Proposed Fitness Function Research Articles

Parallel testing and coverage analysis for context-free applications

Software testing being one of the major phases of the software development life cycle is critical in delivering reliable software products. Traditional manual GUI testing has severe limitations like insufficient test coverage, labor intensiveness, complexity involved and cost ineffectiveness. “How much testing is enough or sufficient?” still remains a challenging question. Coverage analysis helps to guide the test engineers regarding test coverage and is used extensively to determine the effectiveness of selected testing methodology. The problems mentioned above related to manual GUI testing prompted the need for automation of GUI testing and coverage analysis. With the rapid emergence of GUI based context free applications, automated testing tools seems less effective. Recently, it has been learnt that concurrent and distributed machines based cluster can be used to reduce the required effort to test GUI in context of time. Proposed system use two different evolutionary algorithms concurrently to gain multiple objectives, based on the fitness functions of maximization of GUI test path coverage (measured as the number of events in an event sequence tested by a particular test case) and simultaneously minimizing the number of test cases. For coverage analysis, two of the very well-known multi objective algorithms; NSGA II and MOPSO from evolutionary domain have been employed. Experiments results of coverage analysis show that accuracy to the tune of 85% was achieved in MOPSO and NSGA II. This high level of accuracy is an indicator of usefulness of proposed fitness function. Both algorithms execute more than 90% of test paths.

RST-BatMiner: A fuzzy rule miner integrating rough set feature selection and Bat optimization for detection of diabetes disease

Fuzzy classification rules are more interpretable and cope better with pervasive uncertainty and vagueness with respect to crisp rules. Because of this fact, fuzzy classification rules are extensively used in classification and decision support systems for disease diagnosis. But, most of the rule mining techniques failed to generate accurate and comprehensive fuzzy rules. This paper presents a hybrid decision support system based on Rough Set Theory (RST) and Bat optimization Algorithm (BA) called RST-BatMiner. It consists of two stages. In the first stage, redundant features have been removed from the data set through RST-based QUICK-REDUCT approach. In the second stage, for each class BA is invoked to generate fuzzy rules by minimizing proposed fitness function. Further, an Ada-Boosting technique is applied to the rules generated by BA to increase the accuracy rate of generated fuzzy rules. Moreover, to generate comprehensive fuzzy rules, a new ≠ (not equal) operator along with = (equal) operator is introduced into BA encoding scheme. The proposed RST-BatMiner builds consolidated fuzzy ruleset by learning the rules associated with each class separately. The proposed RST-BatMiner is experimented on six bench-mark datasets namely Pima Indians Diabetes, Wisconsin Breast Cancer, Cleveland Heart disease, iris, wine and glass, in order to validate its generalization capability. These experimental results show that except for wine dataset the proposed RST-BatMiner yields high accuracy and comprehensible ruleset when compared to other state-of-the-art bio-inspired based fuzzy rule miners and Fuzzy Rule Based Classification Systems (FRBCS) in the literature. In the case of wine dataset, the proposed RST-BatMiner yields second highest accuracy along with comprehensible ruleset.

Fractional lion optimization for cluster head-based routing protocol in wireless sensor network

Due to the hopeful application of gathering information from unreachable position, wireless sensor network creates an immense challenge for data routing to maximize the communication with more energy efficiency. In order to design the energy efficient routing, the optimization based clustering protocols are more preferred in wireless sensor network. In this paper, we have proposed competent optimization based algorithm called Fractional lion (FLION) clustering algorithm for creating the energy efficient routing path. Here, the proposed clustering algorithm is used to increase the energy and lifetime of the network nodes by selecting the rapid cluster head. In addition, we have proposed multi-objective FLION clustering algorithm to develop the new fitness function based on the five objectives like intra-cluster distance, inter-cluster distance, cluster head energy, normal nodes energy and delay. Here, the proposed fitness function is used to find the rapid cluster centroid for an efficient routing path. Finally, the performance of the proposed clustering algorithm is compared with the existing clustering algorithms such as low energy adaptive clustering hierarchy (LEACH), particle swarm optimization (PSO), artificial bee colony (ABC) and Fractional ABC clustering algorithm. The results proved that the lifetime of the wireless sensor nodes is maximized by the proposed FLION based multi-objective clustering algorithm as compared with existing protocols.

