity dam is performed through an interactive process involving a preliminary layout of the structure followed by a stability and stress analysis. This study presents a method to define the optimal top width of gravity dam with genetic algorithm. To solve the optimization task (minimize the cost of the dam), an optimization routine based on genetic algorithms (GAs) was impleme...

One key feature of Japanese products is that their design quality is robust—it remains unaffected by the deleterious impact of environmental or other factors often beyond the user’s control. As a result, designing and producing such high-quality products have become the challenge for all manufacturers aspiring to be world-class in today’s marketplace. This article shows how expressly crafted ge...

Optimal network ring design is a difficult problem characterised by the requirement to compare a large number of potential solutions (network designs). The problem of network ring design can be described as consisting of three parts: routing, link capacity assignment and ring determination. It has traditionally been broken down into a number of subproblems, solved in sequence, and usually by he...

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 present and discuss the results of an experimental analysis in the design of Boolean networks by means of genetic algorithms. A population of networks is evolved with the aim of finding a network such that the attractor it reaches is of required length l. In general, any target can be defined, provided that it is possible to model the task as an optimisation problem over the space of network...

A Proportional-Integral-Derivative (PID) control is the most commonly used algorithm in industrial control systems. With its three term functionality providing regulation to both transient and steady state responses, PID control offers the simplest and most efficient solution to many industrial control problems. Tuning of PID gain parameters continuous to be important as these parameters has gr...