Simple Genetic Algorithm

In modern-day manufacturing process, flexible manufacturing system (FMS) is used for efficient production of parts. In FMS, processing times are important while preparing the production schedule. The manufacturing cost will vary from part to part depending on the processing time and type of machine used. In this paper a case study is considered in which three machines produce three different parts by doing different operations. Each machine can perform all the different operations to produce all the three parts. All the operations can be done in all the three machines. The production timings and corresponding costs vary from machine to machine. The operations sequences for different parts are different. The objective functions considered are minimization of the total flow time, machine workload balancing, maximum workload on machine and minimization of total tool cost. In the first step, we have considered randomly different sequence of operations on different machines, that is, operation index and machine index, and the objective function values are calculated. In the second step, the values of objective function are calculated if a particular machine is not working for manufacturing the three parts, and hence those operations are processed on alternate machines. The observations after calculations are that the total flow time, machine workload, maximum workload on machine and total cost are found to be different for different sequences and due to machine failure these values increased and better operation index and machine index are identified to meet the objective functions.

This work deals with the application of Artificial Immune Systems (AIS) in distribution systems. Based on natural immune systems, these systems present some important characteristics that may be useful in optimization of power systems operation and planning. In this paper, such a technique is enhanced by tangent vector to help the search solution, so the optimization process is accelerated. The tests are obtained by using the IEEE 34bus distribution system.

The continuous double-auction (CDA) is a powerful market mechanism, noted for its speed and efficiency, and is the mechanism underlying the organization of open-outcry 'trading pits' at major international derivatives markets. In previous publications, Cliff & Bruten demonstrated that software 'trading agents' need more than zero intelligence to give human-like CDA price-equilibration behavior and presented results from experiments with simple adaptive trading agents in CDA markets and in one-sided auctions. These agents give very good performance on standard measures of trading activity such as allocative efficiency, Smith's 'alpha' measure of price convergence, and profit dispersion, but only when parameters governing the adaptation mechanism are set to appropriate values. Determining good or optimal combinations of parameters by hand is possible, but can be labor-intensive. This paper presents the first results from using a genetic algorithm to optimize all the real-valued parameters governing adaptation in the trading agents. It is shown that a simple genetic algorithm (GA), in combination with an appropriate evaluation function, can deliver good parameter settings from random initial-value conditions. The evolutionary trajectories of the population through the 8 -dimensional parameter space are illustrated, and the use of the GA to identify parameters that are redundant or even harmful is discussed.

Reliability is one of the very important characteristics of the distributed computing system (DCS), and articles on task allocation (an NP-Hard problem) to maximize the reliability of DCS have appeared in the past [S. Kartik, C.S. Ram Murthy, IEEE Trans. Comput. 46 (6) (1997) 719; S.M. Shatz, Wang, Goto, IEEE Trans. Comput. 41 (90) (1992) 1156]. Genetic Algorithm (GA) has emerged as a successful tool for optimization purposes. We, in this work, have used a simple GA to optimize the reliability of DCS with task allocation.

Reliability is one of the very important characteristics of the distributed computing system (DCS), and articles on task allocation (an NP-Hard problem) to maximize the reliability of DCS have appeared in the past [S. Kartik, C.S. Ram Murthy, IEEE Trans. Comput. 46 (6) (1997) 719; S.M. Shatz, Wang, Goto, IEEE Trans. Comput. 41 (90) (1992) 1156]. Genetic Algorithm (GA) has emerged as a successful tool for optimization purposes. We, in this work, have used a simple GA to optimize the reliability of DCS with task allocation.

Machine-loading problem of a flexible manufacturing system is known for its complexity. This problem encompasses various types of flexibility aspects pertaining to part selection and operation assignments along with constraints ranging from simple algebraic to potentially very complex conditional constraints. From the literature, it has been seen that simple genetic-algorithm-based heuristics for this problem lead to constraint violations and large number of generations. This paper extends the simple genetic algorithm and proposes a new methodology, constraint-based genetic algorithm (CBGA) to handle a complex variety of variables and constraints in a typical FMS-loading problem. To achieve this aim, three new genetic operators-constraint based: initialization, crossover, and mutation are introduced. The methodology developed here helps avoid getting trapped at local minima. The application of the algorithm is tested on standard data sets and its superiority is demonstrated. The solution approach is illustrated by a simple example and the robustness of the algorithm is tested on five well-known functions.