Particle morphology effects in random sequential adsorption

The properties of the random sequential adsorption of objects of various shapes on a two-dimensional triangular lattice are studied numerically by means of Monte Carlo simulations. The depositing objects are formed by self-avoiding lattice steps, whereby the size of the objects is gradually increased by wrapping the walks in several different ways. The aim of this work is to investigate the impact of the geometrical properties of the shapes on the jamming density θ_{J} and on the temporal evolution of the coverage fraction θ(t). Our results suggest that the order of symmetry axis of a shape exerts a decisive influence on adsorption kinetics near the jamming limit θ_{J}. The decay of probability for the insertion of a new particle onto a lattice is described in a broad range of the coverage θ by the product between the linear and the stretched exponential function for all examined objects. The corresponding fitting parameters are discussed within the context of the shape descriptors, such as rotational symmetry and the shape factor (parameter of nonsphericity) of the objects. Predictions following from our calculations suggest that the proposed fitting function for the insertion probability is consistent with the exponential approach of the coverage fraction θ(t) to the jamming limit θ_{J}.

Improved Genetic Algorithm to Reduce Mutation Testing Cost

Mutation testing is a fault-based testing technique that helps generating effective test cases. Mutation testing is computationally expensive, because it requires executing hundreds and even thousands of mutants. In this situation, search-based approaches like genetic algorithm can help to automate test case generation to reduce the cost. In this paper, we present an improved genetic algorithm that can reduce computational cost of mutation testing. First, we present a novel state-based and control-oriented fitness function that efficiently uses object-oriented program features to evaluate a test case. We then empirically evaluate it using our implemented tool, eMuJava, and compare it with the standard fitness function. Results show that although our proposed fitness function provides detailed information about fitness of a test case but standard genetic algorithm is incapable of using that effectively to repair the test cases. Therefore, we propose a new two-way crossover and adaptable mutation methods that intelligently use the fitness information to generate fitter offspring. Finally, we compare the improved genetic algorithm with random testing, standard genetic algorithm, and EvoSuite. Experiment results prove that our proposed approach can find the optimal test cases in less number of attempts (reduces computational cost). Besides that it can detect software bugs from suspiciously equivalent mutants and these mutants eventually get killed (increases mutation score).

A quality guaranteed robust image watermarking optimization with Artificial Bee Colony

Achieving robustness with a limited distortion level is a challenging design problem for watermarking systems in multimedia applications with a guaranteed quality requirement. In this paper, we provide an intelligent system for watermarking through incorporating a meta-heuristic technique along with an embedding method to achieve an optimized performance. The optimization objective is to provide the maximum possible robustness without exceeding a predetermined distortion limit. Hence, the quality level of the watermarking method could be guaranteed through that constraint optimization. A new fitness function is defined to provide the required convergence toward the optimum solution for the defined optimization problem. The fitness function is based on dividing its applied solution population into two groups, where each group is ranked according to a different objective. Thus, the multi-objectives in the problem are decoupled and solved through two single-objective sub-problems. Unlike existing watermarking optimization techniques, the proposed work does not require weighting factors. To illustrate the effectiveness of the proposed approach, we employ a recent watermarking technique, and then use it as the embedding method to be optimized. The Artificial Bee Colony is selected as the meta-heuristic optimization method in which the proposed fitness function is used. Experimental results show that the imposed quality constraint is satisfied, and that the proposed method provides enhanced robustness under different attacks for various quality thresholds. The presented approach offers a robust solution that can be applied to numerous multimedia applications such as film industry, intelligent surveillance and security systems.

The privacy preserving method for dynamic trajectory releasing based on adaptive clustering

The traditional trajectory privacy preserving methods are static releasing methods, only focusing on the trajectories in a specific time span. Therefore, the traditional methods need to recalculate the last released results, in dealing with dynamic trajectory releasing issues as the new time slice arriving. That would increase the computing costs of dynamic trajectory releasing. Furthermore, the static methods treat the trajectory as the basic unit of clustering, and that is a coarse granularity unit. For there exist the different parts among the trajectories, the formed k equivalence classes would have a large trajectory generalization area, severely reducing the availability of the released trajectories. Thus, the dynamic trajectory releasing method based on the adaptive clustering is proposed. This method utilizes the designed Gibbs sampling-cluster method to detect the RR (Representative Region). By which the issue of unaligned sampling time caused by the different moving speed and the sampling frequency can be resolved. And the detected RRs are treated as the critical region to be protected. In this paper, we utilize the proposed fitness function, which could evaluate the rationality of the equivalence classes, to cluster the RRs into GR (Generalization Region), and guarantee the trajectory segments between GRs k-anonymity. The adaptive adjustment policy is adopted in the RR and GR detection process. The adjustment process of RR and GR could effectively reduce the computing cost in trajectory releasing, by considering the relevance between the successive time slices. By the experimental comparison, the effectiveness and rationality of the proposed method and is verified.

