A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the num- ber of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Al- gorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which repre- sents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the- loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits

A bi-objective genetic algorithm approach to risk mitigation in project scheduling

A problem of risk mitigation in project scheduling is formulated as a bi-objective optimization problem, where the expected makespan and the expected total cost are both to be minimized. The expected total cost is the sum of four cost components: overhead cost, activity execution cost, cost of reducing risks and penalty cost for tardiness. Risks for activities are predefined. For each risk at an activity, various levels are defined, which correspond to the results of different preventive measures. Only those risks with a probable impact on the duration of the related activity are considered here. Impacts of risks are not only accounted for through the expected makespan but are also translated into cost and thus have an impact on the expected total cost. An MIP model and a heuristic solution approach based on genetic algorithms (GAs) is proposed. The experiments conducted indicate that GAs provide a fast and effective solution approach to the problem. For smaller problems, the results obtained by the GA are very good. For larger problems, there is room for improvement

Intelligent design of manufacturing systems.

The design of a manufacturing system is normally performed in two distinct stages, i.e. steady state design and dynamic state design. Within each system design stage a variety of decisions need to be made of which essential ones are the determination of the product range to be manufactured, the layout of equipment on the shopfloor, allocation of work tasks to workstations, planning of aggregate capacity requirements and determining the lot sizes to be processed. This research work has examined the individual problem areas listed above in order to identify the efficiency of current solution techniques and to determine the problems experienced with their use. It has been identified that for each design problem. although there are an assortment of solution techniques available, the majority of these techniques are unable to generate optimal or near optimal solutions to problems of a practical size. In addition, a variety of limitations have been identified that restrict the use of existing techniques. For example, existing methods are limited with respect to the external conditions over which they are applicable and/or cannot enable qualitative or subjective judgements of experienced personnel to influence solution outcomes. An investigation of optimization techniques has been carried out which indicated that genetic algorithms offer great potential in solving the variety of problem areas involved in manufacturing systems design. This research has, therefore, concentrated on testing the use of genetic algorithms to make individual manufacturing design decisions. In particular, the ability of genetic algorithms to generate better solutions than existing techniques has been examined and their ability to overcome the range of limitations that exist with current solution techniques. IIFor each problem area, a typical solution has been coded in terms of a genetic algorithm structure, a suitable objective function constructed and experiments performed to identify the most suitable operators and operator parameter values to use. The best solution generated using these parameters has then been compared with the solution derived using a traditional solution technique. In addition, from the range of experiments undertaken the underlying relationships have been identified between problem characteristics and optimality of operator types and parameter values. The results of the research have identified that genetic algorithms could provide an improved solution technique for all manufacturing design decision areas investigated. In most areas genetic algorithms identified lower cost solutions and overcame many of the limitations of existing techniques

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method

A novel haptic model and environment for maxillofacial surgical operation planning and manipulation

This paper presents a practical method and a new haptic model to support manipulations of bones and their segments during the planning of a surgical operation in a virtual environment using a haptic interface. To perform an effective dental surgery it is important to have all the operation related information of the patient available beforehand in order to plan the operation and avoid any complications. A haptic interface with a virtual and accurate patient model to support the planning of bone cuts is therefore critical, useful and necessary for the surgeons. The system proposed uses DICOM images taken from a digital tomography scanner and creates a mesh model of the filtered skull, from which the jaw bone can be isolated for further use. A novel solution for cutting the bones has been developed and it uses the haptic tool to determine and define the bone-cutting plane in the bone, and this new approach creates three new meshes of the original model. Using this approach the computational power is optimized and a real time feedback can be achieved during all bone manipulations. During the movement of the mesh cutting, a novel friction profile is predefined in the haptical system to simulate the force feedback feel of different densities in the bone

Meta-Stability of Interacting Adaptive Agents

The adaptive process can be considered as being driven by two fundamental forces: exploitation and exploration. While the explorative process may be deterministic, the resultant effect may be stochastic. Stochastic effects may also exist in the expoitative process. This thesis considers the effects of stochastic fluctuations inherent in the adaptive process on the behavioural dynamics of a population of interacting agents. It is hypothesied that in such systems, one or more attractors in the population space exist; and that transitions between these attractors can occur; either as a result of internal shocks (sampling fluctuations) or external shocks (environmental changes). It is further postulated that such transitions in the (microscopic) population space may be observable as phase transitions in the behaviour of macroscopic observables. A simple model of a stock market, driven by asexual reproduction (selection plus mutation) is put forward as a testbed. A statistical dynamics analysis of the behaviour of this market is then developed. Fixed points in the space of agent behaviours are located, and market dynamics are compared to the analytic predictions. Additionally, an analysis of the relative importance of internal shocks(sampling fluctuations) and external shocks( the stock dividend sequence) across varying population size is presented

WEB TABANLI SANAL GENETİK ALGORİTMA LABORATUARI

      İnternet teknolojilerindeki gelişmeler, öğrenme ihtiyaçlarının farklılaşması, bireylerin daha esnek ve kişiselleştirilmiş bir öğrenme ortamını talep etmeleri internet  tabanlı uzaktan öğretim modellerinin oluşturulmasına zemin hazırlamaktadır. Uzaktan  eğitim kendi arasında çeşitli kategorilere ayrılmakta, uzak sanal laboratuarlar da bu kategoriler arasında ele alınmaktadır. Bu çalışmada, genetik algoritmalar (GA) konusunda örnek deneyler içeren uzak sanal laboratuar uygulamalarının gerçekleştirildiği bir deney sistemi hazırlanmıştır. Genelde, GA son derece teorik derslerden biridir ve bir öğrencinin programlama dillerinden herhangi birinde GA uygulaması yazması oldukça zordur. Ayrıca, yazılmış bir programda; nüfus büyüklüğü, çaprazlama oranı, mutasyon oranı ve kodlama biçimi gibi parametrelerin en iyi değerlerini bulmak zaman alıcı bir iştir. Bu zorlu adımların gerçekleştirilmesinden sonra, öğrenciler GA’nın çalışmasını irdeleyebilmektedirler. Oysaki çalışmamızda, öğrenciler tek bir satır program kodu dahi yazmadan, İnternet üzerinden uzak sanal GA laboratuarına erişerek, GA’nın çalışma ve performansını inceleyebilmektedirler. Çalışmada; Matlab, Matlab Web Sunucu, Apache Sunucu, PHP ve Javascript gibi yazılım araçları kullanılmıştır