This paper presents an exhaustive study of the Simple Genetic Algorithm (SGA), Steady State Genetic Algorithm (SSGA) and Replacement Genetic Algorithm (RGA). The performance of each method is analyzed in relation to several operators types of crossover, selection and mutation, as well as in relation to the probabilities of crossover and mutation with and without dynamic change of its values during the optimization process. In addition, the space reduction of the design variables and global elitism are analyzed. All GAs are effective when used with its best operations and values of parameters. For each GA, both sets of best operation types and parameters are found. The dynamic change of crossover and mutation probabilities, the space reduction and the global elitism during the evolution process show that great improvement can be achieved for all GA types. These GAs are applied to TEAM benchmark problem 22.

Inspired by the evolution principle, GA becomes a more powerful optimization technique, although the authors have already implemented them in a rather different way in other algorithms; hence their differences concerning computational efficiency are extremely visible (Goldberg, 1989) [1]. This work aims to evaluate the computational complexity of the three GA variants: the Standard Genetic Algorithm (SGA), the Elitist Genetic Algorithm (EGA), and the Parallel Genetic Algorithm (PGA) (Mitchell, 1998) [2]. It will study computational complexity in terms of time (with respect to speed of convergence), number of function evaluations, and space complexity in terms of memory size, and memory needed to store the population (Deb, 2001) [3]. The experiment results show how time and memory consumption are related in executing it, hence providing insight on selecting the most appropriate type of GA when the resources used by environments are of paramount importance (Eiben & Smith, 2015) [6]. This might be followed by work on hybrid strategies and adaptive approaches that enhance performance further in GA algorithms (Srinivas & Patnaik, 1994) [12].

Machine-loading problem of a flexible manufacturing system is known for its complexity. This problem encompasses various types of flexibility aspects pertaining to part selection and operation assignments along with constraints ranging from simple algebraic to potentially very complex conditional constraints. From the literature, it has been seen that simple genetic-algorithm-based heuristics for this problem lead to constraint violations and large number of generations. This paper extends the simple genetic algorithm and proposes a new methodology, constraint-based genetic algorithm (CBGA) to handle a complex variety of variables and constraints in a typical FMS-loading problem. To achieve this aim, three new genetic operators-constraint based: initialization, crossover, and mutation are introduced. The methodology developed here helps avoid getting trapped at local minima. The application of the algorithm is tested on standard data sets and its superiority is demonstrated. The solution approach is illustrated by a simple example and the robustness of the algorithm is tested on five well-known functions.

The study of the injury caused by vehicle-teenage cyclist crash is presented in this paper. The results of the crash with three vehicles: sedan, SUV and Pick up are compared. Three different positions are analyzed: front, rear and lateral position. The injury on the cyclist head is examined by HIC criterion, in the way indicated by the rules. A comparison is done between the results of the simulations for Pick up, SUV and sedan, concluding that the injury of the head is more dangerous for Pick-up impact than SUV or the sedan, but only at greater speed than 40 km/h. Teenage cyclist is more likely to suffer an injury to the chest in rear impacts with the sedan, because 3 ms values remain above the values obtained with the SUV and Pick up. Unlike Pick up could cause greater injury to the chest in the front and side impact because of greater height from the ground. The vehicle mass has not great importance, but only to low speed. Consideration is made that teenage cyclist has a better c...