Reliability-redundancy-location allocation with maximum reliability and minimum cost using search techniques

ContextA safety critical system requires an automated and optimal allocation of redundant component instances to its existing components, including: 1) the selection of components (locations) on which the redundancy must be applied, 2) how many redundant component instances of varying reliability and cost should be allocated to each selected location. ObjectiveOur work aims to searching for the near optimal allocation solutions achieving the higher reliability of the system within the allowed cost. Such allocation must be made earlier, for example, while designing the architecture of the system to avoid unnecessary complexity of addressing unsafe situations discovered in the system development and deployment phases. MethodWith the above objective in mind, we propose a search-based allocation approach based on the overall objectives of maximizing the overall system reliability and minimizing the cost of introducing and allocating redundancy structures to the system. The architecture of a system modeled using the Unified Modeling Language (UML) along with redundancy structures is encoded as an optimization problem. To guide a search algorithm to solve the problem, we propose a fitness function based on the two optimization objectives: high reliability and low cost. ResultsWe empirically evaluated the performance of four search algorithms (Genetic Algorithm, (1 + 1) Evolutionary Algorithm, Alternating Variable Method (AVM) and Random Search) together with the proposed fitness function on 10 real-world Subsea Oil&Gas Production Systems of varying complexity. Results show that the AVM algorithm significantly outperforms the rest. ConclusionBased on the results of empirical evaluation, we found that AVM can provide the best allocation of redundancy structures as compared to the rest of the algorithms. On average, AVM provided 0.008% of more reliability while saving 26.78% on allocation cost as compared to RS. Our novel solution based on the results of empirical evaluation is implemented as a software tool.

Optimal sensor placement for maximum area coverage (MAC) for damage localization in composite structures

In this paper an optimal sensor placement algorithm for attaining the maximum area coverage (MAC) within a sensor network is presented. The proposed novel approach takes into account physical properties of Lamb wave propagation (attenuation profile, direction dependant group velocity due to material anisotropy) and geometrical complexities (boundary reflections, presence of openings) of the structure. A feature of the proposed optimization approach lies in the fact that it is independent of characteristics of the damage detection algorithm (e.g. probability of detection) making it readily up-scalable to large complex composite structures such as aircraft stiffened composite panel. The proposed fitness function (MAC) is independent of damage parameters (type, severity, location). Statistical analysis carried out shows that the proposed optimum sensor network with MAC results in high probability of damage localization. Genetic algorithm is coupled with the fitness function to provide an efficient optimization strategy.

A Computational Intelligence Method for Effective Diagnosis of Heart Disease using Genetic Algorithm

In recent years improvement of new and effective medical domain applications has vital role in research. Computational Intelligence Systems (CIS) has profound influence in the enlargement of these effective medical field applications and tools. One of the prevalent diseases that world facing is heart disease. The Computational Intelligence Systems uses input clinical data from different knowledge resources throughout the world and applies this data on different computational intelligence tools that uses sophisticated algorithms. The sophisticated algorithms plays prominent role in the construction of medical clinical analysis tools. These tools may be used as an extra aid for the clinical diagnosis of the diseases for the doctors and clinicians. In this paper a novel method for the diagnosis of heart disease has been proposed using Genetic Algorithms. In this approach an effective association rules are inferred using Genetic Algorithm approach which uses tournament selection, crossover, mutation and new proposed fitness function. The Cleaveland data set is used for the experimentation. This data set is collected from the UCI machine learning repository experimental results are prominent when compared with some of the supervised learning techniques.

Zen-ReqOptimizer: a search-based approach for requirements assignment optimization

At early phases of a product development lifecycle of large scale Cyber-Physical Systems (CPSs), a large number of requirements need to be assigned to stakeholders from different organizations or departments of the same organization for review, clarification and checking their conformance to standards and regulations. These requirements have various characteristics such as extents of importance to the organization, complexity, and dependencies between each other, thereby requiring different effort (workload) to review and clarify. While working with our industrial partners in the domain of CPSs, we discovered an optimization problem, where an optimal solution is required for assigning requirements to various stakeholders by maximizing their familiarity to assigned requirements, meanwhile balancing the overall workload of each stakeholder. In this direction, we propose a fitness function that takes into account all the above-mentioned factors to guide a search algorithm to find an optimal solution. As a pilot experiment, we first investigated four commonly applied search algorithms (i.e., GA, (1 + 1) EA, AVM, RS) together with the proposed fitness function and results show that (1 + 1) EA performs significantly better than the other algorithms. Since our optimization problem is multi-objective, we further empirically evaluated the performance of the fitness function with six multi-objective search algorithms (CellDE, MOCell, NSGA-II, PAES, SMPSO, SPEA2) together with (1 + 1) EA (the best in the pilot study) and RS (as the baseline) in terms of finding an optimal solution using an real-world case study and 120 artificial problems of varying complexity. Results show that both for the real-world case study and the artificial problems (1 + 1) EA achieved the best performance for each single objective and NSGA-II achieved the best performance for the overall fitness. NSGA-II has the ability to solve a wide range of problems without having their performance degraded significantly and (1 + 1) EA is not fit for problems with less than 250 requirements Therefore we recommend that, if a project manager is interested in a particular objective then (1 + 1) EA should be used; otherwise, NSGA-II should be applied to obtain optimal solutions when putting the overall fitness as the first priority.

