Configuring an evolutionary Algorithm (EA) can be a haphazard and inefficient process. An EA practitioner may have to choose between a plethora of search operator types and other parameter settings. In contrast, the g...
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ISBN:
(纸本)9781509042401
Configuring an evolutionary Algorithm (EA) can be a haphazard and inefficient process. An EA practitioner may have to choose between a plethora of search operator types and other parameter settings. In contrast, the goal of EA principled design is a more streamlined and systematic design methodology, which first seeks to better understand the problem domain, and only then uses such acquired insights to guide the choice of parameters and operators. We introduce a new approach to principled design of EAs based on kernel methods. Several popular machine learning and data analysis paradigms, which have been successfully applied to a wide range of difficult real world problems, would fall under this kernel umbrella. We demonstrate how kernel functions, which capture useful problem domain knowledge, can be used to directly construct EA search operators. We test two kernel search operators on a suite of four challenging combinatorial optimization problem domains. These novel kernel search operators exhibit superior performance to some traditional EA search operators.
Complex Event Processing (CEP) is an emerging technology to process streaming data and to generate response actions in real time. CEP systems treat all sensor data as primitive events and attempt to detect semanticall...
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ISBN:
(纸本)9780769556703
Complex Event Processing (CEP) is an emerging technology to process streaming data and to generate response actions in real time. CEP systems treat all sensor data as primitive events and attempt to detect semantically high level events and related actions by matching them using event patterns. These event patterns are the rules which combine primitive events according to temporal, logical, or spatial correlations among them. Although event patterns (decision rules) can be provided by experts in simplistic scenarios, the huge amount of sensor data makes this unfeasible. The main purpose of the underlying paper is replacing manual identification of event patterns. Considering the uncertainty related to the sensor data, Fuzzy Unordered Rule Induction Algorithm (FURIA) was implemented to identify event patterns after selecting the relevant feature subset using Elitist Pareto-based Multi Objective evolutionary Algorithm for Diversity Reinforcement (ENORA). The results were compared to the alternative machine learning approaches.
Nowadays, algorithmic studies of multi-objective evolutionary algorithms (MOEAs) are flooded with too many search algorithms. Each MOEA has its own expert problem domain. To clarify not only the optimal MOEA and its p...
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ISBN:
(纸本)9781509026784
Nowadays, algorithmic studies of multi-objective evolutionary algorithms (MOEAs) are flooded with too many search algorithms. Each MOEA has its own expert problem domain. To clarify not only the optimal MOEA and its parameters for each of multiple multi-objective optimization problems (MOPs) but the robust MOEAs for multiple MOPs, this work proposes a meta-MOEA framework to search the Pareto optimal algorithmic parameters for multiple MOPs. In this work, we use two DTLZ2 benchmark problems with 2 and 4 objectives and optimize the base algorithm, the crossover rate and its parameter, the mutation rate and its parameter for the both DTLZ2 problems by the meta-MOEA. The experiment results show that the optimal algorithmic parameters for each of two DTLZ2 problems are different and the robust algorithmic parameters for both problems can be obtained by the meta-MOEA framework.
The use of anti-virus software has become something of an act of faith. A recent study showed that more than 80% of all personal computers have anti-virus software installed. However, the protection mechanisms in plac...
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ISBN:
(纸本)9783319311531
The use of anti-virus software has become something of an act of faith. A recent study showed that more than 80% of all personal computers have anti-virus software installed. However, the protection mechanisms in place are far less effective than users would expect. Malware analysis is a classical example of cat-and-mouse game: as new antivirus techniques are developed, malware authors respond with new ones to thwart analysis. Every day, anti-virus companies analyze thousands of malware that has been collected through honeypots, hence they restrict the research to only already existing viruses. This article describes a novel method for malware obfuscation based an evolutionary opcode generator and a special ad-hoc packer. The results can be used by the security industry to test the ability of their system to react to malware mutations.
evolutionary algorithms (EAs) have been used in varying ways for design and other creative tasks. One of the main elements of these algorithms is the fitness function used by the algorithm to evaluate the quality of t...
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ISBN:
(纸本)9783319190907;9783319190891
evolutionary algorithms (EAs) have been used in varying ways for design and other creative tasks. One of the main elements of these algorithms is the fitness function used by the algorithm to evaluate the quality of the potential solutions it proposes. The fitness function ultimately represents domain knowledge that serves to bias, constrain, and guide the algorithm's search for an acceptable solution. In this paper, we explore the degree to which the fitness function's implementation affects the search process in an evolutionary algorithm. To perform this, the reliability and speed of the algorithm, as well as the quality of the designs produced by it, are measured for different fitness function implementations. These measurements are then compared and contrasted.
3D cutting and packing problems have important applications and are of particular relevance to the transportation of cargo in the form of Container Loading Problems (CLP). Many algorithms have been proposed for solvin...
