OBJECTIVE: Ubiquitous technologies can be leveraged to construct ecologically relevant metrics that complement traditional psychological assessments. This study aims to determine the feasibility of smartphone-derived real-world keyboard metadata to serve as digital biomarkers of mood. MATERIALS AND METHODS: BiAffect, a real-world observation study based on a freely available iPhone app, allowed the unobtrusive collection of typing metadata through a custom virtual keyboard that replaces the default keyboard. User demographics and self-reports for depression severity (Patient Health Questionnaire-8) were also collected. Using >14 million keypresses from 250 users who reported demographic information and a subset of 147 users who additionally completed at least 1 Patient Health Questionnaire, we employed hierarchical growth curve mixed-effects models to capture the effects of mood, demographics, and time of day on keyboard metadata. RESULTS: We analyzed 86 541 typing sessions associated with a total of 543 Patient Health Questionnaires. Results showed that more severe depression relates to more variable typing speed (P < .001), shorter session duration (P < .001), and lower accuracy (P < .05). Additionally, typing speed and variability exhibit a diurnal pattern, being fastest and least variable at midday. Older users exhibit slower and more variable typing, as well as more pronounced slowing in the evening. The effects of aging and time of day did not impact the relationship of mood to typing variables and were recapitulated in the 250-user group. CONCLUSIONS: Keystroke dynamics, unobtrusively collected in the real world, are significantly associated with mood despite diurnal patterns and effects of age, and thus could serve as a foundation for constructing digital biomarkers.

Bayesian Network (BN) structure learning is a complex search problem, generally characterized by multimodality and epistasis. Genetic Algorithms (GAs) have been extensively used to pursue the BN structure learning task. This paper presents a new approach which incorporates the structural properties of the problem into GA mechanisms. The proposed approach uses a new recombination operator named Parent Set crossover, capable of reducing the disruptive action of the recombination process and enhancing its exploitative power. The new operator has been compared with a comprehensive set of other crossover operators as part of two genetic strategies: a canonical GA and a GA with an adaptive mutation scheme. All examined crossover operators were applied on both canonical and adaptive GAs and then compared in terms of various performance metrics. The experiments involve performance measures at the end of evolution as well as their convergence behavior across generations. The performance of the proposed method was also compared with the state-of-the-art non-evolutionary BN structure learning algorithms. Results show that the proposed recombination method enhances the algorithmic efficiency over a variety of test cases of different size.

Exploration and exploitation are the two cornerstones which characterize Evolutionary Algorithms (EAs) capabilities. Maintaining the reciprocal balance of the explorative and exploitative power is the key to the success of EA applications. Accordingly, this work is concerned with proposing a diversity-guided genetic algorithm with a new mutation scheme that is capable of exploring the unseen regions of the search space, as well as exploiting the already-found promising elements. The proposed mutation operator specifies different mutation rates for different sites of an encoded solution. These site-specific rates are carefully derived based on the underlying pattern of highly-fit solutions, adjusted to every single individual, and adapted throughout the evolution to retain a good ratio between exploration and exploitation. Furthermore, in order to more directly monitor the exploration vs. exploitation balance, the proposed method is augmented with a diversity control process assuring that the search process does not lose the required balance between the two forces.

This paper is concerned with proposing an elitist genetic algorithm which makes use of a new mutation scheme aimed to tackle both explorative and exploitative responsibilities of genetic operators. The proposed mutation scheme follows an approach similar to motif representation in biology, to derive the underlying pattern of highly-fit solutions discovered so far. This pattern is then used to derive mutation rates specified for every site along the encoded solutions. The site-specific rates are amended for every individual to balance the required explorative and exploitative power. The Markov chain model of the proposed method is also derived and used to analyze its convergence properties.

