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Overcompression is the process of post-processing compressed images to gain either further size reduction or improved quality. This is made possible by the fact that the set of all "reasonable" images has a sparse structure. In this work, we apply this idea to the overcompression of JPEG images: We reduce the blocking artifacts commonly seen in JPEG images by allowing the low frequency coefficients of the DCT to vary slightly. Evolutionary strategies are used in order to guide the modification of the coefficients towards a smoother image. [ABSTRACT FROM AUTHOR]

A Hidden Markov Model (HMM) is used as an efficient and robust technique for human activities classification. The HMM evaluates a set of video recordings to classify each scene as a function of the future, actual and previous scenes. The probabilities of transition between states of the HMM and the observation model should be adjusted in order to obtain a correct classification. In this work, these matrixes are estimated using the well known Baum-Welch algorithm that is based on the definition of the real observations as a mixture of two Gaussians for each state. The application of the GA follows the same principle but the optimization is carried out considering the classification. In this case, GA optimizes the Gaussian parameters considering as a fitness function the results of the classification application. Results show the improvement of GA techniques for human activities recognition. [ABSTRACT FROM AUTHOR]

Most of the work about flowshop scheduling problems assume that the problem data are known exactly at the advance or the common approach to the treatment of the uncertainties in the problem is use of probabilistic models. However, the evaluation and optimization of the probabilistic model is computationally expensive and rational only when the descriptions of the uncertain parameters are available from the historical data. In addition, a certain amount of delay on due dates may be tolerated in most real-world situations although they are handled as crisp dates in most of the previous papers. In this paper we deal with a flowshop scheduling problem with fuzzy processing times and flexible due dates. Schedules are generated by a proposed algorithm in the context of ant colony optimization metaheuristic approach. [ABSTRACT FROM AUTHOR]

Supply chain optimization usually involves multiple objectives. In this paper, supply chains are optimized with a multi-objective optimization approach based on genetic algorithm and simulation model. The supply chains are first modeled as batch deterministic and stochastic Petri nets, and a simulation-based optimization method is developed for inventory policies of the supply chains with a multi-objective optimization approach as its search engine. In this method, the performance of a supply chain is evaluated by simulating its Petri net model, and a Non dominated Sorting Genetic Algorithm (NSGA2) is used to guide the optimization search process towards global optima. An application to a real-life supply chain demonstrates that our approach can obtain inventory policies better than ones currently used in practice in terms of two objectives: inventory cost and service level. [ABSTRACT FROM AUTHOR]

We consider the Railway Traveling Salesman Problem, denoted RTSP, in which a salesman using the railway network wishes to visit a certain number of cities to carry out his/her business, starting and ending at the same city, and having the goal to minimize the overall time of the journey. The RTSP is NP-hard and it is related to the Generalized Traveling Salesman Problem. In this paper we present an effective meta-heuristic based on ant colony optimization (ACO) for solving the RTSP. Computational results are reported for real-world and synthetic data. The results obtained demonstrate the superiority of the proposed algorithm in comparison with the existing method. [ABSTRACT FROM AUTHOR]

A greedy randomized adaptive search procedure with path relinking is presented for the capacitated arc routing problem with time windows. Contrary to the vehicle routing problem with time windows, this problem received little attention. Numerical experiments indicate that the proposed metaheuristic is competitive with the best published algorithms: on a set of 24 instances, it reaches the optimum 17 times and improves the best-known solution 5 times, including 4 new optima. [ABSTRACT FROM AUTHOR]

CHAC, a Multi-Objective Ant Colony Optimization (MOACO), has been designed to solve the problem of finding the path that minimizes resources while maximizing safety for a military unit. The new version presented in this paper takes into acount new, more realistic, conditions and constraints. CHAC's previously proposed transition rules have been tested in more realistic maps. In addition some improvements in the implementation have been made, so better solutions are yielded. These solutions are better than a baseline greedy algorithm, and still good from a military point of view. [ABSTRACT FROM AUTHOR]

When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control. [ABSTRACT FROM AUTHOR]