In this paper, optimal operating rules for water quality management in reservoir-river systems are developed using a methodology combining a water quality simulation model and a stochastic GA-based conflict resolution technique. As different decision-makers and stakeholders are involved in the water quality management in reservoir-river systems, a new stochastic form of the Nash bargaining theory is used to resolve the existing conflict of interests related to water supply to different demands, allocated water quality and waste load allocation in downstream river. The expected value of the Nash product is considered as the objective function of the model which can incorporate the inherent uncertainty of reservoir inflow. A water quality simulation model is also developed to simulate the thermal stratification cycle in the reservoir, the quality of releases from different outlets as well as the temporal and spatial variation of the pollutants in the downstream river. In this study, a Varying Chromosome Length Genetic Algorithm (VLGA), which has computational advantages comparing to other alternative models, is used. VLGA provides a good initial solution for Simple Genetic Algorithms and comparing to Stochastic Dynamic Programming (SDP) reduces the number of state transitions checked in each stage. The proposed model, which is called Stochastic Varying Chromosome Length Genetic Algorithm with water Quality constraints (SVLGAQ), is applied to the Ghomrud Reservoir-River system in the central part of Iran. The results show, the proposed model for reservoir operation and waste load allocation can reduce the salinity of the allocated water demands as well as the salinity build-up in the reservoir.

In electrical distribution networks, inefficiencies and instabilities often arise from inductive loads like motors and transformers, which exhibit a lagging power factor. This reduces system capacity, increases losses, and can lead to lower voltage levels. To address these issues, integrating parallel capacitors proves effective, enhancing the power factor, improving voltage profiles, and reducing overall system losses and costs. This research explores the optimal placement of parallel capacitors within a distribution network to manage reactive power effectively, thereby minimizing losses and improving voltage stability and system efficiency. Utilizing DigSILENT Power Factory and MATLAB, a genetic algorithm optimizes the location and sizing of capacitors in a 33-bus distribution network, considering scenarios with and without distributed generation (DG) and the impact of harmonic currents. The study finds that incorrect sizing or placement of capacitors can worsen voltage deviations when higher harmonic levels are present. However, the optimization method accurately determines the best parameters for capacitor installation, ensuring compliance with voltage and harmonic constraints. Deploying more than three to four capacitors does not significantly affect outcomes, while a single busbar capacitor often fails to meet operational standards. In conclusion, strategic capacitor placement and sizing can significantly reduce losses, enhance voltage stability, and mitigate inefficiencies caused by inductive loads. Attention to harmonics is crucial to avoid negative impacts on the network. This approach offers a replicable framework for similar optimizations in other distribution systems, advancing smart grid technology implementation.

In this paper a complex scheduling problem in flexible manufacturing system (FMS) has been addressed with a novel approach called knowledge based genetic algorithm (KBGA). The literature review indicates that meta-heuristics may be used for combinatorial decision-making problem in FMS and simple genetic algorithm (SGA) is one of the meta-heuristics that has attracted many researchers. This novel approach combines KB (which uses the power of tacit and implicit expert knowledge) and inherent quality of SGA for searching the optima simultaneously. In this novel approach, the knowledge has been used on four different stages of SGA: initialization, selection, crossover, and mutation. Two objective functions known as throughput and mean flow time, have been taken to measure the performance of the FMS. The usefulness of the algorithm has been measured on the basis of number of generations used for achieving better results than SGA. To show the efficacy of the proposed algorithm, a numerical example of scheduling data set has been tested. The KBGA was also tested on 10 different moderate size of data set to show its robustness for large sized problems involving flexibility (that offers multiple options) in FMS.

A new model for automatic generation of Evolutionary Algorithms (EAs) by evolutionary means is proposed in this paper. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several wellknown benchmarking problems.

A new model for automatic generation of Evolutionary Algorithms (EAs) by evolutionary means is proposed in this paper. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several wellknown benchmarking problems.

Automated discovery of rules is, due to its applicability, one of the most fundamental and important method in Knowledge Discovery in Databases(KDD). It has been an active research area in the recent past. This paper presents a classification algorithm based on Evolutionary Approach(EA) that discovers interesting classification rules in the form If P Then D. A flexible encoding scheme, genetic operators and a suitable fitness function to measure the goodness of rules are proposed for effective evolution of rule sets. The proposed algorithm is validated on several datasets of UCI data set repository and the experimental results are presented to demonstrate the effectiveness of the proposed scheme for automated rule mining.

