evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of natural selection. In this paper we briefly int...
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evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of natural selection. In this paper we briefly introduce the main concepts behind evolutionary computing. We present the main components all evolutionary algorithms (EAs), sketch the differences between different types of EAs and survey application areas ranging from optimization, modeling and simulation to entertainment. (C) 2002 Published by Elsevier Science B.V.
Machine learning has been widely applied to malware detection tasks;but unfortunately, they exhibit significant vulnerability to adversarial attacks and can be easily circumvented using perturbation carefully crafted....
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Machine learning has been widely applied to malware detection tasks;but unfortunately, they exhibit significant vulnerability to adversarial attacks and can be easily circumvented using perturbation carefully crafted. Concurrently, we are witnessing a corresponding increase in the attention dedicated to adversarial attacks against malware detection models. Nevertheless, current research on adversarial examples still faces obstacles such as poor escape effectiveness and difficulty in preserving functionality. Particularly, greedily recruiting the best manipulations from a vast search space often leads to poor diversity of adversarial perturbation sequence. To rectify these shortcomings, this paper proposes an automated, continuously optimized approach for generating malware adversarial examples based on evolutionary computing. Our method filters effective action sequences from a large pool of random manipulations, assigning different priorities to different actions. The generation and optimization of adversarial examples are formalized as a sparse minimization optimization problem based on a fixed-length action vector. We introduce AOP-Mal, a novel genetic framework to automatically generate and optimize adversarial examples. The initialization and evolution of the population depend on the priority of actions, as well as the proposed novel evolutionary operator. The experimental results demonstrate that our attack strategy effectively bypasses the detection mechanisms and outperforms most state-of-the-art malware adversarial frameworks. Our hope is to help researchers understand the intentions of attackers and explore more powerful defense mechanisms.
There are diverse parameters available for measuring performance of an academic institute. Graduation rate of an institute is an important indicator of institute's success. It is essential to understand which fact...
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There are diverse parameters available for measuring performance of an academic institute. Graduation rate of an institute is an important indicator of institute’s success. It is essential to understand which factors...
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There are diverse parameters available for measuring performance of an academic institute. Graduation rate of an institute is an important indicator of institute’s success. It is essential to understand which factors lead to better graduation rates. Hence, a prediction system which helps institutes well in advance to avoid poor graduation rate is required. In this study, a novel adaptive dimensionality reduction model is proposed using evolutionary computing and machine learning to better predict institute graduation rate. This work has explored the feature optimization capacity of evolutionary algorithm with weight assignment approach to each dimension. A high dimensional dataset is considered for analyzing attributes that affect institute graduation rates. Proposed model uses adaptive approach of incrementing weights of contributing features which lead to minimum error. Experimental results show that proposed model yields optimum dimensions, low execution time and minimum error. Predictive analysis presented could lead to useful future directions for education domain stakeholders.
Traditional methods often employed to solve complex real world problems tend to inhibit elaborate exploration of the search space. They can be expensive and often results in sub-optimal solutions. evolutionary computa...
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Traditional methods often employed to solve complex real world problems tend to inhibit elaborate exploration of the search space. They can be expensive and often results in sub-optimal solutions. evolutionary computation (EC) is generating considerable interest for solving real world engineering problems. They are proving robust in delivering global optimal solutions and helping to resolve limitations encountered in traditional methods. EC harnesses the power of natural selection to turn computers into optimisation tools. The core methodologies of EC are genetic algorithms (GA), evolutionary programming (EP), evolution strategies (ES) and genetic programming (GP). This paper attempts to bridge the gap between theory and practice by exploring characteristics of real world problems and by surveying recent EC applications for solving real world problems in the manufacturing industry. The survey outlines the current status and trends of EC applications in manufacturing industry. For each application domain, the paper describes the general domain problem, common issues, current trends, and the improvements generated by adopting the GA strategy. The paper concludes with an outline of inhibitors to industrial applications of optimisation algorithms. (C) 2004 Elsevier B.V. All rights reserved.
Customer relationship management (CRM) is a customer-centric business strategy which a company employs to improve customer experience and satisfaction by customizing products and services to customers' needs. This...
