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The aim of this paper is to design a position controller of a DC motor by selection of a PID parameters using genetic algorithm. The model of a DC motor is considered as a third order system. And this paper compares two kinds of tuning methods of parameter for PID controller. One is the controller design by the genetic algorithm, second is the controller design by the Ziegler and Nichols method. It was found that the proposed PID parameters adjustment by the genetic algorithm is better than the Ziegler & Nichols' method. The proposed method could be applied to the higher order system also. [ABSTRACT FROM AUTHOR]

This paper studies the simultaneous scheduling of landside container handling operations in a container terminal. Issues addressed include scheduling the sequence of loading (unloading) of containers to (from) the vessels from (to) the quayside, assigning trucks to transport containers between quayside and yard side, and scheduling operations of yard cranes in different yard zones. A mathematical model describing the characteristics of the problem is developed. The objective is to minimize the total completion time for handling all the containers under consideration in the terminal. As optimizing the landside container handling operations in a terminal is known to be NP-hard, a new genetic algorithm with the selection process based on the principle of simulated annealing is developed in this paper to solve the problem. Comparison of the respective results obtained by using the proposed genetic algorithm, the canonical genetic algorithm and the simulated annealing algorithm clearly shows that the total completion times obtained by the proposed algorithm are 12%-18% shorter than that obtained by GA and SA, and the computing times of GA-SA are only 50% of that of GA. The proposed genetic algorithm is indeed superior to the other two algorithms. [ABSTRACT FROM AUTHOR]

A standard genetic algorithm is asexual. To mimic nature more closely, this paper incorporates gender in a genetic algorithm. Two individuals from opposite sex are permitted for reproduction. Earlier researchers considered that the gender of the offspring depends on the gender of the individual removed from the population. Thus the number of males and females are kept constant. This assumption was relaxed by introducing randomness during gender assignment to the offspring after the crossover operation. However, due to the stochastic nature of the process, this may generate a population of single sex which leads to no regeneration being possible. This paper introduces adaptive gender assignment to the offspring so that the gender of the offspring is decided based on the gender density in the population. The proposed algorithm is applied to design a pressure vessel. [ABSTRACT FROM AUTHOR]

This paper presents an architecture which is suitable for a massive parallelization of the compact genetic algorithm. The resulting scheme has three major advantages. First, it has low synchronization costs. Second, it is fault tolerant, and third, it is scalable. The paper argues that the benefits that can be obtained with the proposed approach is potentially higher than those obtained with traditional parallel genetic algorithms. [ABSTRACT FROM AUTHOR]

One of the most promising methods of interacting with small portable computing devices, such as personal digital assistants, is the use of handwriting. In order to make this communication method more natural, we proposed to visually observe the writing process on ordinary paper and to automatically recover the pen trajectory from numerical tablet sequences. On the basis of this work we developed handwriting recognition system based on visual coding and genetic algorithm. The system was applied on Arabic script. In this paper we will present the different steps of the handwriting recognition system. We focus our contribution on genetic algorithm method. [ABSTRACT FROM AUTHOR]

In terms of goal orientedness, selection is the driving force of Genetic Algorithms (GAs). In contrast to crossover and mutation, selection is completely generic, i.e. independent of the actually employed problem and its representation. GA-selection is usually implemented as selection for reproduction (parent selection). In this paper we propose a second selection step after reproduction which is also absolutely problem independent. This self-adaptive selection mechanism, which will be referred to as offspring selection, is closely related to the general selection model of population genetics. As the problem- and representation-specific implementation of reproduction in GAs (crossover) is often critical in terms of preservation of essential genetic information, offspring selection has proven to be very suited for improving the global solution quality and robustness concerning parameter settings and operators of GAs in various fields of applications. The experimental part of the paper discusses the potential of the new selection model exemplarily on the basis of standardized real-valued test functions in high dimensions. [ABSTRACT FROM AUTHOR]

