Genetic Algorithm

No new technology appears in a society without causing changes to that society. Artists instinctively respond to these changes and the potential meanings of the technologies that bring them about. In this way, artists' interest in technology goes beyond the simple desire to possess new tools for artistic expression. Artists serve to prepare society for the invisible changes going on within it by producing artworks in response to the mechanisms of change. This paper discusses the authors' approaches to using concepts from artificial life in their musical works, which are basically of two kinds -artificial worlds producing music as an output, and interactive compositional tools using evolutionary algorithms to generate music and sound. It also provides a brief cultural context for these works.

This paper presents a comparison study for the optimization of stencil printing operations using hybrid intelligence technique and response surface methodology (RSM). An average 60% of soldering defects are attributed to solder paste stencil printing process in surface mount assembly (SMA). The manufacturing costs decrease with increasing first-pass yield in the stencil printing process. This study compares two hybrid intelligence approaches with RSM as methods of solving the stencil printing optimization problem that involves multiple performance characteristics. The optimization process is threefold. A data set obtained from an experimental design following data preprocessing process provides an accurate data source for RSM study and training neural networks to formulate the nonlinear model of the stencil printing process with/without combining multiple performance characteristics into a single desirability value, followed by a genetic algorithm searching the trained neural networks for obtaining the optimal parameter sets. The empirical defect-per-millionopportunities (DPMO) measurements demonstrate that the two hybrid intelligence methods can provide satisfactory performance for stencil printing optimization problem.

This paper presents two hybrid genetic algorithms (HGAs) to optimize the component placement operation for the collect-and-place machines in printed circuit board (PCB) assembly. The component placement problem is to optimize (i) the assignment of components to a movable revolver head or assembly tour, (ii) the sequence of component placements on a stationary PCB in each tour, and (iii) the arrangement of component types to stationary feeders simultaneously. The objective of the problem is to minimize the total traveling time spent by the revolver head for assembling all components on the PCB. The major difference between the HGAs is that the initial solutions are generated randomly in HGA1. The Clarke and Wright saving method, the nearest neighbor heuristic, and the neighborhood frequency heuristic are incorporated into HGA2 for the initialization procedure. A computational study is carried out to compare the algorithms with different population sizes. It is proved that the performance of HGA2 is superior to HGA1 in terms of the total assembly time.

This paper deals with the PCB assembly problem. We propose an efficient genetic algorithm to solve the problem. The algorithm uses well-known crossover and mutation operators adapted to the specific representation used. Computational experiments show that our algorithm improves the state-of-the-art result on a benchmark for the problem. Furthermore, we propose a set of benchmarks for this problem and apply our algorithm to solve each instance on this set. We compare the generated results with those produced by a method widely used in the industry and, for each instance, we obtain the best result.

Many multivariate techniques have been applied to diagnose faults such as Principal Component Analysis (PCA), Fisher's Discriminant Analysis (FDA), and Discriminant Partial Least Squares (DPLS). However, it has been shown that FDA and DPLS are more proficient than PCA for diagnosing faults. And recently applying kernel on FDA which is called KFDA (Kernel FDA) has showed outperformance than linear FDA based method. We propose in this research work an advanced KFDA for faults classification with Building knowledge base for faults structure using FSN. A case study is done on a chemical G-Plant process, constructed and experimental runs are done in Okayama University, Japan. The results are showing improving performance of fault detection rate for the new model over FDA.

DNA computing is a new computing paradigm which uses bio-molecular as information storage media and biochemical tools as information processing operators. It has shows many successful and promising results for various applications. Since DNA reactions are probabilistic reactions, it can cause the different results for the same situations, which can be regarded as errors in the computation. To overcome the drawbacks, much works have focused to design the error-minimized DNA sequences to improve the reliability of DNA computing. In this research, Population-based ACO (P-ACO) is proposed to solve the DNA sequence optimization. P-ACO approach is a meta-heuristic algorithm that uses some ants to obtain the solutions based on the pheromone in their colony. The DNA sequence design problem is modelled by four nodes, representing four DNA bases (A, T, C, and G). The results from the proposed algorithm are compared with other sequence design methods, which are Genetic Algorithm (GA), and Mult...