Quality metric‐based fitness function for robust watermarking optimisation with Bees algorithm

The design of a robust watermarking technique has been always suffering from the conflict between the watermark robustness and the quality of the watermarked image. In this study, the embedding strength parameters for per-block image watermarking in the discrete cosine transform (DCT) domain are optimised. A fitness function is proposed to best suit the optimisation problem. The optimum solution is selected based on the quality and the robustness achieved using that solution. For a given image block, the peak-signal-to-noise ratio (PSNR) is used as a quality metric to measure the imperceptibility for the watermarked block. However, the robustness cannot be measured for a single watermark bit using traditional metrics. The proposed method uses the PSNR quality metric to indicate the degree of robustness. Hence, optimum embedding in terms of quality and robustness can be achieved. To demonstrate the effectiveness of the proposed approach, a recent watermarking technique is modified, and then used as the embedding method to be optimised. The Bees algorithm is selected as the optimisation method and the proposed fitness function is applied. Experimental results show that the proposed method provides enhanced imperceptibility and robustness under different attacks.

Modular structure of brain functional networks: breaking the resolution limit by Surprise.

The modular organization of brain networks has been widely investigated using graph theoretical approaches. Recently, it has been demonstrated that graph partitioning methods based on the maximization of global fitness functions, like Newman’s Modularity, suffer from a resolution limit, as they fail to detect modules that are smaller than a scale determined by the size of the entire network. Here we explore the effects of this limitation on the study of brain connectivity networks. We demonstrate that the resolution limit prevents detection of important details of the brain modular structure, thus hampering the ability to appreciate differences between networks and to assess the topological roles of nodes. We show that Surprise, a recently proposed fitness function based on probability theory, does not suffer from these limitations. Surprise maximization in brain co-activation and functional connectivity resting state networks reveals the presence of a rich structure of heterogeneously distributed modules, and differences in networks’ partitions that are undetectable by resolution-limited methods. Moreover, Surprise leads to a more accurate identification of the network’s connector hubs, the elements that integrate the brain modules into a cohesive structure.

Flujo Óptimo Reactivo para Minimización de Pérdidas: una Nueva Propuesta de Función de Adaptación y Manejo de Restricciones

This work presents a fitness function and constraint handling approach applied to the problem of optimal reactive power dispatch. This problem aims at the reduction of power losses by deciding on voltage setpoints of generators, transformers taps and switchable capacitor and reactor banks. The fitness function consist of a product of sub-functions that represent permissible limits on system variables and that also includes a specific goal on power loss reduction. The main advantage of the proposed fitness function is the fact that the global optimum of the problem is known beforehand. A genetic algorithm was used to solve the ORPD with both fitness functions. This fitness function was implemented and compared with a traditional fitness function by means of a genetic algorithm for three study IEEE cases: 57, 118 and 300 bus systems. It was found that the proposed fitness function showed better performance than the traditional one, guaranteeing the system operation within the feasible region.

Multi-level fractional order PSO new paradigm algorithm for image segmentation

Nowadays, the resolution of the image characterisation problem is a very active and developed research field. Various applications areas were processed such as robotics and autonomous systems, computer vision, map processing and medical imaging. Pre-processing, segmentation, recognition and classification are the main areas of study. Multilevel segmentation of Benchmark images is our application field in this study. It is used to separate the original image into regions with common characteristics of an interesting viewing quality and a fast time execution. The purpose is to automatically determine the optimal threshold values based between-class variance maximisation. The choice of the already used method essentially depends on the quantitative characteristics. We note the optimal threshold values, the minimum CPU processing time and the stability of the proposed fitness function. Likewise, qualitatively results are required. The principle aim of this paper is to propose a new PSO algorithm for multilevel segmentation based on a novel fitness function and modified inertia component to find the optimal thresholds. Experimental results applied on a set of benchmarks images have been proven efficiencies and advantages in multilevel compared to other metaheuristics such as Genetic Algorithms (GA), Otsu method, Conventional PSO and Fractional-Order Darwinian PSO (DPSO).