The optimal sizing of a small autonomous hybrid power system can be a very challenging task, due to the large number of design settings and the uncertainty in key parameters. This problem belongs to the category of combinatorial optimization, and its solution based on the traditional method of exhaustive enumeration can be proved extremely time-consuming. This paper proposes a binary genetic algorithm in order to solve the optimal sizing problem. Genetic algorithms are popular optimization metaheuristic techniques based on the principles of genetics and natural selection and evolution, and can be applied to discrete or continuous solution space problems. The obtained results prove the performance of the proposed methodology in terms of solution quality and computational time. [ABSTRACT FROM AUTHOR]

The motivation for this paper is to develop an approach to optimization of beam design. Under given loading and support conditions, the comparative strength of three (3) common structural shapes was determined. This led to the conclusion that a particular structural shape together with its dimensions will give the optimal solution in beam design in terms of the least cross-sectional area to support the given load, which would then translate to savings in cost and reduction in weight of the structural member. An investigation was also conducted to take into consideration the effect in the dimensions of the structural shapes of uncertainties due to manufacturing limitations and tolerances. This resulted in an assessment of the order of magnitude of this effect on the design variables. In solving the resulting optimization problems, MATLAB's Genetic Algorithm and Direct Search Toolbox was employed. [ABSTRACT FROM AUTHOR]

This paper presents a new technique for designing a robust DC motor speed controller based on the concept of fixed-structure robust controller and a mixed sensitivity method. The uncertainty caused by the parameter changes of motor resistance, motor inductance and load are formulated as multiplicative uncertainty weight, which are used in the objective function in the design. Performance weight is designed based on the closed-loop objective which is normally applied in H∞ optimal control. Particle Swarm Optimization (PSO) is adopted to solve the optimization problem and find the optimal controller. The proposed technique can solve the problem of complicated and high order controller of conventional H∞ optimal control and also retains the robust performance of conventional H∞ optimal control. Simulation results in a DC motor speed control system show the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

A new state space representation of a class of combinatorial optimization problems is introduced. The representation enables efficient implementation of exhaustive search for an optimal solution in bounded NP complete problems such as the traveling salesman problem (TSP) with a relatively small number of cities. Furthermore, it facilitates effective heuristic search for sub optimal solutions for problems with large number of cities. This paper surveys structures for representing solutions to the TSP and the use of these structures in iterative hill climbing (ITHC) and genetic algorithms (GA). The mapping of these structures along with respective operators to a newly proposed electro-optical vector by matrix multiplication (VMM) architecture is detailed. In addition, time space tradeoffs related to using a record keeping mechanism for storing intermediate solutions are presented and the effect of record keeping on the performance of these heuristics in the new architecture is evaluated. Results of running these algorithms on sequential architecture as well as a simulation-based estimation of the speedup obtained are supplied. The results show that the VMM architecture can speedup various variants of the TSP algorithm by a factor of 30x to 50x. [ABSTRACT FROM AUTHOR]

This paper proposes a new location problem of competitive facilities, e.g. shops. In the most studies of competitive facility location, the distance between the facilities and their customers is represented as the Euclid distance. The proposing location problem introduces the A-distance, proposed by Widmayer etc., for representing the situation that the directions which customers can move are given. For solving the formulated facility location problem efficiently, it is shown that the problem is reformulated as a combinatorial optimization problem, and its solving method based on genetic algorithms is proposed. The efficiency of the solving method is shown by applying to several examples of the competitive facility location problems. [ABSTRACT FROM AUTHOR]

In this paper a new method for optimization of Elastic Bunch Graph Matching (EBGM) algorithm in frontal face recognition is presented. In EBGM algorithm, some pre-determined wavelength of Gabor wavelet is used to extract features from face image. For optimization of EBGM algorithm, Genetic Algorithm (GA) is used to select the best wavelengths of Gabor wavelet. For evaluation, algorithm has been tested on 300 classes of FERET face database. In training phase, only one image per class is trained. The recognition rate of optimized EBGM is about 91%. Also the optimized EBGM can run 1.5 times faster than original EBGM. [ABSTRACT FROM AUTHOR]