Network analysis in geospatial information system (GIS) provides strong decision support for users in searching optimal route, finding the nearest facility and determining the service area. Searching optimal path is an important advanced analysis function in GIS. In present GIS route finding modules, heuristic algorithms have been used to carry out its search strategy. Due to the lack of global sampling in the feasible solution space, these algorithms have considerable possibility of being trapped into local optima. This paper addresses the problem of selecting route to a given destination on an actual map under a static environment. The proposed solution uses a genetic algorithm (GA). A part of an arterial road is regarded as a virus. We generate a population of viruses in addition to a population of routes. A customized method based on a genetic algorithm has been proposed and successfully implemented in an area in the northeast of Tehran using the optimal combination of viruses.

Page 1. A Parallel Genetic Algorithm for Rule Discovery in Large Databases Dieferson Luis Alves de Araujo' , Heitor S. Lopes', Alex A. Freitas2 CEFET-PR - Centro Federal de EducaGBo Tecnol6gica do Paranh CPGEI - Curso ...

Testing is a key issue in the design and production of digital circuits and the adoption of Built-In Self Test techniques is increasingly popular. This paper shows an application in the field of Electronic CAD of the Selfish Gene algorithm, an evolutionary algorithm based on a recent interpretation of the Darwinian theory. A three-phase optimization algorithm is exploited for determining the structure of a Built-In Self Test architecture that is able to achieve good fault coverage results with a reduced area overhead.

Radiographic inspection is a reliable non-destructive test, widely used for integrity evaluation of structures and equipments. Nowadays, high quality images with very accurate resolutions have been supported by modern digital radiographic systems. However, the image analysis for internal defect detection and geometric characterization is still a not totally automated task. The main reason is that image analysis is usually a very complex task, which involves heuristic decisions based on experiences, as object detection and recognition. For that reason, a new automatic radiographic image analysis system was developed in order to identify important components or component parts, which must be inspected separately, as weld joints, pipe walls, pipe wall thicknesses, valves and mechanical parts. The developed methodology involves the use of a genetic algorithm search to find desirable patterns on the image. Image indexing procedures are used for a final verification process. As a result, the system offers quick and correct answers and also flexibility to be applied in others applications.

To exploit a heterogeneous computing (HC) environment, an application task may be decomposed into subtasks that have data dependencies. Subtask matching and scheduling consists of assigning subtasks to machines, ordering subtask execution for each machine, and ordering intermachine data transfers. The goal is to achieve the minimal completion time for the task. A heuristic approach based on a genetic algorithm is developed to do matching and scheduling in HC environments. It is assumed that the matcher/scheduler is in control of a dedicated HC suite of machines. The characteristics of this genetic-algorithm-based approach include: separation of the matching and the scheduling representations, independence of the chromosome structure from the details of the communication subsystem, and consideration of overlap among all computations and communications that obey subtask precedence constraints. It is applicable to the static scheduling of production jobs and can be readily used to collectively schedule a set of tasks that are decomposed into subtasks. Some parameters and the selection scheme of the genetic algorithm were chosen experimentally to achieve the best performance. Extensive simulation tests were conducted. For small-sized problems (e.g., a small number of subtasks and a small number of machines), exhaustive searches were used to verify that this genetic-algorithm-based approach found the optimal solutions. Simulation results for larger-sized problems showed that this genetic-algorithm-based approach outperformed two nonevolutionary heuristics and a random search.

