Evolutionary Algorithm-based Approach Research Articles

An evolutionary algorithm-based approach for efficient design of Wiener spline non-linear adaptive filter and its experimental validation

An evolutionary algorithm-based approach for efficient design of Wiener spline non-linear adaptive filter and its experimental validation

Evolutionary multitasking cooperative transfer for multiobjective hyperspectral sparse unmixing

Evolutionary multiobjective optimization is vigorous but not efficient in solving the hyperspectral sparse unmixing problem, while most related algorithms suffer from high computational complexity. This limitation becomes more pronounced in dealing with multiple complex hyperspectral sparse unmixing tasks (HSUTs) concurrently. In fact, different HSUTs are interrelated on spectral libraries and search spaces. However, there is currently no evolutionary algorithm-based approach that explores the cooperativity of multiple HSUTs. Taking advantage of this opportunity, we propose a novel evolutionary multitasking cooperative transfer (EMCT) framework for multiobjective hyperspectral sparse unmixing in this paper. EMCT can handle different HSUTs simultaneously with a uniformly coded population, and discover the correlation between different tasks to promote their convergence. To overcome the issue of unequal dimensions and non-corresponding spectra in the spectral libraries utilized by different tasks, we propose a novel library alignment strategy to alleviate the multitasking negative transfer. In addition, we design novel initialization strategy and genetic operators to improve the population diversity and the efficiency of population evolution between different HSUTs. We utilize the reconstruction error of every endmember in the spectral library for each task as prior knowledge to evenly distribute the population within the sparsity interval to be explored. In initialization and population evolution, EMCT prefers those decision variables with lower error scores, guiding the population to explore more probable active endmember sets. On the three most commonly used simulated datasets and one real dataset, EMCT achieves state-of-the-art (SOTA) or competitive experimental results. Furthermore, the potential of the EMCT framework is also demonstrated in combination with other multiobjective sparse unmixing algorithms and evolutionary multitasking explicit transfer strategy.

Novel Multivariable Evolutionary Algorithm-Based Method for Modal Reconstruction of the Corneal Surface from Sparse and Incomplete Point Clouds.

Three-dimensional reconstruction of the corneal surface provides a powerful tool for managing corneal diseases. This study proposes a novel method for reconstructing the corneal surface from elevation point clouds, using modal schemes capable of reproducing corneal shapes using surface polynomial functions. The multivariable polynomial fitting was performed using a non-dominated sorting multivariable genetic algorithm (NS-MVGA). Standard reconstruction methods using least-squares discrete fitting (LSQ) and sequential quadratic programming (SQP) were compared with the evolutionary algorithm-based approach. The study included 270 corneal surfaces of 135 eyes of 102 patients (ages 11-63) sorted in two groups: control (66 eyes of 33 patients) and keratoconus (KC) (69 eyes of 69 patients). Tomographic information (Sirius, Costruzione Strumenti Oftalmici, Italy) was processed using Matlab. The goodness of fit for each method was evaluated using mean squared error (MSE), measured at the same nodes where the elevation data were collected. Polynomial fitting based on NS-MVGA improves MSE values by 86% compared to LSQ-based methods in healthy patients. Moreover, this new method improves aberrated surface reconstruction by an average value of 56% if compared with LSQ-based methods in keratoconus patients. Finally, significant improvements were also found in morpho-geometric parameters, such as asphericity and corneal curvature radii.