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Customer relationship management (CRM) is a customer-centric business strategy which a company employs to improve customer experience and satisfaction by customizing products and services to customers' needs. This strategy, when implemented in totality eventually increases the revenue of the company. Traditionally, data mining (DM) techniques have been applied to solve various analytical CRM tasks. In turn, optimization techniques have long been used for training some of the DM techniques. However, during the past few years, evolutionary techniques have become so powerful and versatile that they can be deployed as a substitute for some DM techniques. This trend caught the attention of the researchers working in the analytical CRM area as they too started solving the CRM tasks using evolutionary techniques alone. In this context, we present a survey of evolutionary computing techniques applied to CRM tasks. In this paper, we surveyed 78 papers that were published during 1998 and 2015, where the application of evolutionary computing (EC) techniques to analytical CRM tasks is the main focus. The survey includes papers involving evolutionary computing techniques applied to the analytical CRM tasks under single-as well as multi-objective optimization framework. The purpose of the survey is to let the reader realize the versatility and power of EC techniques in solving analytical CRM tasks in the service industry and suggesting future directions. (C) 2016 Elsevier Ltd. All rights reserved.
evolutionary computing techniques, including genetic algorithms (GA), particle swarm optimization (PSO) and ants system (AS) are applied to the localization problem of a mobile robot. Salient features of robot localiz...
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evolutionary computing techniques, including genetic algorithms (GA), particle swarm optimization (PSO) and ants system (AS) are applied to the localization problem of a mobile robot. Salient features of robot localization are that the system is partially dynamic and information for fitness evaluation is incomplete and corrupted by noise. In this research, variations to the above three evolutionary computing techniques are proposed to tackle the specific dynamic and noisy system. Their performances are compared based on simulation and experiment results and the feasibility of the proposed approach to mobile robot localization is demonstrated. (c) 2006 Elsevier Ltd. All rights reserved.
Craniofacial reconstruction is one of the dominating research domains having vital significance towards forensic purposes as well as archaeological investigation needs. With a goal to enable an error-resilient and swi...
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Craniofacial reconstruction is one of the dominating research domains having vital significance towards forensic purposes as well as archaeological investigation needs. With a goal to enable an error-resilient and swift craniofacial reconstruction model, in this paper an evolutionary computing assisted enhanced regression model has been proposed. We have developed an enhanced over-fitting resilient regression model called Ridge Regression (RR) as a statistical method to perform craniofacial reconstruction using landmark points and skull-face/(tissue) skin features. Our proposed model incorporates a hybrid evolutionary computing scheme containing Particle Swarm Optimization (PSO) and Differential Evolution (DE) to perform feature point selection and landmark count reduction. Here, the prime objective is to reduce the feature sets and landmark that can eventually make craniofacial reduction process more time efficient and accurate. The performance assessment reveals that the proposed PSO-DEFS based RRM model outperforms existing approaches such as the least square support vector regression (LSSVR) and partial least square regression (PLSR).
Particle filters constitute themselves a highly powerful estimation tool, especially when dealing with non-linear non-Gaussian systems. However, traditional approaches present several limitations, which reduce signifi...
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Particle filters constitute themselves a highly powerful estimation tool, especially when dealing with non-linear non-Gaussian systems. However, traditional approaches present several limitations, which reduce significantly their performance. evolutionary algorithms, and more specifically their optimization capabilities, may be used in order to overcome particle-filtering weaknesses. In this paper, a novel FPGA-based particle filter that takes advantage of evolutionary computation in order to estimate motion patterns is presented. The evolutionary algorithm, which has been included inside the resampling stage, mitigates the known sample impoverishment phenomenon, very common in particle-filtering systems. In addition, a hybrid mutation technique using two different mutation operators, each of them with a specific purpose, is proposed in order to enhance estimation results and make a more robust system. Moreover, implementing the proposed evolutionary Particle Filter as a hardware accelerator has led to faster processing times than different software implementations of the same algorithm.
One of the major challenges in medical domain is the extraction of comprehensible knowledge from medical diagnosis data. In this paper, a two-phase hybrid evolutionary classification technique is proposed to extract c...
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One of the major challenges in medical domain is the extraction of comprehensible knowledge from medical diagnosis data. In this paper, a two-phase hybrid evolutionary classification technique is proposed to extract classification rules that can be used in clinical practice for better understanding and prevention of unwanted medical events. In the first phase, a hybrid evolutionary algorithm (EA) is utilized to confine the search space by evolving a pool of good candidate rules, e.g. genetic programming (GP) is applied to evolve nominal attributes for free structured rules and genetic algorithm (GA) is used to optimize the numeric attributes for concise classification rules without the need of discretization. These candidate rules are then used in the second phase to optimize the order and number of rules in the evolution for forming accurate and comprehensible rule sets. The proposed evolutionary classifier (EvoC) is validated upon hepatitis and breast cancer datasets obtained from the UCI machine-learning repository. Simulation results show that the evolutionary classifier produces comprehensible rules and good classification accuracy for the medical datasets. Results obtained from t-tests further justify its robustness and invariance to random partition of datasets. (C) 2003 Elsevier Science B.V. All rights reserved.
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