The main purpose of this study is to present a new method of evaluation of the heterogeneity of microporous carbons (called the LAPLACE method). The simple genetic algorithm (SGA) is modified and applied to the study of the LAPLACE method. The applicability of this procedure as well as the CONTIN package is checked based on new adsorption isotherm equations derived for two model pore size distributions (trapezoidal and bitriangular). The obtained results show that the proposed procedure and algorithm can be successfully used for the evaluation of the heterogeneity of microporous carbons. This procedure, as well as other (namely the Jaroniec-Choma equation, DFT theory, Nguyen-Do, and Horvath-Kawazoe) methods, together with the distributions of adsorption potential and adsorption energy, is applied to the characterization of series of commercially available carbons.

Conservation of functionally identical copies of the same gene throughout the generations is not an easy task. In this study, based on the biological evidence that suggests the existence of the developmental error as one of the ways to preserve redundancy, our goal is to investigate the impact of developmental error on a simple problem using a genetic algorithm(GA). Developmental errors exist during the biological development of an individual. The biological models with developmental errors have demonstrated that it is possible to maintain redundant copies in the existence of proper mutation rates and developmental error rates. Our preliminary results with a simple problem demonstrates that using developmental error helps preserving the redundant copies and maintaining a better solution quality for the redundant copies of a gene. Besides the developmental error, we propose a new error type that comes into play during the fitness assignment and enhances the quality of the solutions when used together with developmental error.

This paper proposes a new musical notation with Lindenmayer grammars, and describes the use of grammar evolution for the automatic generation of music expressed in this notation, with the normalized compression distance as the fitness function. The computer music generated tries to reproduce the style of a selected pre-existent piece of music. In spite of the simplicity of the algorithm, the procedure obtains interesting results.

In this paper, two approaches based on steepest descent method for solving unconstrained or bound constrained optimization problems having continuously differentiable objective functions have been proposed. In the first approach, an efficient heuristic method, viz. Genetic Algorithm is applied for finding the step length in each iteration of steepest descent method. Then, this idea has been extended to a set of multi-point approximations instead of single point approximation to avoid the convergence of the existing method at local optimum point of multi-modal objective functions and a new method (we call it as population based steepest descent method) has been proposed to find the global or close-toglobal optima. Finally, to demonstrate the performance of both the proposed methods, several multi-dimensional standard test functions available in the literature have been solved. The results have been compared with the same of recently developed two hybrid algorithms with respect to different comparative factors like; functions evaluations, number of iterations, CPU time (computational time) and frequency of occurrence of close-toglobal solution.

Genetic Algorithm is an important optimization technique, though its application in Fuzzy system is usually limited by problems like local optimal and premature convergence. With an aim to improve the performance of simple Genetic Algorithm, we propose a multi-population genetic algorithm MP-GA which uses two populations collaborating with each other, and apply it to fuzzy controller design to optimize its control rules. The simulation results of Inverted Pendulum demonstrate the effectiveness of this proposed method.

Machine-loading problem of a flexible manufacturing system is known for its complexity. This problem encompasses various types of flexibility aspects pertaining to part selection and operation assignments along with constraints ranging from simple algebraic to potentially very complex conditional constraints. From the literature, it has been seen that simple genetic-algorithm-based heuristics for this problem lead to constraint violations and large number of generations. This paper extends the simple genetic algorithm and proposes a new methodology, constraint-based genetic algorithm (CBGA) to handle a complex variety of variables and constraints in a typical FMS-loading problem. To achieve this aim, three new genetic operators-constraint based: initialization, crossover, and mutation are introduced. The methodology developed here helps avoid getting trapped at local minima. The application of the algorithm is tested on standard data sets and its superiority is demonstrated. The solution approach is illustrated by a simple example and the robustness of the algorithm is tested on five well-known functions.

Machine-loading problem of a flexible manufacturing system is known for its complexity. This problem encompasses various types of flexibility aspects pertaining to part selection and operation assignments along with constraints ranging from simple algebraic to potentially very complex conditional constraints. From the literature, it has been seen that simple genetic-algorithm-based heuristics for this problem lead to constraint violations and large number of generations. This paper extends the simple genetic algorithm and proposes a new methodology, constraint-based genetic algorithm (CBGA) to handle a complex variety of variables and constraints in a typical FMS-loading problem. To achieve this aim, three new genetic operators-constraint based: initialization, crossover, and mutation are introduced. The methodology developed here helps avoid getting trapped at local minima. The application of the algorithm is tested on standard data sets and its superiority is demonstrated. The solution approach is illustrated by a simple example and the robustness of the algorithm is tested on five well-known functions.