Several statistical tools and most recently Functional Networks (FN) have been used to solve nonlinear regression problems. One of the tasks associated with all of these methodologies consists of discovering the functional form of the contribution of the explanatory variables to the response variable. In this paper, we tackle this problem using functional network models (FNs). Since these models usually involve from a moderate to high number of parameters, a genetic algorithm (GA) for model selection is proposed. After an introduction of FNs and GAs, the performance of the proposed methodology is assessed using a simulation study as well as a real-life data set. [ABSTRACT FROM AUTHOR]

In this paper, hybrid particle swarm optimization (PSO) is proposed for solving the challenging multi-contingency transient stability constrained optimal power flow (MC-TSCOPF) problem. The objective of this nonlinear optimization problem is to minimize the total fuel cost of the system and at the same time fulfil the transient stability requirements. The optimal power flow (OPF) with transient stability constraints considered is re-formulated as an extended OPF with additional rotor angle inequality constraints, which is suitable for hybrid PSO to solve. Comparison between various existing hybrid PSO techniques is carried out by solving the New England 39-bus system. Experimental results indicate that the hybrid PSO integrated with the mutation operation of genetic algorithms is better than the other existing hybrid PSO methods in both solution quality and stability. [ABSTRACT FROM AUTHOR]

This paper presents a detailed comparison between a conventional PI controller and a variable structure controller based on a fuzzy sliding mode strategy used for speed control in direct torque control induction motor drive. Genetic algorithms are used to tune the PI controller gains to ensure optimal performance. The performance of the two controllers are investigated and compared for different dynamic operating conditions such as of reference speed and for load torque step changes at nominal parameters and in the presence of parameter variation and imprecision. Results show that the PI controller has better performance for nominal operating conditions while the fuzzy sliding mode is more robust against parameter variation and uncertainty, and is less sensitive to external load torque disturbances with a fast dynamic response. [ABSTRACT FROM AUTHOR]

The increased usage of embedded pre-designed reusable cores necessitates a core-based test strategy in which cores are tested as separate entities. Test application time is a major issue in System-on-Chip Testing (SOC). Pre-designed cores and reusable modules are popularly used in the design of large and complex systems. As the complexity of system increases, the test application time also significantly increases. Available techniques for testing of core-based SOC do not provide a systematic means of compact test solutions. The test application time must be minimized to transport test data to and from the cores. In this paper, we present a Genetic Algorithm (GA)-based approach to optimize the test vectors for globally asynchronous locally synchronous SOC Benchmark Circuits. This approach provides optimal results comparable to other methods of similar problems. Based on our experiments, the test results for four ITC-02 SOC Test Benchmark circuits are presented. The results of GA-based approach are shown to be superior to the heuristic approaches proposed in the literature. [ABSTRACT FROM AUTHOR]

In this paper we used genetic algorithms to 1 find the solution of game theory. We proposed new method foe solving game theory and find the optimal strategy for player A or player B. We can benefit from the relationship between game theory and the linear programming to find the fitness function and tested this fitness function at different examples . [ABSTRACT FROM AUTHOR]