EVOLUTIONARY ALGORITHM-BASED PARETO FRONT EXPLORATION FOR EFFICIENT COST-PERFORMANCE TRADEOFFS IN BIG DATA ANALYTICS

Big data analytics often involves complex decision-making processes that require finding efficient cost-performance tradeoffs. Evolutionary algorithms (EAs) have proven to be effective in solving multi-objective optimization problems by exploring the Pareto front, which represents the optimal tradeoffs between conflicting objectives. In this paper, we propose an evolutionary algorithm-based approach for Pareto front exploration in big data analytics. Our approach employs a novel fitness function that incorporates both cost and performance metrics, allowing the algorithm to simultaneously optimize for both objectives. We introduce several mutation and crossover operators tailored for big data analytics, ensuring effective exploration of the solution space. To validate the effectiveness of our approach, we conduct experiments using real-world big data analytics scenarios. The results demonstrate that our evolutionary algorithm-based approach successfully explores the Pareto front, enabling decision-makers to identify optimal cost-performance tradeoffs in big data analytics.

Evolutionary Algorithm-based Approach for Multi-Objective Optimization of a Complex Reliability System

Dealing with the conflicting objectives in reliability analysis of complex engineering systems is always a challenging task. Here, we have taken two conflicting objectives namely reducing cost and increasing the reliability of a complex reliability system named life support system in space capsule (LS3C) into consideration. A novel multi-objective evolutionary algorithm named MOPSO-CD has been employed to get various Pareto Optimal Fronts (POFs) in accordance with different parameter tuning. The simulation results so obtained provide a wide range of varieties of POFs to decision maker (DM).

An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-II

In computer vision, convolutional neural networks have a wide range of uses. Images represent most of today’s data, so it’s important to know how to handle these large amounts of data efficiently. Convolutional neural networks have been shown to solve image processing problems effectively. However, when designing the network structure for a particular problem, you need to adjust the hyperparameters for higher accuracy. This technique is time consuming and requires a lot of work and domain knowledge. Designing a convolutional neural network architecture is a classic NP-hard optimization challenge. On the other hand, different datasets require different combinations of models or hyperparameters, which can be time consuming and inconvenient. Various approaches have been proposed to overcome this problem, such as grid search limited to low-dimensional space and queuing by random selection. To address this issue, we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks (CNNs) using optimized hyperparameters. This study proposes a method using Non-dominated sorted genetic algorithms (NSGA) to improve the hyperparameters of the CNN model. In addition, different types and parameter ranges of existing genetic algorithms are used. A comparative study was conducted with various state-of-the-art methodologies and algorithms. Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy, and the results are published in modern computing literature.

Binary Particle Swarm Optimization with an improved genetic algorithm to solve multi-document text summarization problem of Hindi documents

Automatic text summarization plays a vital role in text retrieval. However, very little interest, as well as attention to Hindi text summarization and the wide use of hybridization of an evolutionary and swarm-based method for solving optimization problems, motivates us to develop a technique for achieving better results. We have used a combination of the Title feature, Sentence length, Sentence position, Numerical Data, Thematic word, Proper Noun and Term frequency & Inverse Sentence Frequency to find the results. The proposed BPSO-IGA comprising Binary Particle Swarm Optimization (BPSO) and an improved genetic algorithm finds a well-formatted summary from multiple documents. The method for calculating the score of the sentence is proposed initially. A combination of BPSO and IGA finds the features’ optimal scores and then is used to generate the final summary. The proposed method is compared with six well-known techniques, SummaRuNNer, ChunkRank, TGraph, UniRank, FF-SE, and MDS-ACS, on FIRE 2011 Hindi dataset. To strengthen the hypothesis that all seven features are required in the summary generation, the summary is also generated using only ONE and a combination of more than one feature. It is found that the best summary is generated when all seven features are considered. The BPSO-IGA performs best among all the methods for precision, recall, f-measure, cohesion, and non-redundancy. This research aims to design a multi-document extractive text summarizer for Hindi documents using a swarm intelligence and evolutionary algorithm-based hybrid approach.