This paper examines the formulation and solution of the discrete version of the stochastic Network Design Problem (NDP) with incorporated network travel time reliability requirements. The NDP is considered as a two-stage Stackelberg game with complete information and is formulated as a combinatorial stochastic bi-level programming problem. The current approach introduces the element of risk in the metrics of the design process through representing the stochastic nature of various system components related to users' attributes and network characteristics. The estimation procedure combines the use of mathematical simulation for the risk assessment with evolutionary optimization techniques (Genetic Algorithms), as they can suitably address complex non-convex problems, such as the present one. The implementation over a test network signifies the potential benefits of the proposed methodology, in terms of intrinsically incorporating stochasticity and reliability requirements to enhance the design process of urban road networks. [ABSTRACT FROM AUTHOR]

Researchers examining genetic algorithms (GAs) in applied settings rarely have access to anything other than fitness values of the best individuals to observe the behavior of the GA. In particular, researchers do not know what schemata are present in the population. Even when researchers look beyond best fitness values, they concentrate on either performance related measures like average fitness and robustness, or low-level descriptions like bit-level diversity measures. To understand the behavior of the GA on dynamic problems, it would be useful to track what is occurring on the "semantic" level of schemata. Thus in this paper we examine the evolving "content" in terms of schemata, as the GA solves dynamic problems. This allows us to better understand the behavior of the GA in dynamic environments. We finish by summarizing this knowledge and speculate about future work to address some of the new problems that we discovered during these experiments. [ABSTRACT FROM AUTHOR]

This paper presents an evolutionary computing approach for the estimation of dynamic Origin-Destination (O-D) trip matrices from automatic traffic counts in urban networks. A multi-objective, simultaneous optimization problem is formulated to obtain a mutually consistent solution between the resulting O-D matrix and the path/link flow loading pattern. A genetically augmented microscopic simulation procedure is used to determine the path flow pattern between each O-D pair by estimating the set of turning proportions at each intersection. The proposed approach circumvents the restrictions associated with employing a user-optimal Dynamic Traffic Assignment (DTA) procedure and provides a stochastic global search of the optimal O-D trip and turning flow distributions. The application of the model into a real arterial street sub-network demonstrates its ability to provide results of satisfactory accuracy within fast computing speeds and, hence, its potential usefulness to support the deployment of dynamic urban traffic management systems. [ABSTRACT FROM AUTHOR]

In the steady-state model for genetic algorithms (SSGA), the choice of a replacement strategy plays an important role in performance. Being able to handle changes is important for an optimization algorithm since many real-world problems are dynamic in nature. The main aim of this study is to experimentally compare different variations for basic replacement strategies in a dynamic environment. To cope with changes, a very simple mechanism of duplicate elimination is used. As an example of a dynamic problem, a dynamic version of the multi-dimensional knapsack problem is chosen. The results obtained here are in keeping with previous studies while some further interesting results are also obtained due to the special landscape features of the chosen problem. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are time-varying. In this paper, a triggered memory scheme is introduced into the particle swarm optimization to deal with dynamic environments. The triggered memory scheme enhances traditional memory scheme with a triggered memory generator. Experimental study over a benchmark dynamic problem shows that the triggered memory-based particle swarm optimization algorithm has stronger robustness and adaptability than traditional particle swarm optimization algorithms, both with and without traditional memory scheme, for dynamic optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents an approach to new method for music composition with multi-agent system, which is a field of new research and offers vast possibilities for both scientific research and artistic expression. Proposed model is an ecological approach in which the agents coexist in a virtual world displaying predator/prey behavior. It realizes self-organization of sound structure and real-time performance with laptop PC. The demo movies, including all reference movies in section 3 are available on the Web (http://www.dubdb.com/gismo/ evo/). Please refer to them for agents' behaviors and sounds. [ABSTRACT FROM AUTHOR]