This work presents system identification using neural network approaches for modelling a laboratory based twin rotor multi-input multi-output system (TRMS). Here we focus on a memetic algorithm based approach for training the multilayer perceptron neural network (NN) applied to nonlinear system identification. In the proposed system identification scheme, we have exploited three global search methods namely genetic algorithm (GA), Particle Swarm Optimization (PSO) and differential evolution (DE) which have been hybridized with the gradient descent method i.e. the back propagation (BP) algorithm to overcome the slow convergence of the evolving neural networks (EANN). The local search BP algorithm is used as an operator for GA, PSO and DE. These algorithms have been tested on a laboratory based TRMS for nonlinear system identification to prove their efficacy.

In this paper a method based on combining adaptive neuro fuzzy inference systems (ANFISs) and genetic algorithm (GA) is applied for design and optimization of a circularly polarized microstrip antenna for L1 frequency band of GPS. In design process, trained ANFISs are used for estimating return loss and axial ratio. In optimization process, a proper objective function is defined and minimized with GA in order to obtain optimum physical parameters. The optimization method is much faster than conventional optimization methods. Both simulation and measurement results confirm the accuracy and efficiency of the method.

An integrated approach of the radial basis function neural network (RBFNN) and Takagi-Sugeno (TS) fuzzy scheme with genetic optimization of their parameters has been developed in this paper to design intelligent adaptive controllers for improving the transient stability performance of power system. At the outset this concept is applied to a simple device such as the thyristor controlled series capacitor (TCSC) connected in a single-machine infinite bus power system and is then extended to the interline power flow controller (IPFC) connected in a multimachine power system. The RBFNN uses single neuron architecture and its parameters are dynamically updated in an online fashion with the TS-fuzzy scheme designed with only four rules and triangular membership function. The rules of the TS-fuzzy scheme are derived from the real or reactive power error and their derivatives either at the TCSC or IPFC buses depending on the device. Further, to implement this combined scheme only one coefficient in the TS-fuzzy rules needs to be optimized. The optimization of this coefficient, as well as the coefficient for auxiliary signal generation, is performed through genetic algorithm. The performance of the new controller is evaluated both in single-machine and multimachine power systems subjected to various transient disturbances. The new genetic-neuro-fuzzy control scheme exhibits a superior damping performance as well as a greater critical clearing time in comparison to the existing PI as well as RBFNN controller with updating of its parameters through the extended Kalman filter (EKF). Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation.

INTRODUCTION Epilepsy is the fourth most common neurological problem affecting human population after migrane, stroke and Alzheimer's disease (Epilepsy foundation). Epilepsy belongs to a group of neurological disorders showing neuronal hyperexcitability characterized by recurrent seizures in different parts of the brain [1]. According to WHO, it is estimated that the condition affects approximately 50 million people worldwide, 80 % of which resides in developing countries. Enormous AEDs belonging to different classes are used nowadays to treat epilepsy including phenytoin, carbamazepine, valproic acid, topiramate, gabapentin, felbamate, levetiracetam, etc. [2]. However, the current therapy using the presently available antiepileptic drugs shows limitations viz. insufficient, seizure control, unpredictability of effect and its loss, poor efficacy, inadequate information about the receptors involved, etc. Moreover AEDs do not prevent epilepsy in persons at risk of drug-resistance. Further, long term therapy with these drugs pose an array of side effects which includes but is not limited to drowsiness, ataxia, gastrointestinal disturbances, megaloblasticanaemia and hirsutism [3,4]. Hence there exists the continuous need for the discovery and development of new antiepileptic agents with higher activity, minimal or negligible toxicity and side effects. A number of heterocyclic derivatives possessing nitrogen has been reported to play an important role in pharmaceutical Design, Synthesis and Anticonvulsant Evaluation of N-[(Substituted 1H-pyrazol-3-yl)amino]-2-(4-methylphenyl)quinazolin-4(3H)-one Derivatives

In this study, an alternative method has been proposed for the parameter estimation in non-linear regression. This method is the genetic algorithms technique which is widely used in recent years. Unlike other parameter estimation methods, genetic algorithms do not require supplementary information and thus are functional for practical purposes. Genetic algorithms are shown to achieve effective solutions compared to other methods in the S-type growth models. The results have established that genetic algorithms with no need for auxiliary information can be used for parameter estimation in non-linear regression.