The goal of resource leveling is to minimize the deviation between the resource requirements and the desired resource profile to prevent problems that caused by fluctuation of required resources. It is a common problem and arises frequently in real situations and therefore has been studied numerous times. Almost in all of these studies and the resulting solutions, there exist a common element, which is once an activity is started, it cannot be stopped and restarted again. That is, it cannot be split. In many instances in actual construction, there exist activities that can be split. So, it seems to be very useful to develop a model for resource leveling problems that allows certain activities to be split. An important interesting property that such a model should have is its simplicity. By simplicity we mean its ease of use by inexpert users without strong mathematical background. This paper presents a model based on genetic algorithms to level resources that permits selected activities to stop and restart. This splitting of activities results in improvement to the leveling solution that is traditionally achieved when splitting is not allowed. Examples are presented that illustrate the improvement in the solution obtained from the proposed model compared to models that do not allow splitting and compares the result to that obtained using commercially available software. The model presented here is very efficient and effective. Therefore, it can be used in real situations to automatically solve resource leveling problems efficiently to obtain effective results. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is a stochastic and population-based search algorithm that demonstrates its effctiveness in solving complex nonlinear optimization problems. Although the original PSO is very simple and effective, how to determine appropriate values of parameters in PSO is yet to be found. This paper proposes a novel method called evolutionary PSO, which estimates values of parameters in PSO for effectively finding globally optimal parameter values by a real-coded genetic algorithm. A crucial idea here is to adopt a temporary cumulative fitness instead of instantaneous fitness in a real-coded genetic algorithm for evaluating the performance of the PSO. It provides a useful measure that efficiently determines appropriate values of parameters in PSO. To demonstrate the effectiveness of the proposed method, we implement a simple computer experiment on a 2-dimensional optimization problem, and analyze the characteristics of dependency on initial condition. [ABSTRACT FROM AUTHOR]

Data Mining consists of an evolving set of techniques that can be used to extract valuable Information and knowledge from massive volumes of data. Data Mining Research and tools have focused on commercial sector applications. This Research paper highlights the data mining techniques applied to mine for Location and Mobility Management. Location Management is a very important and complex problem in today's mobile computing environments. There is a need to develop algorithms that could capture this complexity, yet can be easily implemented and used to solve a wide range of Location Management scenarios. In the Reporting cell Location Management scheme, the mapping is done on the basis of classification. Some cells in the network are designated as Reporting cells; Mobile terminals update their positions (Location Update) upon entering one of these reporting cells. The remaining cells are designated as Non-reporting cells. In the proposed scheme, an supervised learning technique is being introduced which maintains the history or mobility pattern (of size h) of the last visited reporting cell. The updation does not take place, when the user roams with in the reporting cells of his mobility pattern. The location management is updated when the user enters in to the new reporting cell, which is not in his history. As a result the updation cost is proportionately reduced with the value of h (the number of entries in the history) Artificial life techniques have been used to solve a wide range of complex problems in recent times. The power of these techniques stems from the capability in searching large search spaces, which arise in many combinatorial optimization problems very efficiently. To create such a planner, Genetic Algorithm has been implemented to show that the total cost is less when compared with the existing cost based updation and searching scheme. [ABSTRACT FROM AUTHOR]

This paper discusses a novel performance driven placement technique based on distributed Genetic Algorithms, and focuses particularly on the following points:(l) The algorithm has two-level hierarchical structure consisting of outline placement and detail placement. (2) For selection control, which is one of the genetic operations, new multi-objective functions are introduced. (3) In order to reduce the computation time, a parallel processing is introduced. Results show improvement of 22.5% for worst path delay, 11.7% for power consumption, 15.9% for wire congestion and 10.7% for chip area. [ABSTRACT FROM AUTHOR]

Temperature modelling of a homogeneous medium, when this medium is radiated by therapeutic ultrasound, is a fundamental step in order to analyse the performance of estimators for in-vivo modelling. In this paper punctual and invasive temperature estimation in a homo-geneous medium is employed. Radial Basis Functions Neural Networks (RBFNNs) are used as estimators. The best fitted RBFNNs are selected using a Multi-objective Genetic Algorithm (MOGA). An absolute average error of 0.0084°C was attained with these estimators. [ABSTRACT FROM AUTHOR]

Locking caches, providing full determinism and good performance, are a very interesting solution to replacing conventional caches in real-time systems. In such systems, temporal correctness must be guaranteed. The use of predictable components, like locking caches, helps the system designer to determine if all the tasks will meet its deadlines. However, when locking caches are used in a static manner, the system performance depends on the instructions loaded and locked in cache. The selection of these instructions may be accomplished through a genetic algorithm. This paper shows the impact of the fitness function in the final performance provided by the real-time system. Three fit- ness functions have been evaluated, showing differences in the utilisation and performance obtained. [ABSTRACT FROM AUTHOR]