When dealing with dynamic environments two major aspects must be considered in order to improve the algorithms' adaptability to changes: diversity and memory. In this paper we propose and study a new evolutionary algorithm that combines two populations, one playing the role of memory, with a biological inspired recombination operator to promote and maintain diversity. The size of the memory mechanism may vary along time. The size of the (usual) search population may also change in such a way that the sum of the individuals in the two populations does not exceed an established limit. The two populations have minimum and maximum sizes allowed that change according to the stage of the evolutionary process: if an alteration is detected in the environment, the search population increases its size in order to readapt quickly to the new conditions. When it is time to update memory, its size is increased if necessary. A genetic operator, inspired by the biological process of conjugation, is proposed and combined with this memory scheme. Our ideas were tested under different dynamics and compared with other approaches on two benchmark problems. The obtained results show the efficacy, efficiency and robustness of the investigated algorithm. [ABSTRACT FROM AUTHOR]

Addressing dynamic optimization problems has been a challenging task for the genetic algorithm community. Over the years, several approaches have been developed into genetic algorithms to enhance their performance in dynamic environments. One major approach is to maintain the diversity of the population, e.g., via random immigrants. This paper proposes an elitism-based immigrants scheme for genetic algorithms in dynamic environments. In the scheme, the elite from previous generation is used as the base to create immigrants via mutation to replace the worst individuals in the current population. This way, the introduced immigrants are more adapted to the changing environment. This paper also proposes a hybrid scheme that combines the elitism-based immigrants scheme with traditional random immigrants scheme to deal with significant changes. The experimental results show that the proposed elitism-based and hybrid immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments. [ABSTRACT FROM AUTHOR]

This paper introduces a new evolutionary model of the art and design process. The whole process of interactive image creating was decomposed into three steps: evolving the Bezier curve, evolving the geometric transformations of the curve, and evolving the style of painting it. A model was implemented, and a series of promising experiments was performed. [ABSTRACT FROM AUTHOR]

This paper discusses a study towards dynamic fitness based partitioning in IntraVascular UltraSound (IVUS) image analysis. Mixed-Integer Evolution Strategies (MI-ES) have recently been successfully used to optimize control parameters of a multi-agent image interpretation system for IVUS images lumen detection. However, because of complex interpretation contexts, it is impossible to find one single solution which works well on each possible image of each possible patient. Therefore it would be wise to let MI-ES find a set of solutions based on an optimal partition of IVUS images. Here a methodology is presented which does dynamic fitness based partitioning of the data during the MI-ES parameter optimization procedure. As a first step we applied this method to a challenging artificial test case which demonstrates the feasibility of our approach. [ABSTRACT FROM AUTHOR]

The work concerns using evolutionary methods to evolve melodic sequences, obtained through a music generative approach from Chua's circuit, a non-linear dynamic system, universal paradigm for studying chaos. The main idea was to investigate how to turn potential aesthetical musical forms, generated by chaotic attractors, in melodic patterns, according to the western musical tradition. A single attractor was chosen from the extended gallery of the Chua's dynamical systems. A specific codification scheme was used to map the attractor's space of phases into the musical pitch domain. A genetic algorithm was used to search throughout all possible solutions in the space of the attractor's parameters. Musical patterns were selected by a suitable fitness function. Experimental data show a progressive increase of the fitness values. [ABSTRACT FROM AUTHOR]

Music pieces have been composed using a behavior-inspired evolutionary algorithm, harmony search (HS). The HS algorithm mimics behaviors of music players in an improvisation process, where each player produces a pitch based on one of three operations (random selection, memory consideration, and pitch adjustment) in order to find a better state of harmony which can be translated into a solution vector in the optimization process. When HS was applied to the organum (an early form of polyphonic music) composition, it could successfully compose harmony lines based on original Gregorian chant lines. [ABSTRACT FROM AUTHOR]

Research on the application of Interactive Evolutionary Computation(IEC) to the field of musical computation has been improved in recent years, marking an interesting parallel to the current trend of applying human characteristics or sensitivities to computer systems. However, past techniques developed for IEC-based composition have not necessarily proven very effective for professional use. This is due to the large difference between data representation used by IEC and authored classical music composition. To solve this difficulties, we purpose a new IEC approach to music composition based on classical music theory. In this paper, we describe an established system according to the above idea, and detail of making success of composition a piece. [ABSTRACT FROM AUTHOR]