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3D cutting and packing problems have important applications and are of particular relevance to the transportation of cargo in the form of Container Loading Problems (CLP). Many algorithms have been proposed for solving the 2D/3D cutting stock problems but most of them consider single objective optimization. The goal of the problem is to load the boxes that would provide the highest total volume and weight to the container, without exceeding the container limits. These two objectives are conflicting because the volume of a box is usually not proportional to its weight. This work deals with a multi-objective formulation of the CLP. We propose to apply multi-objective evolutionary algorithms in order to obtain a set of non-dominated solutions, from which the final users would choose the one to be definitely carried out. To apply evolutionary approaches we have defined a representation scheme for the candidate solutions, a set of evolutionary operators and a method to generate and evaluate the candidate solutions. The obtained results for generated instances on standard containers demonstrate the importance of the evaluation heuristic to be applied. (C) 2016 The Authors. Published by Elsevier B.V.
During the past decade, continuum topology optimization became an important industrial tool for the conceptual design of mechanical structures. The field of evolutionary computation provides suitable stochastic optimi...
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ISBN:
(纸本)9781509006229
During the past decade, continuum topology optimization became an important industrial tool for the conceptual design of mechanical structures. The field of evolutionary computation provides suitable stochastic optimization algorithms for problems involving strong non-linearities or black-box simulations, for which existing gradient-based methods are not feasible. Due to the high design freedom of the phenotypic space, the encoding of the structural design is a critical aspect when applying evolutionary algorithms. Currently, the encoding approaches are scattered throughout different literature fields. This paper gathers them and provides a contemporary overview on the various structural representations used in conjunction with evolutionary computation for topology optimization. The important influence of the representation on the scalability of the approaches motivates the proposed categorization in three groups: Grid, Geometric and Indirect Representations. The existing representations are described and discussed on a conceptual level and chances and challenges are outlined.
This paper investigates the use of a metaheuristic evolutionary algorithm. The algorithm, known as Bird Mating Optimizer (BMO), allows fault diagnosis and state-of-health estimation by comparing the battery parameters...
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ISBN:
(纸本)9781509009169
This paper investigates the use of a metaheuristic evolutionary algorithm. The algorithm, known as Bird Mating Optimizer (BMO), allows fault diagnosis and state-of-health estimation by comparing the battery parameters with the values for a brand new battery, in real time, which in turn allows the battery management system (BMS) to improve the energy management and eventually the lifetime of the battery. In this study, the equivalent-circuit model (ECM) parameters of a lead-acid battery are extracted from its voltage response using the BMO algorithm. The accuracy of the BMO method is then compared to traditional Least Square (LS) algorithm. The BMO extracted parameters showed close fit to the experimental data.
The advantages of evolutionary computation with very large populations for many-objective optimization problems are investigated. The effects of a population size of up to 1,000,000 are studied, with the number of gen...
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ISBN:
(纸本)9781509006229
The advantages of evolutionary computation with very large populations for many-objective optimization problems are investigated. The effects of a population size of up to 1,000,000 are studied, with the number of generations fixed at 100. To overcome difficulty in computational time, we use a many-objective evolutionary algorithm designed for massive parallelization (CHEETAH) on the K supercomputer. For unimodal test problems DTLZ2 and DTLZ4, the inverted generational distance (IGD) decreases as the population increases while the generational distance (GD) is saturated with a population size of 10,000. This means an evolutionary computation with massive population size mainly contributes to improvement of diversity of obtained non-dominated solutions. Even when the total number of evaluations is fixed, this conclusion is unchanged. For the multimodal test problems DTLZ1 and DTLZ3, GD and IGD are reduced with increasing population size of up to 10,000 but are not significantly improved with population sizes larger than this. This is probably due to the difficulty in obtaining good non-dominated solutions for DTLZ1 and DTLZ3 with current CHEETAH. Because CHEETAH is bases on NSGA-II (only the non-dominated sort portion is modified for more effective many-objective optimization and parallelization), we expect that the current conclusion qualitatively stays the same for other NSGA-II-based algorithms. To take advantage of the larger population size, development of operators such as selection and crossover designed for very large population size may be required.
In this paper, we proposed a two-phase many-objective evolutionary algorithm to tackle many objective optimization problems. In the first phase, the algorithm focuses on achieving good convergence towards the boundary...
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ISBN:
(纸本)9781509006229
In this paper, we proposed a two-phase many-objective evolutionary algorithm to tackle many objective optimization problems. In the first phase, the algorithm focuses on achieving good convergence towards the boundary Pareto optimal solutions. In the second phase, it maintains a good balance between convergence and diversity by using a set of widely spread reference lines. In addition, a penalty based adjustment for reference line has been adopted to handle many objective optimization problems with incomplete PFs. The performance of our proposed algorithm is validated and compared with four state-of-the-art many objective evolutionary algorithms on DTLZ problems. The results show that our proposed algorithm is very competitive with other compared algorithms.
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