Genetic Algorithm based Tuning for Enhancing Timing Performance of a Switch Capacitor Array Waveform Digitizer Circuit

The chapter covers two main areas, these being an introduction to the technology and techniques associated with genetic algorithms and then the second part looks at how genetic algorithms can be used to search for good topological solutions to engineering design challenges. The start of the chapter places genetic algorithms in context compared to other evolutionary algorithms and also describes the reasons why genetic algorithms are potentially useful. This is then followed by a look at the concept of a search space. Section two looks at the canonical genetic algorithm as a basic introduction to the technology and includes an examination of the main techniques used to encode the genome, fitness functions, operators and selection. Section three looks at how genetic algorithms can be used for design and chooses the specific example of the conceptual design of commercial office buildings. Section four introduces the basic concepts of topological search and explains how having the right form of representation is vital before looking at example relating to structural components and the design of domes using a genetic algorithm linked to computational geometry techniques. The final section then looks at further methods using generative representations and generative geometries as possible solutions to the need to develop powerful forms of representation for handling topological search in genetic algorithms.

In many industries including metallurgy, petrochemicals, paper, aerospace, pharmaceutical, electronics, ceramic and automotive manufacturing, the production system can contain a machine bottleneck which processes, in some cases, all the jobs. The management of this machine is crucial for the enterprise because it is responsible for delays in customers' orders delivery. The jobs scheduling on this machine is a way of approaching the problem and treats the whole production system. The single machine model provides a basis for more complicated environment such those seen in many real cases. Also, several studies have shown that several jobs, if not all, have time-dependent setup times. So, decision makers must therefore organize the jobs scheduling on this single machine by trying to minimize downtime while respecting the different deadlines. On the other hand, several resolution methods have been developed in literature to solve this kind of problem using approaching methods like metaheuristics or hybrid methods integrating exact methods. This thesis is in this direction. In this research, we propose several effective approaches to solve the single machine scheduling problem with sequence-dependent setup times with the objective to minimize total tardiness of the jobs. First, a new crossover operator in a genetic algorithm is proposed. This algorithm shows its ability to be competitive with the best approaches found in the literature on benchmark problems. We thus highlight the importance of incorporating specific problem knowledge into genetic operators, even if classical genetic operators could be used. However, the results obtained show that the proposed genetic algorithm is less efficient in comparison to the best resolution method found in the literature. Indeed, we noticed that the proposed algorithm lacks an intensification process for enhancing its. To overcome this shortcoming, we explore resolution class methods that showed promising prospects in the last decade. Even, the hybrid algorithms have achieved very good results in a wide variety of problems. In recent years, several researchers have introduced the hybridization of metaheuristics with exact methods. In fact, this kind of hybridization can be an attractive alternative because both methods have very different characteristics that can be combined to produce better results. Thus we explore the design of hybrid genetic algorithms improving the intensification process of the proposed genetic algorithm by integrating an exact method and mechanisms from other resolution methods to improve the performance. However, we find few approaches hybridizing metaheuristics and constraint programming in the literature to solve scheduling problems. In a second step, we propose a modeling for the total tardiness single machine with setup-dependent times scheduling problem using the constraint-based scheduling and the ILOG C++ API. The related experiments show that algorithm efficiency improves with specific tree search strategies and heuristic scheduling variables. Under prohibitive time computing, the proposed approach results are generally far from the best known solutions. However, we got interesting performance on small instances to consider allowing its hybridization with other resolution methods. In a third step, we introduce a hybrid approach based on the integration between genetic algorithm and constraint based scheduling approaches. This latter approach is integrated in the reproduction and the intensification processes of a genetic algorithm autonomously. Finally, we propose a hybrid algorithm which integrates several concepts from constraint programming, multi-objective optimization and ant colonies optimization in a crossover operator into a genetic algorithm. The proposed hybrid algorithms show excellent performance on benchmark problems from the literature by improving many of the best known solutions for some of them.

Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms

Development and validation of a hybrid aerodynamic design method for curved diffusers using genetic algorithm and ball-spine inverse design method

Facility layout is an important aspect of designing any manufacturing setup. However, the problem of finding optimal layouts is hard and deterministic techniques are not computationally feasible. In this work a genetic algorithm is presented for obtaining efficient layouts. The different aspects involved in the design of efficient genetic algorithms are discussed in detail. It is shown that the population maintained by the genetic algorithm for facility layout should consist of feasible solutions only. A new efficient crossover operator is developed. Experimental results obtained with the proposed algorithm on test problems taken from the literature are promising.

SHRNUTÍ Tento članek popisuje metodu automatickeho navrhu siluety vozu. V průběhu historie vzniklo mnoho automobilů jejichž rysy jsou do značne miry podobne. Tyto rysy (siluety) můžeme extrahovat pomoci statisticke metody analyzy hlavnich komponent a použit je pro navrh nove siluety, ktera je dale optimalizovana pomoci genetickych algoritmů. Proces optimalizace zohledňuje zadane fyzikalni parametry jako je objem prostoru pro motor, zavazadla a pasažery. Vysledek produkovany algoritmem pak slouži jako inspirace pro člověka-designera a může byt zobecněn na vybrany typ produktu.