Most real life applications have huge search spaces. Evolutionary Computation provides an advantage in the form of parallel explorations of many parts of the search space. In this report, Genetic Programming is the technique we used to search for good melodic fragments. It is generally accepted that knowledge is a crucial factor to guide search. Here, we show that SOM can be used to facilitate the encoding of domain knowledge into the system. The SOM was trained with music of desired quality and was used as fitness functions. In this work, we are not interested in music with complex rules but with simple music employed in computer games. We argue that this technique provides a flexible and adaptive means to capture the domain knowledge in the system. [ABSTRACT FROM AUTHOR]

This paper describes an automated computer improviser which attempts to follow and improvise against the frequencies and timbres found in an incoming audio stream. The improviser is controlled by an ever changing set of sequences which are generated by analysing the incoming audio stream (which may be a feed from a live musician) for its physical and musical properties such as pitch and amplitude. Control data from these sequences is passed to the synthesis engine where it is used to configure sonic events. These sonic events are generated using sound synthesis algorithms designed by an unsupervised genetic algorithm where the fitness function compares snapshots of the incoming audio to snapshots of the audio output of the evolving synthesizers in the spectral domain in order to drive the population to match the incoming sounds. The sound generating performance system and sound designing evolutionary system operate in real time in parallel to produce an interactive stream of synthesised sound. An overview of related systems is provided, this system is described then some preliminary results are presented. [ABSTRACT FROM AUTHOR]

This paper describes an experiment carried out to determine which, among several possible evolutionary and non-evolutionary sound synthesizer graphical user interfaces, is the most suitable for the task of matching a target sound. Results show that standard and new varieties of evolutionary interface are competitive with a standard non-evolutionary interface, achieving better results in some situations and worse in others. Subjects' comments suggest a preference for a new type of evolutionary interface, presented here, which allows faster audition of the population, avoiding the need for time-consuming fitness evaluation of poor-quality sounds. [ABSTRACT FROM AUTHOR]

We present several practical improvements to the interactive evolution of 2D images, some of which are also applicable to more general genetic programming problems. We introduce tree alignments to improve the animation of evolved images when using genetic cross dissolves. The goal of these improvements is to strengthen the interactive evolution toolset and give the artist greater control and expressive power. [ABSTRACT FROM AUTHOR]

This paper presents an approximation towards an evolutionary generator of alliterative text. A simple text is given along with the preferred phoneme for alliterations as input. Then the evolutionary algorithm (with the aid of a phonemic transcriber, Microsoft Word and Google) will try to produce an alternative sentence while trying to preserve the initial meaning and coherence. A bigram language model and the evaluation of the phonetic analysis are used to assess the fitness of the sentences. [ABSTRACT FROM AUTHOR]

A new way to control sound spatial dispersion using the ESSynth Method is introduced here. The Interaural Time Difference (ITD) is used as genotype of an evolutionary control of sound spatialization. Sound intensity and the ITD azimuth angle are used to define spatial dispersion and spatial similarity. Experimental results where crossover and mutation rates were used to create spatial sonic trajectories are discussed. [ABSTRACT FROM AUTHOR]

This paper outlines a current lack of consideration for the environmental context of Evolutionary Algorithms used for the generation of music. We attempt to readdress this balance by outlining the benefits of developing strong coupling strategies between agent and environment. It goes on to discuss the relationship between artistic process and the viewer and suggests a placement of the viewer and agent in a shared environmental context to facilitate understanding of the artistic process and a feeling of participation in the work. The paper then goes on to outline the installation ‘Excuse MeÕ and how it attempts to achieve a level of Sonic Ecology through the use of a shared environmental context. [ABSTRACT FROM AUTHOR]