Real-time parking availability information is important in urban areas, and if available could reduce congestion, pollution, and gas consumption. In this paper, we present a software solution called PhonePark for detecting the availability of on-street parking spaces. The solution uses the GPS and/or accelerometer sensors in a traveler's mobile phone to automatically detect when and where the traveler parked her car, and when she released a parking slot. PhonePark can also utilize the mobile phone's Bluetooth sensor or piggyback on street parking payment transactions for parking activity detection. Thus, the solution considers only mobile phones and does not rely on any external sensors such as cameras, wireless sensors embedded in the pavements, or ultrasonic sensors on vehicles. Further contributions include an algorithm to compute the historical parking availability profile for an arbitrary street block and algorithms to estimate the parking availability in real-time for a given street block. The algorithms are evaluated using real-time and real world street parking data.

Exploration and exploitation are the two cornerstones which characterize Evolutionary Algorithms (EAs) capabilities. Maintaining the reciprocal balance of the explorative and exploitative power is the key to the success of EA applications. Accordingly, in this work the canonical Genetic Algorithm is augmented by a new mutation scheme that is capable of exploring the unseen regions of the search space, and simultaneously exploiting the already-found promising elements. The proposed mutation operator specifies different mutation rates for different sites (loci) of the individuals. These site-specific rates are wisely derived based on the fitness and structure of the population individuals. In order to retain the balance of the required exploration and exploitation, the mutation rates are adapted during the evolution. To demonstrate the efficacy of the proposed algorithm, the method is evaluated using a set of benchmark problems and the outcome is compared with a series of well-known relevant algorithms. The results demonstrate that the newly suggested method significantly outperforms its rivals.

This work is concerned with proposing a robust framework for optimizing operator rates of simple Genetic Algorithms (GAs) during a GA run. The suggested framework is built upon a formerly proposed GA Markov chain model to estimate the optimal values of the operator rates based on the time and the current state of the evolution. Though the proposed framework has been formalized for optimizing both mutation and crossover rates, in the current paper, we only implemented it as the mutation rate optimizer and kept the crossover rate constant. To demonstrate the efficacy of the proposed algorithm, the method is evaluated using a set of benchmark problems and the outcome is compared with a series of well-known relevant algorithms. The results demonstrate that the newly suggested algorithm significantly outperforms its rivals.

The simulation of intervehicle communication (IVC) in a Vehicular Ad-Hoc Network (VANET) requires the modeling of traffic movement patterns and the simulation of wireless communication between the vehicles which act as mobile nodes within the wireless network. This paper discusses the calibration and fitness of the JIST/SWANS network simulator for use in hybrid VANET simulation using readily available wireless hardware. The associated research creates a hybrid simulation platform coupling the microscopic transportation modeling of flow and vehicle movement with a wireless simulator to offer analysis on wireless interaction in a vehicular network. Although the ultimate goal of this research is to model the behavior of a handheld Intelligent Traveler Assistant (ITA) device, this paper concerns the validation of the ad-hoc wireless portion of the SWANS simulator.

Dealing with many free parameters and finding an appropriate set of parameter values for an evolutionary algorithm (EA) has been a longstanding major challenge of the evolutionary computation community. Such difficulty has directed researchers' attention towards devising an automated ways of controlling EA parameters. This work is concerned with proposing a novel method which adaptively adjusts EA and specifically genetic algorithm (GA) mutation rates. The proposed method incorporates the underlying statistical framework of biological evolutionary models into the generic context of evolutionary algorithms. By using such model, besides adapting the mutation rate, this method aims to wisely determine the types of replacing genes in the mutation procedure. To demonstrate the efficacy of the proposed algorithm, the method is evaluated using a wide array of test functions and the outcome is compared with a state-of-the-art adaptive mutation evolutionary algorithm. The results demonstrates that the newly suggested algorithm significantly outperform its adaptive rival in most of the test cases.