The Quadratic Assignment Problem (QAP) is one of the classical combinatorial optimization problems and is known for its diverse applications. In this paper, we suggest a genetic algorithm for the QAP and report its computational behavior. The genetic algorithm incorporates many greedy principles in its design and, hence, we refer to it as a greedy genetic algorithm. The ideas we incorporate in the greedy genetic algorithm include (i) generating the initial population using a randomized construction heuristic; (ii) new crossover schemes; (iii) a special purpose immigration scheme that promotes diversity; (iv) periodic local optimization of a subset of the population; (v) tournamenting among different populations; and (vi) an overall design that attempts to strike a balance between diversity and a bias towards fitter individuals. We test our algorithm on all the benchmark instances of QAPLIB, a well-known library of QAP instances. Out of the 132 total instances in QAPLIB of varied sizes, the greedy genetic algorithm obtained the best known solution for 103 instances, and for the remaining instances (except one) found solutions within 1 % of the best known solutions.

An evolutive algorithm for the optimal design of wind farms is presented. The algorithm objective is to optimize the profits given an investment on a wind farm. Net present value will be used as a figure of the revenue. To work out this figure, several economic factors such as the initial capital investment, the discount rate, the value of the generated energy and the number of the years spanned by the investment are considered. All this factors depends on the preliminary design of the wind park (number, type, tower height and layout situation of wind generators), which are the variables to set. r

This paper describes our current research with RAGA (Rule Acquisition with a Genetic Algorithm). RAGA is a genetic algorithm and genetic programming hybrid that is designed for the tasks of supervised and certain types of unsupervised data mining. Since its initial release we have improved its predictive accuracy and data coverage, as well as its ability to generate more scalable rule hierarchies. These enhancements and several experiments are described.

The increasing complexity of circuit design needs to be managed with appropriate optimization algorithms and accurate statistical description of design models in order to reach the design specifics, guaranteeing ''zero defects''. In the Design for Yield open problems are the design of effective optimization algorithms and statistical analysis for yield design, which require time consuming techniques. New methods have to balance accuracy, robustness and computational effort. Typical analog integrated circuit optimization problems are computationally hard and require the handling of multiple, conflicting, and non-commensurate objectives having strong nonlinear interdependence. This paper tackles the problem by evolutionary algorithms to produce tradeoff solutions on the Pareto Front. In this research work Integrated Circuit (IC) design has been formulated as a constrained multi-objective optimization problem defined in a mixed integer/discrete/continuous domain. The following real-life circuits, RF Low Noise Amplifier, Leapfrog Filter, and Ultra Wideband LNA, were selected as test bed. The proposed algorithm, A-NSGAII, was shown to produce acceptable and robust solutions in the tested applications, where state-of-art algorithms and circuit designers failed. The results show significant improvement in all the chosen IC design problems.

previous sections, are: scalability with respect to the number of nodes in the network, self-organization, self-healing, energy efficiency, a sufficient degree of connectivity among nodes, low-complexity, low cost and size of nodes. Those protocol architectures and technical solutions providing such features can be considered as a potential framework for the creation of these networks, but, unfortunately, the definition of such a protocol architecture and technical solution is not simple, and the research still needs to work on it.

Organising shifts, or work rosters, is a problem that affects a large number of businesses where staff are subject to some kind of work rotation or other. Researchers in the fields of Operations Research and Artificial Intelligence have devised several systems in an attempt to optimise the problem. This paper focuses on the problem of medial staff shift rotation at a hospital emergency department, and attempts to establish a method to automate its management by applying genetic algorithms. It also analyses one of the duty rosters that came out of the proposed solution.