Placing an ensemble of pressure sensors for leak detection in water distribution networks under measurement uncertainty

AbstractLarge volumes of water are wasted through leakage in water distribution networks, and early detection of leakages is important to minimize lost water. Pressure sensors can be placed in a network to detect changes in pressure that indicate the presence of a new leak. This study presents a new approach for placing a set of pressure sensors by creating a list of candidate locations based on sensitivity to leaks that are simulated at all potential nodes in a network. The selection of a set of sensors is explored for two objectives, which are the minimization of the number of sensors and the time of detection. The non-dominated sorting genetic algorithm (NSGA-II) is used to explore trade-offs between these objectives. The effect of measurement uncertainty on the selection of sensor locations is explored by identifying alternative non-dominated fronts for different values for sensor error. The evolutionary algorithm-based approach is applied and demonstrated for the C-Town water network.

Parallel Evolutionary Algorithm for Designing Water Distribution Networks to Minimize Background Leakage

AbstractLeaks in water distribution systems waste energy and water resources, increase damage to infrastructure, and may allow contamination of potable water. This research develops an evolutionary algorithm-based approach to minimize the cost of water loss, new infrastructure, and operations that reduce background leakage. A new design approach is introduced that minimizes capital and operational costs, including energy and water loss costs. Design decisions identify a combination of infrastructure improvements, including pipe replacement and valve installment, and operation rules for tanks and pumps. Solution approaches are developed to solve both a single-objective and multiobjective problem formulation. A genetic algorithm and a nondominated sorting genetic algorithm are implemented within a high-performance computing platform to select tank sizes, pump placement and operations, placement of pressure-reducing valves, and pipe diameters for replacing pipes. The evolutionary algorithm approaches identif...

System-level synthesis of multi-ASIP platforms using an uncertainty model

In this paper we propose a system-level synthesis for MPSoCs that integrates multiple Application Specific Instruction Set Processors (ASIPs). Each ASIP is customized for a specific set of tasks. The system-level synthesis is responsible for assigning the tasks to the ASIPs, exploring different platform alternatives. We can allocate tasks to the different ASIPs and determine if the applications are schedulable only knowing the worst-case execution time (WCET) of each task. We can estimate the WCET only after establishing the micro-architecture of the ASIP. At the same time, an ASIP micro-architecture can be derived only knowing the assignment of tasks to ASIP. To address this circular dependency, we propose an Uncertainty Model for the WCETs, which captures the performance of tasks running on a range of possible ASIP implementations. We propose a novel stochastic schedulability analysis to evaluate each multi-ASIP platform. We use an Evolutionary Algorithm-based approach to explore the design space of macro-architecture possibilities and we evaluate it using real case studies.

Evolutionary algorithm for analyzing higher degree research student recruitment and completion

In this paper, we consider a decision problem arising from higher degree research student recruitment process in a university environment. The problem is to recruit a number of research students by maximizing the sum of a performance index satisfying a number of constraints, such as supervision capacity and resource limitation. The problem is dynamic in nature as the number of eligible applicants, the supervision capacity, completion time, funding for scholarships, and other resources vary from period to period and they are difficult to predict in advance. In this research, we have developed a mathematical model to represent this dynamic decision problem and adopted an evolutionary algorithm-based approach to solve the problem. We have demonstrated how the recruitment decision can be made with a defined objective and how the model can be used for long-run planning for improvement of higher degree research program.

Optimal design of power system stabilizer for power systems including doubly fed induction generator wind turbines

This paper presents an evolutionary algorithm-based approach for optimal design of power system stabilizer (PSS) for multi-machine power systems that include doubly fed induction generator wind turbines. The proposed evolutionary algorithm is an improved particle swarm optimization named chaotic particle swarm optimization with passive congregation (CPSO-PC) applied for finding the optimal settings of PSS parameters. Two different eigenvalue-based objectives are combined as the objective function for the optimization problem of tuning PSS parameters. The first objective function comprises the damping factor of lightly damped electro-mechanical modes and the second one includes the damping ratio of these modes. The effectiveness of the proposed method to design PSS for the power systems including DFIG (Doubly Fed Induction Generator) is extensively demonstrated through eigenvalue analysis and time-domain simulations and also by comparing its simulation results with the results of other heuristic optimization approaches.