Quality Enhancement of AC Chopper Using Genetic Algorithm

This paper mainly deals with the design of AC chopper using Genetic Algorithm based harmonic elimination technique. Genetic Algorithm is used to calculate optimum switching angles to eliminate lower order harmonics in the output voltage. Total Harmonic Distortion of output voltage is calculated from the obtained switching angles and also adopted in the proposed fitness function. Comparative analysis is made for the switching angles obtained by the Newton Raphson method and the proposed Genetic Algorithm. The analysis reveals that the proposed technique is on par with conventional method. Additionally, the Genetic Algorithm approach offers less computational burden, guaranteed global optima in most cases and faster convergence. The proposed method is simulated in Matlab/Simulink model and the results shows that the proposed method works with high effectiveness, accuracy and rapidity.

Quality Enhancement of AC Chopper Using Genetic Algorithm

This paper mainly deals with the design of AC chopper using Genetic Algorithm based harmonic elimination technique. Genetic Algorithm is used to calculate optimum switching angles to eliminate lower order harmonics in the output voltage. Total Harmonic Distortion of output voltage is calculated from the obtained switching angles and also adopted in the proposed fitness function. Comparative analysis is made for the switching angles obtained by the Newton Raphson method and the proposed Genetic Algorithm. The analysis reveals that the proposed technique is on par with conventional method. Additionally, the Genetic Algorithm approach offers less computational burden, guaranteed global optima in most cases and faster convergence. The proposed method is simulated in Matlab/Simulink model and the results shows that the proposed method works with high effectiveness, accuracy and rapidity.

Optimal Sensor Placement for Damage Detection Based on Ultrasonic Guided Wave

In this work a methodology for effective positioning of sensors and actuators for damage detection and characterisation is described. The novelty of the proposed methodology is that the fitness function to be optimised does not contain probability of detection (POD) which needs to be obtained for every possible sensor combination. The proposed fitness function is to provide the maximum coverage of the structure via Lamb waves and reduce the negative effects of boundary reflections. Once the fitness function is defines, genetic algorithm (GA) is used as an optimisation strategy to result in optimal sensor positioning.

A swarm intelligent method for traffic light scheduling: application to real urban traffic networks

Traffic lights play an important role nowadays for solving complex and serious urban traffic problems. How to optimize the schedule of hundreds of traffic lights has become a challenging and exciting problem. This paper proposes an inner and outer cellular automaton mechanism combined with particle swarm optimization (IOCA-PSO) method to achieve a dynamic and real-time optimization scheduling of urban traffic lights. The IOCA-PSO method includes the inner cellular model (ICM), the outer cellular model (OCM), and the fitness function. Our work can be divided into following parts: (1) Concise basic transition rules and affiliated transition rules are proposed in ICM, which can help the proposed phase cycle planning (PCP) algorithm achieve a globally sophisticated scheduling and offer effective solutions for different traffic problems; (2) Benefited from the combination of cellular automaton (CA) and particle swarm optimization (PSO), the proposed inner and outer cellular PSO (IOPSO) algorithm in OCM offers a strong search ability to find out the optimal timing control; (3) The proposed fitness function can evaluate and conduct the optimization of traffic lights' scheduling dynamically for different aims by adjusting parameters. Extensive experiments show that, compared with the PSO method, the genetic algorithm method and the RANDOM method in real cases, IOCA-PSO presents distinct improvements under different traffic conditions, which shows a high adaptability of the proposed method in urban traffic network scales under different traffic flow states, intersection numbers, and vehicle numbers.

A Simple Fitness Function for Minimum Attribute Reduction.

The goal of minimal attribute reduction is to find the minimal subset R of the condition attribute set C such that R has the same classification quality as C. This problem is well known to be NP-hard. When only one minimal attribute reduction is required, it was transformed into a nonlinearly constrained combinatorial optimization problem over a Boolean space and some heuristic search approaches were used. In this case, the fitness function is one of the keys of this problem. It required that the fitness function must satisfy the equivalence between the optimal solution and the minimal attribute reduction. Unfortunately, the existing fitness functions either do not meet the equivalence, or are too complicated. In this paper, a simple and better fitness function based on positive domain was given. Theoretical proof shows that the optimal solution is equivalent to minimal attribute reduction. Experimental results show that the proposed fitness function is better than the existing fitness function for each algorithm in test.