This work deals with the application of Artificial Immune Systems (AIS) in distribution systems. Based on natural immune systems, these systems present some important characteristics that may be useful in optimization of power systems operation and planning. In this paper, such a technique is enhanced by tangent vector to help the search solution, so the optimization process is accelerated. The tests are obtained by using the IEEE 34bus distribution system.

Learning morphemes from any plain text is an emerging research area in the natural language processing. Knowledge about the process of word formation is helpful in devising automatic segmentation of words into their constituent morphemes. This thesis applies unsupervised morpheme induction method, based on the statistical behavior of words, to induce morphemes for word segmentation. The morpheme cache for the purpose is based on the Dirichlet Process (DP) and stores frequency information of the induced morphemes and their occurrences in a Zipfian distribution. This thesis uses a number of empirical, morpheme-level grammar models to classify the induced morphemes under the labels prefix, stem and suffix. These grammar models capture the different structural relationships among the morphemes. Furthermore, the morphemic categorization reduces the problems of over segmentation. The output of the strategy demonstrates a significant improvement on the baseline system. Finally, the thesis measures the performance of the unsupervised morphology learning system for Nepali.

The aim of this work is to study the influence of head boundary conditions during real-world pedestrian head trauma simulation. Both Multi-Body System (MBS) and Finite Element (FE) models are used for pedestrian-versus-vehicle accident reconstructions. Four head boundary conditions are studied (from an isolated head to a head attached to the whole body). The effect of each configuration on head mechanical response parameters is computed. The responses include rigid-body kinematics responses as well as the intra-cerebral responses of a head FE model. It appears that head boundary conditions at neck level are significant, especially in terms of intra-cerebral response.

Species distribution models (SDMs) have received increasing attention in freshwater management to support decision making. Existing SDMs are mainly data-driven and often developed with statistical and machine learning methods but with little consideration of hypothetic ecological knowledge. Conceptual SDMs exist, but lack in performance, making them less interesting for decision management. Therefore, there is a need for model identification tools that search for alternative model formulations. This paper presents a methodology, illustrated with the example of river pollution in Ecuador, using a simple genetic algorithm (SGA) to identify well performing SDMs by means of an input variable selection (IVS). An analysis for 14 macroinvertebrate taxa shows that the SGA is able to identify well performing SDMs. It is observed that uncertainty on the model structure is relatively large. The developed tool can aid model developers and decision makers to obtain insights in driving factors shaping the species assemblage.

The aim of this research is to develop decision support tools for identifying optimal location for groundwater development to meet the future demands in the Teeb Area. Teeb Area is located in north and north east of Missan Province, south of Iraq. This area is about 1860 km 2. A twodimensional mathematical model is developed to simulate the flow regime of the upper part of Quaternary Deposits. The suggested conceptual model, which is advocated to simulate the flow regime of aquifer is fixed for one layer, i.e. the activity of the deeper aquifer is negligible. The model is calibrated using trial and error procedure in two stages, steady state followed by unsteady state. This model is integrated with an optimization model which is based on the genetic algorithm (GA). Three management cases were undertaken by running the model with adopted calibrated parameters. In the first case found the optimum value of the objective function is (0.32947E+08 m 3 /year), in other words, the pumping rates could be raised to nine times the current pumping rates, with a highest decline in the hydraulic heads of groundwater compared with initial hydraulic heads reached to 6 cm. In a second case twenty six wells out of thirty five can be operated with "on/off" status associated with each well to obtain the maximum value of pumping rate. In third case is allowed to move a location of well anywhere within a user defined region of the model grid until the optimal location is reached. The optimum value of objective function in third case is (0.35539E+08 m 3 /year) with 8% increasing of the pumping rates compared with the first case.