Photomosaics are images composed of smaller images (tiles). Viewed close up the details of the tiles are evident and the big picture is lost. Viewed from a distance the detail of the tiles is lost and the big picture is evident. We show how photomosaics can be generated by evolutionary search and how animations can be created by using the best individuals in a generation as frames of a movie. The animations can generate engaging visual effects such as gradually ‘materialising' a face from a random arrangement of tiles on a screen. Our animations explore self representation through dynamic reinterpretations of mosaic and iconography traditions using target images and tiles that we created. [ABSTRACT FROM AUTHOR]

We present a time domain approach to explore a sound transformation paradigm for musical performance. Given a set of sounds containing a priori desired qualities and a population of agents interacting locally, the method generates both musical form and matter resulting from sonic trajectories. This proposal involves the use of bio-inspired algorithms, which possess intrinsic features of adaptive, self-organizing systems, as definers of generating and structuring processes of sound elements. Self-organization makes viable the temporal emergence of stable structures without an external organizing element. Regarding musical performance as a creative process that can be described using trajectories through the compositional space, and having the simultaneous emergence of musical matter and form resulting from the process itself as the final objective, the conception of a generative paradigm in computer music that does not contemplate a priori external organizing elements is the main focus of this proposal. [ABSTRACT FROM AUTHOR]

In this paper we introduce an hybrid evolutionary algorithm for computer-aided orchestration. Our current approach to orchestration consists in replicating a target sound with a set of instruments sound samples. We show how the orchestration problem can be viewed as a multi-objective 0/1 knapsack problem, with additional constraints and a case-specific criteria formulation. Our search method hybridizes genetic search and local search, for both of which we define ad-hoc genetic and neighborhood operators. A simple modelling of sound combinations is used to create two new mutation operators for genetic search, while a preliminary clustering procedure allows for the computation of sound mixtures neighborhoods for the local search phase. We also show in which way user interaction might be introduced in the orchestration procedure itself, and how to lead the search according to the users choices. [ABSTRACT FROM AUTHOR]

In this essay the construction of an interactive genetic algorithm and agents based application will be described. This construction is driven to plan the development of a human mosaic, starting from an initial design and an adjustment of several parameters. In addition, the same idea of generation will be show as suitable to be applied to character group movements inside a videogame. The software developed is enclosed, as far as the "creative" part of the solution is concerned, inside the evolutionary computation paradigm; while into agent orientation regarding characters coordination. [ABSTRACT FROM AUTHOR]

The research presented in this paper deals concerns interactive evolutionary design systems and specifically with the Interactive Evolutionary Design Environment (IEDE) developed by the authors. We describe the IEDE concentrating upon the three major components: Component-based Representation; Construction and Repair Agents (providing build and evolve services) and a machine learning sub-system. We also describe the clustering technique utilized within the IEDE to improve the user interactivity of the system. [ABSTRACT FROM AUTHOR]

We propose an interactive computer graphics authoring method based on interactive evolutionary computation (IEC). Previous systems mainly employed genetic algorithm (GA) to explore an optimum set of 3D graphics parameters. The proposed method adopts a different computation model called immune algorithm (IA) to ease the creation of varied 3D models even if a user doesn't have any specific idea of final 3D products. Because artistic work like graphics design needs a process to diversify the user's imagery, a tool that can show the user a broad range of solutions is particularly important. IA enables to effectively explore a global optimum solution as well as other multiple quasi-optimum solutions in a huge search space by using its essential mechanisms such as antibody formation and self-regulating function. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method to support texture mapping for intuitive designing of 3D objects in the scene of virtual or real space. To fit a texture pattern to the target 3D object, users should consider not only physical constraints, such as resolutions and scales, but also psychological constraints, such as fitness or moods. This method generates multiple candidate models applying various kinds of texture patterns and allows users to evaluate them sensitively. The technique called Interactive Evolutionary Computation (IEC) helps them to find the pleasant and adequate a texture pattern for the scene easily. Texture pattern is improved corresponding to users' evaluations. This framework provides a powerful environment for interactive texture design. [ABSTRACT FROM AUTHOR]