A genetic-based subspace analysis method for improving Error-Correcting Output Coding

Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques.

An Object-oiented Framework with Multi-objective Cellular Evolutionary Algorithm for Service Restoration of Shipboard Power Networks

Pareto-based approaches clearly outperform conventional multi-objective approaches (i.e., the approaches of converting multi-objective into single objective by weighting factors), particularly when the preference relation among objectives often changes. However, when there are more than three objectives, for most Pareto-based approaches, it is very difficult to meet the speed requirement of service restoration of a shipboard power network. Due to lack of flexibility and compatibility, a fair comparison among algorithms is not easily implemented in process-oriented approaches. In this article, an object-oriented and multi-objective cellular evolutionary algorithm-based approach is proposed. In this technique, the multi-objective nature of the service restoration problem is retained, and the local search technology and elitist strategy are utilized. The proposed method greatly reduces the required execution time for the service restoration, and it has an obvious advantage, compared against other Pareto-based methods, concerning the diversity of solutions along the Pareto front. A public base platform has been constructed by extracting common attributes and operations of various Pareto-based methods. The computational procedures of three performance metrics are improved. As a result, a fair comparison can be achieved in the constructed platform. Simulation results demonstrate the feasibility and effectiveness of the proposed approach.

Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling

Traditionally, process planning and scheduling were performed sequentially, where scheduling was implemented after process plans had been generated. Considering their complementarity, it is necessary to integrate these two functions more tightly to improve the performance of a manufacturing system greatly. In this paper, a mathematical model of integrated process planning and scheduling has been formulated. And, an evolutionary algorithm-based approach has been developed to facilitate the integration and optimization of these two functions. To improve the optimized performance of the approach, efficient genetic representation and operator schemes have been developed. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show that the integrated process planning and scheduling is necessary and the proposed approach has achieved significant improvement.

Efficient Fuzzy Evolutionary Algorithm-Based Approach for Solving the Student Project Allocation Problem

This paper presents a solution framework for the student project allocation (SPA) problem which is based on evolutionary algorithms (EAs), a biologically inspired stochastic, population-based search paradigm. Project-based assessment is a common component of engineering courses that are conducted in universities around the world. In their final year of study, a list of projects is made available by the academic staff and students are required to select a specific number of options from this list. The department then assigns a suitable project to each student such that preferred projects can be allocated to as many students as possible. While student interest is the primary criteria, several additional factors need to be considered such as project prerequisites, load balancing of staff commitments, and other specific university requirements. The allocation problem can therefore be seen as a complex multiobjective problem with multiple constraints. The EA-based project allocation system was recently developed and implemented in a large university department to automate this process, and to improve the matching of students to their desired projects. The solution which provides the highest level of satisfaction in meeting the varied objectives is then used to allocate projects to students. This new automated system is not only able to achieve a very high level of user satisfaction, but is also able to do so in a very short time, resulting in significant time savings.

An evolutionary algorithm-based approach to robust analog circuit design using constrained multi-objective optimization

The increasing complexity of circuit design needs to be managed with appropriate optimization algorithms and accurate statistical description of design models in order to reach the design specifics, guaranteeing ” zero defects”. In the Design for Yield open problems are the design of effective optimization algorithms and statistical analysis for yield design, which require time consuming techniques. New methods have to balance accuracy, robustness and computational effort. Typical analog integrated circuit optimization problems are computationally hard and require the handling of multiple, conflicting, and non-commensurate objectives having strong nonlinear interdependence. This paper tackles the problem by evolutionary algorithms to produce tradeoff solutions on the Pareto Front. In this research work Integrated Circuit (IC) design has been formulated as a constrained multi-objective optimization problem defined in a mixed integer/discrete/continuous domain. The following real-life circuits, RF Low Noise Amplifier, LeapFrog Filter, and Ultra Wideband LNA, were selected as test bed. The proposed algorithm, A-NSGAII, was shown to produce acceptable and robust solutions in the tested applications, where state-of-art algorithms and circuit designers failed. The results show significant improvement in all the chosen IC design problems.