A practical dynamical model of an efficient Simple Genetic Algorithm is presented, introducing in the matrix of the Nix and Vose Markov model a practical postulate related to the schema theorem, that induces deterministic correction factors in the matrix, through Heaviside´s unitary step function. This alteration permits SGA to evolve by efficient deterministic channels. The model simulates the real behaviour of an efficient SGA. The Markov chain is transformed into an absorbing Markov chain. Using the absorbing theory, the expected waiting time, EWT, is computed easily in any situation. For the case of maximum uncertainty, it is obtained an expression for EWT that improves from the standard Nix and Vose model, in relation to the experimental data. Through the deterministic paths, the steady state is obtained when the absorbing state, global optimum, is reached. To emphasize that, with this practical improvement, the theoretical unification of the model of Nix and Vose for the SGA w...

This paper presents a hybrid metaheuristic algorithm to solve the hybrid flow shop scheduling problem (HFSP) with family setup times. Many conditions of HFSP have been extensively studied in recently years and metaheuristics and local search algorithms have also been developed to yield better solutions for multi-objective HFSP. HFSP in this work is based on a harddisk drive manufacturer. An effective NSGA-II integrated with migrating birds optimization (MBO) called MBNSGA-II is proposed to improve the quality of solutions for bi-objective HFSP. Makespan and total tardiness time are the objectives of this HFSP. MBO is added to mutation operation of genetic algorithm to improve the Pareto front. Next, various sizes of benchmark problem are utilized to evaluate the performance of NSGA-II and MBNSGA-II. The comparisons of two algorithms consisting of NSGA-II and MBNSGA-II are provided by using the numerical examples. It is obvious the Pareto fronts obtained from MBNSGA-II are adjacent to the approximated true Pareto front. In terms of inverted generational distance (IGD) which is the index of convergence and diversity of the solution set, the performance of proposed MBNSGA-II outperforms NSGA-II.

In most developed countries, the statistical data of road traffic accidents involving motor vehicle-pedestrian crashes have registered much cause for concern and consequently, a concerted effort by various sectors have for the past two decades been brought to bear towards mitigation efforts. Amongst the different approaches in this direction, it has been established that pedestrian kinematics during impact plays an important role in the ensuing injuries particularly to the head, and has been shown to have a direct bearing with the vehicle front-end shape. This has eventually led to some optimization efforts of the vehicle front-end geometry but due to the complex nature of the problem, many difficulties have been encountered and an exhaustive comprehensive solution has yet to be achieved. In a step towards an attempt in addressing some of these issues, this paper demonstrates the feasibility of an alternative method for developing an optimization friendly deformable vehicle structure, having simple, easily modifiable profile geometry requiring short processing time for the particular purpose of performing multi-parametric optimization of the vehicle front end shape with the goal of minimizing the sustained head injuries of the pedestrian. The proposed hybrid case model has successfully achieved an efficiency of 99.85% in CPU time in comparison to a full finite element model. The polynomial response surface method is employed to generate the mathematical models which in turn are used for the optimization process. The mathematical models developed are found to show acceptable predictive capabilities with the child model having the highest model fitness of 90.7%. The optimization is successfully able to find a front-end geometry which minimizes the HIC values for both the adult 50 th percentile male pedestrian and the 6 year old child pedestrian. Finally, the study further reinforces the necessity for the consideration of the relationship that exists between pedestrian kinematics and vehicle front-end profile in design considerations as well as in development of further test procedures and safety mitigation methods.

In this paper, optimal operating rules for water quality management in reservoir-river systems are developed using a methodology combining a water quality simulation model and a stochastic GA-based conflict resolution technique. As different decision-makers and stakeholders are involved in the water quality management in reservoir-river systems, a new stochastic form of the Nash bargaining theory is used to resolve the existing conflict of interests related to water supply to different demands, allocated water quality and waste load allocation in downstream river. The expected value of the Nash product is considered as the objective function of the model which can incorporate the inherent uncertainty of reservoir inflow. A water quality simulation model is also developed to simulate the thermal stratification cycle in the reservoir, the quality of releases from different outlets as well as the temporal and spatial variation of the pollutants in the downstream river. In this study, a Varying Chromosome Length Genetic Algorithm (VLGA), which has computational advantages comparing to other alternative models, is used. VLGA provides a good initial solution for Simple Genetic Algorithms and comparing to Stochastic Dynamic Programming (SDP) reduces the number of state transitions checked in each stage. The proposed model, which is called Stochastic Varying Chromosome Length Genetic Algorithm with water Quality constraints (SVLGAQ), is applied to the Ghomrud Reservoir-River system in the central part of Iran. The results show, the proposed model for reservoir operation and waste load allocation can reduce the salinity of the allocated water demands as well as the salinity build-up in the reservoir.