This paper studies the global optimisation of bioprocesses employing model-based dynamic programming schemes. Three stochastic optimisation algorithms were tested: simulated annealing, adaptive random search and genetic algorithms. The methods were employed for optimising two challenging optimal control problems of fed-batch bioreactors. The main results show that adaptive random search and genetic algorithms are superior at solving these problems than the simulated annealing based method, both in accuracy and in the number of function evaluations. [ABSTRACT FROM AUTHOR]

This paper proposes a new selection method for Genetic Algorithms. The motivation behind the proposed method is to investigate the effect of different selection methods on the rate of convergence. The new method Dynamic Selection Method (DSM) is based on proportionate selection. DSM functions by continuously changing the criteria for parent selection (dynamic) based on the number of generations in a run and the current generation. Results show that by using DSM to maintain diversity in a population gives slower convergence, but, their overall performance was an improvement. Relationship between slower convergences, in GA runs, leading to better solutions, has been identified. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to the evolution of blackjack strategies, that of cultural learning. Populations of neural network agents are evolved using a genetic algorithm and at each generation the best performing agents are selected as teachers. Cultural learning is implemented through a hidden layer in each teacher’s neural network that is used to produce utterances which are imitated by its pupils during many games of blackjack. Results show that the cultural learning approach outperforms previous work and equals the best known non-card counting human approaches. [ABSTRACT FROM AUTHOR]

This paper presents genetic algorithms with real-coded binary representation - a novel approach to improve the performance of genetic algorithms. The algorithm is capable of maintaining the diversity of the evolved population during the whole run which protects it from the premature convergence. This is achieved by using a special encoding scheme, introducing a high redundancy, which is further supported by the so-called gene-strength adaptation mechanism for controlling the diversity. The mechanism for the population diversity self-regulation increases the robustness of the algorithm when solving non-stationary problems as was empirically proven on two test cases. The achieved results show the competitiveness of the proposed algorithm with other techniques designed for solving non-stationary problems. [ABSTRACT FROM AUTHOR]

In this paper, an integrated modeling system based on a multi-layer perceptron model is developed and evaluated for the forecasting of urban airborne maximum pollutant concentrations. In the first phase, the multi-objective genetic algorithm (MOGA) and sensitivity analysis are used in combination for identifying feasible system inputs. In the second phase, the final evaluation of the developed system is performed for the concentrations of pollutants measured at an urban air quality station in central Helsinki, Finland. This study showed that the evolutionary design of neural network inputs is an efficient tool, which can help to improve the accuracy of the model. The evaluation work itself showed that the developed modeling system is capable of producing fairly good operational forecasts. [ABSTRACT FROM AUTHOR]

Chapter 3 describes fixed channel assignment schemes in cellular networks, with emphasis on recent heuristic algorithms that apply genetic algorithms, tabu search, neural networks, fuzzy logic and other heuristics in solving problems. Dynamic channel assignment, channel borrowing and hybrid channel assignment problems are also discussed. [ABSTRACT FROM PUBLISHER]

The article discusses evolutionary game theory applied to service selection and network ecologies. In spite of implementation differences genetic algorithms and evolutionary game theory share some very fundamental analogies, based essentially on the principles of a replicator dynamic. These similarities are in fact so fundamental that both models can be viewed as two different dynamic implementations of a general replicator dynamic. Of course this is not surprising as both models have their roots in two biological domains, one considering the molecular basis of evolution whereas the other studies the competition between species in an ecological context.

The article gives information about a chapter of the book "Telecommunications Optimization: Heuristic and Adaptive Techniques," edited by David W. Corne, Martin J. Oates and George D. Smith. The approach taken in this chapter is to implement several of the best known methods in order to objectively compare them on randomly generated instances of the network design problem. Because there is keen interest in designing optimum networks, many approaches have been developed, including branch exchange, cut saturation, genetic algorithms, MENTOR algorithms and simulated annealing. In this chapter, authors will look at the results of several of these methods and perform an in-depth study of several heuristic and genetic techniques.