One problem that has plagued Genetic Programming (GP) and its derivatives is numerical constant creation. Given a mathematical formula expressed as a tree structure, the leaf nodes are either variables or constants. Such constants are usually unknown in Symbolic Regression (SR) problems, and GP, as well as many of its derivatives, lack the ability to precisely approximate these values. This is due to the inherently discrete encoding of GP-like methods which are more suited to combinatorial searches than real-valued optimization tasks. Previously, several attempts have been made to resolve this issue, and the dominant solutions have been to either embed a real-valued local optimizer or to develop additional numerically oriented operators. In this paper, an entirely new approach is proposed for constant creation. Through the adoption of a robust, real-valued optimization algorithm known as Differential Evolution (DE), constants and GP-like programs will be simultaneously evolved in such a way that the values of the leaf nodes will be approximated as the tree structure is itself changing. Experimental results from several SR benchmarks are presented and analyzed. The results demonstrate the feasibility of the proposed algorithm and suggest that exotic or computationally expensive methods are not necessary for successful constant creation.

This work is concerned with proposing an adaptive method to dynamically adjust genetic operator probabilities throughout the evolutionary process. The proposed method relies on the individual preferences of each chromosome, rather than the global behavior of the whole population. Hence, each individual carries its own set of parameters, including the probabilities of the genetic operators. The carried parameters undergo the same evolutionary process as the carriers--the chromosomes - do. We call this method Evolved Evolutionary Algorithm (E2A) as it has an additional evolutionary process to evolve control parameters. Furthermore, E2A employs a supplementary mutation operator (DE-mutation) which utilizes the previously overlooked numerical optimization model known as the Differential Evolution to expedite the optimization rate of the genetic parameters. To leverage our previous work, we used Gene Expression Programming (GEP) as a benchmark to determine the performance of our proposed method. Nevertheless, E2A can be easily extended to other genetic programming variants. As the experimental results on a wide array of regression problems demonstrate, the E2A method reveals a faster rate of convergence and provides fitter ultimate solutions. However, to further expose the power of the E2A method, we compared it to related methods using self-adaptation previously applied to Genetic Algorithms. Our benchmarking on the same set of regression problems proves the supremacy of our proposed method both in the accuracy and simplicity of the final solutions.

The European Union Digital Library (EUDL) is the central repository for all content published by the different branches of the European Alliance for Innovation (EAI). EUDL also hosts or indexes content from selected partner or member organizations and affiliates.

Recent work has focused on creating models for generating traveler behavior for micro simulations. With the increase in hand held computers and GPS devices, there is likely to be an increasing demand for extending this idea to predicting an individual’s future travel plans for devices such as a smar

Gene Expression Programming (GEP) is an evolutionary algorithm that incorporates both the idea of a simple, linear chromosome of fixed length used in Genetic Algorithms (GAs) and the tree structure of different sizes and shapes used in Genetic Programming (GP). As with other GP algorithms, GEP has difficulty finding appropriate numeric constants for terminal nodes in the expression trees. In this work, we describe a new approach of constant generation using Differential Evolution (DE), a real-valued GA robust and efficient at parameter optimization. Our experimental results on two symbolic regression problems show that the approach significantly improves the performance of the GEP algorithm. The proposed approach can be easily extended to other Genetic Programming variations.

Inter-vehicle communication by means of wireless Ad Hoc networking has the potential to improve traffic safety and comfort tremendously. Therefore, the application of Vehicular Ad Hoc Networks (VANETs) in the service of Intelligent Transportation Systems (ITS) has been highly focused in recent years. Derived from the successful outcome of a cluster-based framework in Mobile Ad Hoc Networks (MANETs), we apply this network topology to VANETs. Unfortunately, previous studies lack realistic modeling of vehicle mobility and evaluation of clustering performance so they may not correlate well with performance in a real deployment. Hence, in this paper, we propose a realistic micro- simulation model with the hope of contributing to clustering research in VANETs, and demonstrate how clustering algorithms work on it.