In this paper an approach to invert measured data in electromagnetic imaging based on inverse scattering is numerically evaluated. In particular, an Inexact Newton approach is adopted and the semiconvergence behavior of the proposed iterative method, due to the ill-posedness of the considered inverse problem, is assessed: Indeed the iteratates of the algorithm first converge towards the exact solution up to a certain iteration number and, after that, the noise starts to dominate the restoration, and the subsequent iterations deviate from the exact one. Basic theoretical results concerning this semiconvergence phenomenon are briefly recalled, whereas a numerical assessment is performed concerning the application of the method to the image reconstruction of dielectric targets by using interrogating waves in a multiview arrangement.

Traditionally in Genetic Algorithms, the mutation probability parameter maintains a constant value during the search. However, an important difficulty is to determine a priori which probability value is the best suited for a given problem. In this paper we compare three different adaptive algorithms that include strategies to modify the mutation probability without external control. One adaptive strategy uses the genetic diversity present in the population to update the mutation probability. Other strategy is based on the ideas of reinforcement learning and the last one varies the probabilities of mutation depending on the fitness values of the solution. All these strategies eliminate a very expensive computational phase related to the pre-tuning of the algorithmic parameters. The empirical comparisons show that if the genetic algorithm uses the genetic diversity, as the strategy for adapting the mutation probability outperforms the other two strategies.

This paper is concerned with minimization of mean tardiness and flow time in a real single machine production scheduling problem. Two variants of genetic algorithm as metaheuristic are combined with hyper-heuristic approach are proposed to solve this problem. These methods are used to solve instances generated with real world data from a company. Encouraging results are reported.

In this study, the capability of recently introduced crow search algorithm (CSA) was evaluated for structural optimization problems. It is observed that the standard CSA was led to undesirable performance for solving structural optimization problems. Accordingly, three modifications were made on the standard CSA to obtain the enhanced crow search algorithm (ECSA) while no parameter was added to the ECSA. First, each violated decision variable was replaced by corresponding decision variable of the global best solution. Second, a free-fly mechanism was suggested for constraint handling. Third, the personal upper bound strategy (PUBS) was proposed for elimination of inessential structural analyses. To assess the efficacy of the proposed modifications, four popular benchmark structures were employed and each modification was added to the CSA in a separate stage and then its effects were illustrated. The results of benchmark structures were examined in terms of minimum weight, convergence rate, and reliability. The results confirmed that the ECSA was significantly better than the standard CSA. Moreover, the ECSA obtained better or very competitive results in comparison with well-known and other newly developed metaheuristic methods.

In their classic form, reducts as well as typical testors are minimal subsets of attributes that retain the discernibility condition. Constructs are a special type of reducts and represent a kind of generalization of the reduct concept. A construct reliably provides sufficient amount of discrimination between objects belonging to different classes as well as sufficient amount of resemblance between objects belonging to the same class. Based on the relation between constructs, reducts and typical testors this paper focuses on a practical use of this relation. Specifically, we propose a method that allows applying typical testor algorithms for computing constructs. The proposed method involves modifying the classic definition of pairwise object comparison matrix adapting it to the requirements of certain algorithms originally designed to compute typical testors. The usefulness of our method is shown through some examples.

Typical testors are a useful tool for feature selection and for determining feature relevance in supervised classification problems, especially when quantitative and qualitative features are mixed. Nowadays, computing all typical testors is a highly costly procedure; all described algorithms have exponential complexity. Existing algorithms are not acceptable methods owing to several problems (particularly run time) which are dependent on matrix size. Because of this, different approaches, such as sequential algorithms, parallel processing, genetic algorithms, heuristics and others have been developed. This paper describes a novel external type algorithm that improves the run time of all other reported algorithms. We analyze the behaviour of the algorithm in some experiments, whose results are presented here.