Effective Course-of-Action Determination to Achieve Desired Effects

An evolutionary algorithm-based approach to identify effective courses of action (COAs) in dynamic uncertain situations is presented. The uncertain situation is modeled using timed influence nets, an instance of dynamic Bayesian networks. The approach makes significant enhancements to the current trial-and-error-based manual technique, which is not only labor intensive but also not capable of modeling constraints among actionable events. The proposed approach is an attempt to overcome these limitations. It automates the process of COA identification. It also allows a system analyst to capture certain types of constraints among actionable events. Because of its parallel search nature, the approach produces multiple COAs that have a similar fitness value. This feature not only gives more flexibility to a decision maker during mission planning, but it can also be used to generalize the COAs if there exists a pattern among them. This paper also discusses a heuristic that further enhances the performance of the approach.

An Evolutionary Algorithm-Based Approach to Automated Design of Analog and RF Circuits Using Adaptive Normalized Cost Functions

Typical analog and radio frequency (RF) circuit sizing optimization problems are computationally hard and require the handling of several conflicting cost criteria. Many researchers have used sequential stochastic refinement methods to solve them, where the different cost criteria can either be combined into a single-objective function to find a unique solution, or they can be handled by multiobjective optimization methods to produce tradeoff solutions on the Pareto front. This paper presents a method for solving the problem by the former approach. We propose a systematic method for incorporating the tradeoff wisdom inspired by the circuit domain knowledge in the formulation of the composite cost function. Key issues have been identified and the problem has been divided into two parts: a) normalization of objective functions and b) assignment of weights to objectives in the cost function. A nonlinear, parameterized normalization strategy has been proposed and has been shown to be better than traditional linear normalization functions. Further, the designers' problem specific knowledge is assembled in the form of a partially ordered set, which is used to construct a hierarchical cost graph for the problem. The scalar cost function is calculated based on this graph. Adaptive mechanisms have been introduced to dynamically change the structure of the graph to improve the chances of reaching the near-optimal solution. A correlated double sampling offset-compensated switched capacitor analog integrator circuit and an RF low-noise amplifier in an industry-standard 0.18mum CMOS technology have been chosen for experimental study. Optimization results have been shown for both the traditional and the proposed methods. The results show significant improvement in both the chosen design problems

An Efficient Data Mining Method for Learning Bayesian Networks Using an Evolutionary Algorithm-Based Hybrid Approach

Given the explosive growth of data collected from current business environment, data mining can potentially discover new knowledge to improve managerial decision making. This paper proposes a novel data mining approach that employs an evolutionary algorithm to discover knowledge represented in Bayesian networks. The approach is applied successfully to handle the business problem of finding response models from direct marketing data. Learning Bayesian networks from data is a difficult problem. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second one searches good network structures according to a metric. Unfortunately, both approaches have their own drawbacks. Thus, we propose a novel hybrid algorithm of the two approaches, which consists of two phases, namely, the conditional independence (CI) test and the search phases. In the CI test phase, dependency analysis is conducted to reduce the size of the search space. In the search phase, good Bayesian network models are generated by using an evolutionary algorithm. A new operator is introduced to further enhance the search effectiveness and efficiency. In a number of experiments and comparisons, the hybrid algorithm outperforms MDLEP, our previous algorithm which uses evolutionary programming (EP) for network learning, and other network learning algorithms. We then apply the approach to two data sets of direct marketing and compare the performance of the evolved Bayesian networks obtained by the new algorithm with those by MDLEP, the logistic regression models, the na/spl inodot//spl uml/ve Bayesian classifiers, and the tree-augmented na/spl inodot//spl uml/ve Bayesian network classifiers (TAN). In the comparison, the new algorithm outperforms the others.