NeuroGraph is a simulation environment for design, construction and execution of neural networks and genetic algorithms in a distributed computing environment. The simulator parts either run on single computers or as distributed applications on UNIX/X-based networks consisting of personal computers, workstations or multi-processors. The parallelization component offers the possibility to divide computational tasks into concurrently executable modules, according to restrictions due to the neural net topology and computer net capabilities, i.e. NeuroGraph tries to select the best configuration out of the available distributed hardware environment to fit performance requirements.

Designing large structures like dams requires carefully selecting various geometric, hydraulic, and structural characteristics. The required structural design and performance criteria are considered when selecting these characteristics. In order to find the best solution, a variety of restrictions must simultaneously be carefully taken into account. This study presents an effective method for determining the optimal shape design for concrete buttress dams. The research was divided into two crucial phases. The dam's initial design and subsequent modeling were mostly done using DIANA FEA and traditional design and stability analysis. After that, a genetic algorithm was used on the MATLAB platform to control optimizing the dam's shape. Three design factors were used in this phase to alter the goal function and to reduce the amount of Concrete used, which decreased project costs. These variables covered three areas of the buttress's cross-section. Two important limitations were scrutinized during this optimization process: establishing a safety margin against overtopping and preventing sliding. The analysis included a detailed assessment of Shear friction stability to complete a thorough stability study. The optimization efforts had a spectacular result, resulting in a significant 52.365% reduction in the total volume of Concrete used, dropping from 19147.5 cubic meters to 9122.55 cubic meters. This decrease was made possible by reducing three distinct components (X1, X2, X3), with respective proportions of 37.5%, 13.33%, and 30%, including two segments related to the buttress and the final segment linked (slab) to the strip footing.

Thermal and integral lifting analysis of a lattice shell lighting roof based on genetic algorithm and probabilistic design

Ecological Models, Optimization

A genetic algorithm is used for design of infrared filters and in the understanding of the material structure of a resonant tunneling diode. These two components are examples of microdevices and nanodevices that can be numerically simulated using fundamental mathematical and physical models. Because the number of parameters that can be used in the design of one of these devices is large, and because experimental exploration of the design space is unfeasible, reliable software models integrated with global optimization methods are examined. The genetic algorithm and engineering design codes have been implemented on massively parallel computers to exploit their high performance. Design results are presented for the infrared filter showing new and optimized device design.

This paper presents the design of a logic circuit, whose topology is defined through an optimization process carried out by a genetic algorithm with the aim of obtaining an optimal tic-tac-toe player. To achieve this objective, the optimization process is performed by a simple genetic algorithm, and the problem is framed as a maximization problem. Given a logic circuit and a set of Boolean weights on its vertices, the goal is to optimize the performance of the logic circuit as a tic-tac-toe player by optimizing the weights that define its topology, where each set of weights defines a unique configuration and, therefore, a unique circuit. The genetic algorithm treats these sets of weights as individuals in its population. The design of the genetic algorithm involves evaluating various configurations of logic circuits, objective functions, and training models. The presented results showcase an optimal set of these configurations, as well as an analysis of the best player obtained.

In this paper a software environment, called DAGC, is described. The main idea behind the design of DAGC is to facilitate research works in design and development of genetic clustering algorithms for automatic re-modularization of software systems. Within the DAGC environment, clustering algorithms may be assembled or modified by simply selecting the parts from an extendable list of components. Using this distinguishing feature of the DAGC framework, a new algorithm with a new encoding and crossover operator was evolved.

A key challenge for the future is to reduce drastically the human impact on the environment. In the aeronautic field, this challenge aims at optimizing the design of the aircraft to decrease the global mass. This reduction leads to the optimization of every part constitutive of the plane. This operation is even more delicate when the used material is composite material. In this case, it is necessary to find a compromise between the strength, the mass and the manufacturing cost of the component. Due to these different kinds of design constraints it is necessary to assist engineer with decision support system to determine feasible solutions. In this paper, an approach is proposed based on the coupling of the different key characteristics of the design process and on the consideration of the failure risk of the component. The originality of this work is that the manufacturing deviations due to the RTM process are integrated in the simulation of the assembly process. Two kinds of deviations are identified: volume impregnation (injection phase of RTM process) and geometrical deviations (curing and cooling phases). The quantification of these deviations and the related failure risk calculation is based on finite element simulations (Pam RTM® and Samcef® softwares). The use of genetic algorithm allows to estimate the impact of the design choices and their consequences on the failure risk of the component. The main focus of the paper is the optimization of tool design. In the framework of decision support systems, the failure risk calculation is used for making the comparison of possible industrialization alternatives. It is proposed to apply this method on a particular part of the airplane structure: a spar unit made of carbon fiber/epoxy composite.

Physical and virtual K&C analyses are performed to achieve the vehicle dynamics targets by finding the optimum variables such as the position of hardpoints or stiffnesses of bushings. However, finding appropriate design variables that meet all the aims is challenging. This paper evaluates a hardpoint optimization approach to attain suspension K&C characteristic objectives with the design of experiments, neural networks, and genetic algorithm, based on a reference compact-sized prototype vehicle. The MBD model correlation is provided to optimize the hardpoints to improve the vehicle's steering kinematics concerning Ackerman error and camber angle variation that are out of target in baseline suspension. The results showed that NN based optimization strategy to define the hardpoints has significantly improved targeted characteristics compared to conventional response surface methods in the limited design space.