This paper summarizes recent research on competition-based learning procedures performed by the Navy Center for Applied Research in Artificial Intelligence at the Naval Research Laboratory. We have focused on a particularly interesting class of competition-based techniques called genetic algorithms. Genetic algorithms are adaptive search algorithms based on principles derived from the mechanisms of biological evolution. Recent results on the analysis of the implicit parallelism of alternative selection algorithms are summarized, along with an analysis of alternative crossover operators. Applications of these results in practical learning systems for sequential decision problems and for concept classification are also presented.

Classification Rule Mining (CRM) is a data mining technique for discovering important classification rules from large dataset. This work presents an efficient genetic algorithm for discovering significant IF-THEN rules from a given dataset. The proposed algorithm consists of two main steps. First step generates set of classification rules and the second step deletes the weak rules and selects only the significant rules. Since weak rules are deleted and significant rules are selected, the proposed algorithm can be considered as knowledge acquisition tool for classification problems. Experimental results are presented to demonstrate the contribution of the proposed algorithm for discovering the significant rules.

The purpose of this paper and the experiments condu cte herein is to assess the impact of variation in particle inertia (w), particle increment ( c1) and global increment ( c2), on the fitness of particles and there impact upo n particle swarm optimization in a dynamic environmen t. I this paper a 3-dimensional parabolic function is utilized to illustrate an example of a dynamic environment. Results show that the impact of PSO in this environm e t will not be significantly enhanced by the alteration of w, c1 or c2.

It would be difficult to efficiently implement a manufacturing system without solving its design and operational problems. Based on this framework, a system configuration and tooling problem is modeled. The model turns out to be a large mixed integer linear program, so that some alternative optimal seeking and heuristic techniques are used to solve the model for constructing a flow line structured Flexible Manufacturing System. As a result, it may be possible to construct flexible, efficient, simple and easily controllable manufacturing systems.

Collapsible soils are almost found in unsaturated states and involved significant engineering problems. Geotechnical challenges of such soils are represented by the hydro-mechanical behaviour during wetting–drying cycles due to the humidity and climate conditions. The main objective of this paper is to investigate the soil–water characteristic curve (SWCC) of unsaturated collapsible soils. In this study, three types of collapsible soils were investigated such as natural soils of sandy gypseous, silty loess, and artificial soil of gypsum–sand mixture. Determination of soil–water characteristic curve represented by wetting and drying paths has been done using a combination of the axis-translation technique (i.e. pressure plate device) and vapour equilibrium technique (i.e. salts solution desiccators) to cover a wide range of applied suction. The test results show that the air-entry value for all soils occurs at a very low suction range. At the boundary effect zone, the coarse grain si...

During impact with an automobile, a pedestrian suffers multiple impacts with the bumper, hood and the windscreen. Optimisation of the car front using a scalar injury cost function has been demonstrated. The results for impacts simulated in MADYMO show good co-relation with Euro-NCAP ratings for existing vehicles. Optimization of the car front to minimise the injury cost converges to vehicle profiles with features known from earlier studies to be pedestrian friendly. A method to design car fronts for pedestrian safety is evolved.

Machine-loading problem of a flexible manufacturing system is known for its complexity. This problem encompasses various types of flexibility aspects pertaining to part selection and operation assignments along with constraints ranging from simple algebraic to potentially very complex conditional constraints. From the literature, it has been seen that simple genetic-algorithm-based heuristics for this problem lead to constraint violations and large number of generations. This paper extends the simple genetic algorithm and proposes a new methodology, constraint-based genetic algorithm (CBGA) to handle a complex variety of variables and constraints in a typical FMS-loading problem. To achieve this aim, three new genetic operators-constraint based: initialization, crossover, and mutation are introduced. The methodology developed here helps avoid getting trapped at local minima. The application of the algorithm is tested on standard data sets and its superiority is demonstrated. The solution approach is illustrated by a simple example and the robustness of the algorithm is tested on five well-known functions.