Modularity and building blocks have drawn attention from the genetic programming (GP) community for a long time. The results are usually twofold: a hierarchical evolution with adequate building block reuse can accelerate the learning process, but rigidly defined and excessively employed modules may also counteract the expected advantages by confining the reachable search space. In this work, we introduce the concept of emergent loose modules based on a new linear GP system, prefix gene expression programming (P-GEP), in an attempt to balance between the stochastic exploration and the hierarchical construction for the optimal solutions. Emergent loose modules are dynamically produced by the evolution, and are reusable as sub-functions in later generations. The proposed technique is fully illustrated with a simple symbolic regression problem. The initial experimental results suggest it is a flexible approach in identifying the evolved regularity and the emergent loose modules are critical in composing the best solutions

Linear genotype representation and modularity have continuously received extensive attention from the genetic programming (GP) community. The advantages of a linear genotype include a convenient and efficient implementation scheme. However, most existing techniques using a linear genotype follow the imperative programming language paradigm and a direct hierarchical composition for the functionality of the solution is underachieved. Our work is based on prefix gene expression programming (P-GEP), a new GP method featured by a prefix notation based linear genotype representation. Since P-GEP uses a functional language paradigm, its framework results in natural self-emergence of substructures as functional components during the evolution. We propose to preserve and utilize potentially useful emergent substructures via a dynamic substructure library, empowering the algorithm to focus the search on a higher level of the solution structure. Preliminary experiments on the benchmark regression problems have shown the effectiveness of this approach.

We developed a testbed containing: real time data from over 830 highway traffic sensors in the Chicago region, data about weather, and text data about events that might affect traffic. The goal was to detect in real time interesting changes in traffic conditions. Given the size and complexity of the data, we choose to build a large number of separate baseline models. We built a separate baseline for each hour in the day, for each day in the week, and for every 2 or 3 traffic sensors, resulting in over 42,000 separate baseline models. We also built a baseline engine to build the necessary baselines automatically. We modified an open source scoring engine to process in real time each new sensor reading, update the appropriate feature vectors, score the updated feature vectors using the baseline models, and send out real time alerts when deviations from the baselines were detected.

Visualization of a search process can be an effective way to verify the performance of a search algorithm, especially in terms of its coverage of the search space and behavior near local optima. Planar and 3D surface graphs provide the best intuition, e.g., for highlighting "nearest neighbor" solutions. However, they cannot be directly applied to most search and optimization problems because of the high dimensionality of the search space. Building on insights from space-filling curves and their application for combinatorial optimization problems, This work presents a technique for mapping the solution space of hundred-variable combinatorial optimization problems into two-dimensions. Experimental results for electronics assembly optimization are presented as an example of this technique. Mathematical properties of the proposed N-to-2-space transformation are discussed, and several sample visualizations are presented.

Classification is one of the fundamental tasks of data mining. Most rule induction and decision tree algorithms perform a local, greedy search to generate classification rules that are often more complex than necessary. Evolutionary algorithms for pattern classification have recently received increased attention because they can perform global searches. In this paper, we propose a new approach for discovering classification rules by using gene expression programming (GEP), a new technique of genetic programming (GP) with linear representation. The antecedent of discovered rules may involve many different combinations of attributes. To guide the search process, we suggest a fitness function considering both the rule consistency gain and completeness. A multiclass classification problem is formulated as multiple two-class problems by using the one-against-all learning method. The covering strategy is applied to learn multiple rules if applicable for each class. Compact rule sets are subsequently evolved using a two-phase pruning method based on the minimum description length (MDL) principle and the integration theory. Our approach is also noise tolerant and able to deal with both numeric and nominal attributes. Experiments with several benchmark data sets have shown up to 20% improvement in validation accuracy, compared with C4.5 algorithms. Furthermore, the proposed GEP approach is more efficient and tends to generate shorter solutions compared with canonical tree-based GP classifiers.

Applications of computer simulation for surface mount technology (SMT) assembly lines have addressed many aspects of supply chain management, e.g., estimating cycle times, evaluating production equipment and line configurations, optimizing throughput and assessing product design for manufacturability. The implementation of an SMT machine simulator typically requires specialized code based on cycle time characterization experiments for the given machine type. This paper describes a generic simulation toolkit based on a classification of current SMT equipment. A detailed survey of machines has led to the definition of machine families and a generic machine model. A simulation toolkit consisting of object classes written in C++ has been developed to represent the generic machine model. Object-oriented design has enabled the construction of an object hierarchy that models the physical structure of a machine and its operation. A machine definition language has been specified to let the user configure the toolkit for a particular machine. The concept of a "snapshot" object is introduced, as a way to capture simulated operations for both parallel and sequential processes. Finally, this paper presents an application of the toolkit in the optimization of SMT machines. Results are presented for the Universal GSM2, a gantry-type machine.