The position control study of DC servo motors is very important since they are extensively deployed in various servomechanisms. Normally PID controllers are used to improve the transient response of DC servo motors. At present, most tuning methods are designed to provide workable initial values, which are then further manually optimized for a specific requirement. This paper presents a flexible and fast tuning method based on genetic algorithm (GA) to determine the optimal parameters of the PID controller for the desired system specifications. Simulation results show that a wide range of requirements are satisfied with the proposed tuning method. Keywords— DC servomotor, Genetic Algorithm, Optimization, PID controller, PID Tuning, Transient response.

Whenever we have a set of discrete measures of a phenomenon and try to find an analytic function which models such phenomenon, we are solving a problem about finding some parameters that minimizes a computable error function. In this way, parameter estimation may be studied as an optimization problem, in which the fitness function we are trying to minimize is the error one. This work try to do that using a genetic algorithm to obtain three parameters of a function. Particularly, we use data about one village population over time to see the goodness of our algorithm.

The active aeroelastic control aims the reduction or elimination of harmful fluidstructure interaction effects. More recently, piezo-fiber composites, made from piezoelectric fibers embedded into composites, represent a major technological breakthrough for the manufacture of aerospace intelligent structures. The design and synthesis of intelligent systems requires some effort to assess adequate sensor and actuator positioning and performance. Typically, sensors location or arrangements have been determined by using optimization approaches, in order to get the optimal performance in a particular system application. In the smart structures literature, it is usual to find investigations on PZT sensors arrangement optimization based on modal responses. Piezo-fiber composites may furnish appropriate framework to enhance sensor effective, in special when composite structures are considered. Moreover, each piezo-fiber composite sensor may be optimized, allowing adjustments of their internal laminate and PZT fibers, thereby reducing the number of required sensors and improving their integration to the primary structure. This paper presents an investigation on piezo-fiber composites optimization viewing their application as modal sensors or filtering. In this context, sensor tailoring is suggested by configuring the internal layers. The piezo-fiber composite sensor is assumed with internal layers and an extra embedded PZT fibers. Optimization is performed to each layer direction by exploring a metrics on sensor response in modal coordinates for a range in the frequency domain. The finite element modeling is used to represent the dynamics of a uniform plate structure and the piezo-composite. The genetic algorithm is considered as optimization tool using modal parameters to achieve a cost function.

A genetic algorithm (GA) and a backward moving stochastic dynamic programming (SDP) model has been developed for derivation of operational policies for a multi-reservoir system in Kodaiyar River Basin, Tamil Nadu, India. The model was developed with the objective of minimizing the annual sum of squared deviation of desired target releases. The total number of population, crossover probability and number of generations of the GA model was optimized using sensitivity analysis, and penalty function method was used to handle the constraints. The policies developed using the SDP model was evaluated using a simulation model with longer length of inflow data generated using monthly time stepped Thomas-Fiering model. The performance of the developed policies were evaluated using the performance criteria namely, the monthly frequency of irrigation deficit (MFID), Monthly average irrigation deficit (MAID), Percentage monthly irrigation deficit (PMID), Annual frequency of irrigation deficit (AFID), Annual average irrigation deficit (AAID), and Percentage annual irrigation deficit (PAID). Based on the performance, it was concluded that the robostic, probabilistic, random search GA resulted in better optimal operating policies for a multi-reservoir system than the SDP models.

Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern have a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to reduce the cost of feature measurement, increase classifier efficiency, and allow higher classification accuracy. Many current feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and increasing classification efficiency, it does not necessarily reduce the number of features that must be measured since each new feature may be a linear combination of all of the features in the original pattern vector. Here, we present a new approach to feature extraction in which feature selection, feature extraction, and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a vector of feature weights, which are used to scale the individual features in the original pattern vectors in either a linear or a nonlinear fashion. A masking vector is also employed to perform simultaneous selection of a subset of the features. We employ this technique in combination with the nearest neighbor classification rule, and compare the results with classical feature selection and extraction techniques, including sequential floating forward feature selection, and linear discriminant analysis. We also present results for the identification of favorable water-binding sites on protein surfaces, an important problem in biochemistry and drug design.