Design and optimisation of modern complex mixed analogue-digital circuits require new approaches to circuit sizing. In this paper, we present a novel approach based on the application of the univariate marginal distribution algorithm to circuit sizing at the system level. The results of automotive electronics circuits sizing indicate that all design requirements have been fulfilled in comparison with a human design. Experiments indicate that elitism increases the performance of the algorithm. [ABSTRACT FROM AUTHOR]

This work presents scale invariant region detectors that apply evolved operators to extract an interest measure. We evaluate operators using their repeatability rate, and have experimentally identified a plateau of local optima within a space of possible interest operators Ω. The space Ω contains operators constructed with Gaussian derivatives and standard arithmetic operations. From this set of local extrema, we have chosen two operators, obtained by searching within Ω using Genetic Programming, that are optimized for high repeatability and global separability when imaging conditions are modified by a known transformation. Then, by embedding the operators into the linear scale space generated with a Gaussian kernel we can characterize scale invariant features by detecting extrema within the scale space response of each operator. Our scale invariant region detectors exhibit a high performance when compared with state-of-the-art techniques on standard tests. [ABSTRACT FROM AUTHOR]

Photographic supra-projection is a complex and uncertain process that aims at identifying a person by overlaying a photograph and a model of the skull found. The more accurate the skull model is the more reliable the identification decision will be. Usually, forensics are obliged to perform a manual and time consuming process in order to obtain the model of the scanned forensic object. At least semiautomatic methods are demanded by these experts to assist them with this task. Our contribution aims to propose an evolutionary-based image registration methodology for the skull 3D model building problem. Experiments are performed over thirty two problem instances corresponding to a semiautomatic and fully automatic real skull model reconstruction. [ABSTRACT FROM AUTHOR]

This work describes an evolutionary approach to texture segmentation, a long-standing and important problem in computer vision. The difficulty of the problem can be related to the fact that real world textures are complex to model and analyze. In this way, segmenting texture images is hard to achieve due to irregular regions found in textures. We present our EvoSeg algorithm, which uses knowledge derived from texture analysis to identify how many homogeneous regions exist in the scene without a priori information. EvoSeg uses texture features derived from the Gray Level Cooccurrence Matrix and optimizes a fitness measure, based on the minimum variance criteria, using a hierarchical GA. We present qualitative results by applying EvoSeg on synthetic and real world images and compare it with the state-of-the-art JSEG algorithm. [ABSTRACT FROM AUTHOR]

In this paper we address the problem of aligning two partially overlapping surfaces represented by points obtained in subsequent 2D scans for mobile robot pose estimation. The measured points representation contains incomplete measurements. We solve this problem by minimizing an alignment error via a genetic algorithm. Moreover, we propose an alignment metric based on a look-up table built during the first scan. Experimental results related to the convergence of the proposed algorithm are reported. We compare our approach with other scan matching algorithms proposed in the literature, and we show that our approach is faster and more accurate than the others. [ABSTRACT FROM AUTHOR]

The removal of periodic noise is an important problem in image processing. To avoid using the time-consuming methods that require Fourier transform, a simple and efficient spatial filter based on soft mathematical morphology (MM) is proposed in this paper. The soft morphological filter (Soft MF) is optimized by an improved particle swarm optimizer with passive congregation (PSOPC) subject to the least mean square error criterion. The performance of this new filter and its comparison with other commonly used filters are also analyzed, which shows that it is more effective in reducing both periodic and non-periodic noise meanwhile preserving the details of the original image. [ABSTRACT FROM AUTHOR]

The triangulation is a process by which the 3D point position can be calculated from two images where that point is visible. This process requires the intersection of two known lines in the space. However, in the presence of noise this intersection does not occur, then it is necessary to estimate the best approximation. One option towards achieving this goal is the usage of evolutionary algorithms. In general, evolutionary algorithms are very robust optimization techniques, however in some cases, they could have some troubles finding the global optimum getting trapped in a local optimum. To overcome this situation some authors suggested removing the local optima in the search space by means of a single-objective problem to a multi-objective transformation. This process is called multi-objectivization. In this paper we successfully apply this multi-objectivization to the triangulation problem. [ABSTRACT FROM AUTHOR]