This paper introduces an optimizer for a new family of modular, multistation, walking beam, high-speed chip mounters. The objective is to optimize the machines in a manner that would streamline the use of nozzles and part feeder mechanisms and at the same time increase throughput. The optimization of these machines is a large NP-complete problem, and therefore, a heuristic search method is needed to solve the problem in reasonable time. Four knowledge-based systems are introduced to solve this problem. These systems were designed to emulate human experts, who have optimized these types of machines manually. Benchmarks were performed for 18 industrial test cases. The results show that overall, the knowledge-based systems outperformed software supplied by the vendor of the machine in both feeder slot savings and throughput. This performance represents a key improvement, and a prototype system has been implemented in our industrial partner's factory.

MOTIVATION: To determine the most powerful artificial intelligence techniques for automated restriction mapping, and use them to create a powerful multiple-enzyme restriction mapping tool. RESULTS: The most effective search engine utilized model-driven exhaustive search and a form of binary logic pruning based on Pratt's separation theory. Additional experimentation led to the development of an input preprocessing module which significantly speeds up searches, and an output post-processing module which enables users to analyze large solution sets and reduce their apparent complexity. AVAILABILITY: An executable version of the resultant tool, Mapper, can be downloaded from our Web site (http://www.ai.eecs.uic.edu) by selecting the 'Software' option. CONTACT: [email protected] (http://www.ai.eecs.uic.edu/ñelson).

This paper presents a method for improving heuristics using a triangulation technique. Instead of using a heuristic to directly estimate distance (X1, X2) between nodes X1 and X2, the proposed technique selects a reference node Ri applies the heuristic to (X1,Ri) and (X2,Ri), and uses the Euclidean distance formula to calculate a new heuristic value. If two nodes are close to each other, then they should also be approximately equidistant to a third reference node. Utilizing a set of many such reference nodes, node expansions can be reduced for a large class of heuristics. Very early results for this method, referred to as multi-dimensional heuristics, showed that fewer node expansions were needed when using the triangulation technique. New results in this paper include the development of a new learning procedure for selecting reference nodes, experimentation on reusing reference node sets for multiple goal instances, a comparison of multi-dimensional heuristics with weighting and how they dynamically weight states near the goal, and some observations which help explain how and why this technique improves heuristics.

Eight important restriction mapping programs, developed between 1978 and 1993, are analyzed and their performance evaluated. The analyses concentrate on the practical value of the programs to molecular biologists who do restriction mapping on a daily basis, rather than on theoretical efficiency. Although all of the programs could find maps consistent with the data, none were able to discriminate reliably the true map from the other consistent maps, given realistic levels of error. To alleviate this problem, we propose an expert mapping system which utilizes the same inferences and mapping techniques used by expert human mappers to reduce the number of false maps found.

The authors present and evaluate two algorithms-one linear and one logarithmic-for the computation of the restricted transitive closure of a binary database relation. The algorithms are implemented in a relational database management system (Ingres), and on equipment which is fairly common in today's database application environments. The performance evaluation reveals three important points. First, unlike the case of the complete transitive closure computations where the linear (seminaive) method is outperformed by the logarithmic methods, in the computation of the restricted transitive closure the opposite is true. Second, contrary to the popular belief that the algorithms run faster if the size of the intermediate result relations is decreased by deleting excess data, the fastest algorithms are those which attempt to delete no data. Unless deletions can be handled efficiently, their potential benefits are overshadowed by the cost incurred to perform them. Third, the operations union and difference are established as being significantly more expensive than the join operation in these algorithms.>