Production planning and scheduling is one of the core functions in manufacturing systems. Furthermore, this task is drawing even more attention in supply chain environments as problems become harder and more complicated. Most of the traditional approaches to production planning and scheduling have adopted a multi-phased, hierarchical and decompositional approach. This traditional approach does not guarantee a feasible production schedule. And even when capacity constraints are satisfied, it may generate an expensive schedule. In order to overcome the limitations of the traditional approach, several previous studies tried to integrate the production planning and scheduling problems. However, these studies also have some limitations, due to their intrinsic characteristics and the method for incorporating the hierarchical product structure into the scheduling model. In this paper we present a new integrated model for production planning and scheduling for multi-item and multi-level production. Unlike previous lot sizing approaches, detailed scheduling constraints and practical planning criteria are incorporated into our model. We present a mathematical formulation, propose a heuristic solution procedure, and demonstrate the performance of our model by comparing the experimental results with those of a traditional approach and optimal solution.

A novel method, based on genetic algorithms, has been developed and applied to the solution of di erential equations. The new approach is based on the use of real numbers to form the candidate solutions which are improved iteratively by a suitable breeding process. The algorithm was tested in the calculation of the bound states of a double well potential and in the nonlinear density functional calculation. Comparison of the results with those obtained using the direct relaxation method shows excellent agreement.

A lower atmosphere wind profiler, which is a pulse Doppler radar, is a useful operation tool that can provide unattended and continuous observation of wind profiles. Since the observed signals are usually degraded by ground clutters which are overlapped with the wind signal, it is necessary to develop a method to estimate the wind signal from the observed signals. In this paper, we propose to use a genetic algorithm to resolve the overlapped signals.

Extensive research has been devoted to resource constrained project scheduling problem (RCPSP). Resources are renewable and there is a unique way to perform the activities. This work develops a population based evolutionary algorithm namely differential evolution (DE) to schedule project activities to minimize makespan subject to precedence constraints and resources availability. The proposed DE uses a priority value based representation to encode a project schedule and a serial generation scheme to obtain the schedule. The DE algorithm is compared with some existing algorithms available in the literature on the basis of a computational experiment performed on Patterson's test bed. Obtained results show that the performance of the proposed DE is quite satisfactory.

Two observed deficiencies of the GA are its tendency to get trapped at local maxima and the difficulty it has handling a changing environment after convergence has occurred. A mechanism proposed by Sewall Wright in the 1930s addresses the problem of premature convergence: his Shifting Balance Theory (SBT) of evolution. In this work the SBT has been modified to remove

In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results.

We deal in this paper with the problem of automatically generating the style and the layout of web pages and web sites in a real world application where many web sites are considered. One of the main difficulty is to take into account the user preferences which are crucial in web sites design. We propose the use of an interactive genetic algorithm which generates solutions (styles, layouts) and which lets the user select the solutions that he favors based on their graphical representation. Two encodings have been defined for this problem, one linear and fixed length encoding for representing the style, and one variable length encoding based on HTML tables syntax for the layout. We present the results obtained by our method and the analysis of users behavior.

One of the most important practical considerations in the optimization of steel structures is that structural members are generally to be selected from available steel pro®les. Furthermore, the solutions produced according to the national speci®cations are undoubtedly valuable from the point of view of applicability in real-life practice. This paper reports the development of a computer-based systematic approach for discrete optimal design of planar and space structures composed of one-dimensional elements. The main characteristic of the solution methodology is the use of a genetic algorithm (GA) as the optimizer. Applications and experience on steel frame and truss structures are discussed. The results of comparative studies of the GA against other various discrete and continuous optimization algorithms for a class of representative structural design problems are reported to show the eciency of the former. It is observed that a GA often ®nds the region of the search space containing the global optimum, but not the true optimum itself. Also, in this study an approach based on a proposed multilevel optimization is tested and proved to overcome this shortcoming. #