We use a genetic algorithm to solve the problem, widely treated in the specialized literature, of fitting an ellipse to a set of given points. Our proposal uses as the objective function the minimization of the sum of orthogonal Euclidean distances from the given points to the curve; this is a non-linear problem which is usually solved using the minimization of the quadratic distances that allows to use the gradient and the numerical methods based on it, such as Gauss-Newton. The novelty of the proposed approach is that as we are using a GA, our algorithm does not need initialization, and uses the Euclidean distance as the objective function. We will also show that in our experiments, we are able to obtain better results than those previously reported. Additionally our solutions have a very low variance, which indicates the robustness of our approach. [ABSTRACT FROM AUTHOR]

Feature extraction is a crucial step for Computer Vision applications. Finding appropriate features for an application often means hand-crafting task specific features with many parameters to tune. A generalisation to other applications or scenarios is in many cases not possible. Instead of engineering features, we describe an approach which uses Genetic Programming to generate features automatically. In addition, we do not predefine the dimension of the feature vector but pursue an iterative approach to generate an appropriate number of features. We present this approach on the problem of texture classification based on co-occurrence matrices. Our results are compared to those obtained by using seven Haralick texture features, as well as results reported in the literature on the same database. Our approach yielded a classification performance of up to 87% which is an improvement of 30% over the Haralick features. We achieved an improvement of 12% over previously reported results while reducing the dimension of the feature vector from 78 to four. [ABSTRACT FROM AUTHOR]

This paper describes a linear genetic programming approach to multi-class image recognition problems. A new fitness function is introduced to approximate the true feature space. The results show that this approach outperforms the basic tree based genetic programming approach on all the tasks investigated here and that the programs evolved by this approach are easier to interpret. The investigation on the extra registers and program length results in heuristic guidelines for initially setting system parameters. [ABSTRACT FROM AUTHOR]

This article proposes a Memetic Differential Evolution (MDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. The MDE is an adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution (DE) with the exploitative features of two local searchers. The local searchers are adaptively activated by means of a novel control parameter which measures fitness diversity within the population. Numerical results show that the DE framework is efficient for the class of problems under study and employment of exploitative local searchers is helpful in supporting the DE explorative mechanism in avoiding stagnation and thus detecting solutions having a high performance. [ABSTRACT FROM AUTHOR]

The study of human brain functions has dramatically increased in recent years greatly due to the advent of Functional Magnetic Resonance Imaging. This paper presents a genetic programming approach to the problem of classifying the instantaneous cognitive state of a person based on his/her functional Magnetic Resonance Imaging data. The problem provides a very interesting case study of training classifiers with extremely high dimensional, sparse and noisy data. We apply genetic programming for both feature selection and classifier training. We present a successful case study of induced classifiers which accurately discriminate between cognitive states produced by listening to different auditory stimuli. [ABSTRACT FROM AUTHOR]

We describe a novel method of evolutionary visual learning that uses generative approach for assessing learner's ability to recognize image contents. Each learner, implemented as a genetic programming individual, processes visual primitives that represent local salient features derived from a raw input raster image. In response to that input, the learner produces partial reproduction of the input image, and is evaluated according to the quality of that reproduction. We present the method in detail and verify it experimentally on the real-world task of recognition of hand-drawn shapes. [ABSTRACT FROM AUTHOR]

We propose a BCI mouse and speller based on the manipulation of P300 waves in EEG signals. The 2-D motion of the pointer on the screen is controlled by directly combining the amplitudes of the output produced by a filter in the presence of different stimuli. This filter and the features to be combined within it are optimised by a GA. [ABSTRACT FROM AUTHOR]

This paper presents a linear genetic programming approach, that solves simultaneously the region selection and feature extraction tasks, that are applicable to common image recognition problems. The method searches for optimal regions of interest, using texture information as its feature space and classification accuracy as the fitness function. Texture is analyzed based on the gray level cooccurrence matrix and classification is carried out with a SVM committee. Results show effective performance compared with previous results using a standard image database. [ABSTRACT FROM AUTHOR]