An intense research around classifier fusion in recent years revealed that combining performance strongly depends on careful selection of classifiers to be combined. Classifier performance depends, in turn, on careful selection of features, which could be further restricted by the subspaces of the data domain. On the other hand, there is already a number of classifier fusion techniques available and the choice of the most suitable method depends back on the selections made within classifier, features and data spaces. In all these multidimensional selection tasks genetic algorithms (GA) appear to be one of the most suitable techniques providing reasonable balance between searching complexity and the performance of the solutions found. In this work, an attempt is made to revise the capability of genetic algorithms to be applied to selection across many dimensions of the classifier fusion process including data, features, classifiers and even classifier combiners. In the first of the discussed models the potential for combined classification improvement by GA-selected weights for the soft combining of classifier outputs has been investigated. The second of the proposed models describes a more general system where the specifically designed GA is applied to selection carried out simultaneously along many dimensions of the classifier fusion process. Both, the weighted soft combiners and the prototype of the three-dimensional fusion-classifier-feature selection model have been developed and tested using typical benchmark datasets and some comparative experimental results are also presented. #

Animals often spend a signifi cant portion of their lives engaged in interactions with conspecifi cs (Allee 1927 ), and engage in helping, aggression, competition, mating and cooperation, to name but a few possible exchanges. As a result, many behaviours, if not the majority, have a social facet, and even those behaviours How and when social behaviour evolves has long been a focus of study within evolutionary biology, yielding the entire subfi eld of sociobiology and behavioural ecology. Although social behaviours may be explored in the same way as any other type of phenotype, the genetics underlying social behaviours diff er from traits that do not vary depending on the social environment in which they are expressed. Social behaviour is best described as an interacting phenotype : a phenotype that depends at least in part on interactions with social partners for its expression. Models of indirect genetic eff ects provide a quantitative genetic framework for understanding the sources of variation underlying interacting phenotypes. Th ey also suggest a genetic mechanism for inheriting traits that are expressed among rather than within individual animals, and identify selection arising from the interactions (termed social selection ).

We introduce the concept of “minimal” search algorithm for a set of functions to optimize. We investigate the structure of closed under permutation (c.u.p.) sets and we calculate the performance of an algorithm applied to them. We prove that each set of functions based on the distance to a given optimal solution, among which trap functions, onemax or the recently introduced onemix functions, and the NK-landscapes are not c.u.p. and thus the thesis of the sharpened No Free Lunch Theorem does not hold for them. Thus, it makes sense to look for a specific algorithm for those sets. Finally, we propose a method to build a “good” (although not necessarily minimal) search algorithm for a specific given set of problems. The algorithms produced with this technique show better average performance than a genetic algorithm executed on the same set of problems, which was expected given that those algorithms are problem-specific. Nevertheless, in general they cannot be applied for real-life problems, given their high computational complexity that we have been able to estimate.

This paper covers stochastic particle methods for the numerical solution of the nonlinear filtering equations based on the simulation of interacting particle systems. The main contribution of this paper is to prove convergence of such approximations to the optimal filter, thus yielding what seemed to be the first convergence results for such approximations of the nonlinear filtering equations. This new treatment has been influenced primarily by the development of genetic algorithms (J. H. Holland , R. Cerf ) and secondarily by the papers of H. Kunita and L. Stettner . Such interacting particle resolutions encompass genetic algorithms. Incidentally, our models provide essential insight for the analysis of genetic algorithms with a non-homogeneous fitness function with respect to time.

Staff selection for the varying activities performed by enterprises requires a coherent approach, which cannot be simplistic, to the information held. The use of flexible computation and the vague representation of knowledge available by means of linguistic labels allow the problem to be recognised as it is in real life. This paper is an attempt to supply a satisfactory solution to a real staff management problem with linguistic information presenting a linguistic decision model for personnel problem. For reaching a good solution is proposed a novelty genetic algorithm with a linguistic biobjective fitness function.