Type Of Genetic Algorithm Research Articles

Long-haul truck charging planning problem considering time flexibility and energy flexibility

The study explores opportunity charging as a solution for extending electric truck range during routine stops. It proposes a mixed-integer optimization model that combines opportunity charging with truck charging trip planning to optimize charging time and energy. The model examines variability in charging strategy and integrates potential driver choices and behaviors, including hub-charging and en-route charging options. An improved heuristic method is proposed, and the model's effectiveness is verified through numerical experiments. We present the Improved Dung Beetle Optimization (IDBO) algorithm based on the sine-cosine transformation process to solve the model. The proposed IDBO algorithm has the best optimization capability compared to typical genetic algorithms and traditional Dung Beetle Optimization (DBO), and the resulting total cost value was the lowest in all three cases used for the calculation.

DYNAMIC THRESHOLDING GA-BASED ECG FEATURE SELECTION IN CARDIOVASCULAR DISEASE DIAGNOSIS

Electrocardiogram (ECG) data are usually used to diagnose cardiovascular disease (CVD) with the help of a revolutionary algorithm. Feature selection is a crucial step in the development of accurate and reliable diagnostic models for CVDs. This research introduces the dynamic threshold genetic algorithm (DTGA) algorithm, a type of genetic algorithm that is used for optimization problems and discusses its use in the context of feature selection. This research reveals the success of DTGA in selecting relevant ECG features that ultimately enhance accuracy and efficiency in the diagnosis of CVD. This work also proves the benefits of employing DTGA in clinical practice, including a reduction in the amount of time spent diagnosing patients and an increase in the precision with which individuals who are at risk of CVD can be identified.

Real-time, adaptive microphone subset selection for a 128-elementarray

Some advantages of high-sensor count microphone arrays include gain over random noise, increased multiple-source resolution, and potential increases in effective frequency bandwidth. But in real-time applications, computational capabilities of embedded computing hardware might limit the number data channels that can be processed without data loss. For example, given a specific processing pipeline and sampling rate, perhaps only 24 of 128 channels can be supported in real-time. Therefore, the question of which 24-element subset of the 128 possible elements should be being used to detect and track multiple moving acoustic sources. In this presentation, principles of genetic search algorithms are used to develop a method to adaptively select optimal subsets of sensors as data is being processed. Discussion concerning optimality criteria, types of genetic algorithms, and algorithm performance will be presented as well as results corresponding to a 3-D 128-element array of digital MEMS microphones when multiple types of moving acoustic sources were nearby.

Calculation of Optimum Transit Times with Real-Coded Genetic Algorithm

Electron energy analysers have been designed to analyse charged-particle beams at specific energies. The design is based on the principle that electrons with different energies arrive at the detector at different times. Since electrons with different energies follow different orbits within these analysers. In collision experiments, it is very important to determine the trajectories and transit times of the charged particles in the analyser. In this study, optimum solutions for transit times of charged particles were provided using a real-coded genetic algorithm. Hyper parameters and types of genetic algorithm were obtained using trial and error methods, in this study. The results of this study indicate that genetic algorithm gives time resolution values in a wide data set with high accuracy. The results show that genetic algorithms (GA) are a fascinating approach for solving search and optimization problems.

Modification of Genetic Algorithm Based on Extinction Events and Migration

This article presents a genetic algorithm modification inspired by events related to great extinctions. The main objective of the modification was to minimize the number of objective function solutions until the minimum for the function was established. It was assumed that, within each step, a population should be smaller than that recommended in the applicable literature, the number of iterations should be limited, the solution area should be variable, and a great extinction event should take place following several iterations. Calculations were performed for 10 individuals within a population, 10 iterations, two generations each, with a great extinction event happening once every three iterations. The developed algorithm was presented, capable of indicating the minimum number of Eggholder and Rastrigin functions, with a higher probability than the master algorithm (default “ga” in MATLAB) at the same number of objective function solutions. An algorithm was proposed focusing on minimizing the randomization of the objective function, which may be an alternative to the surrogate model. Typically, the emphasis is on achieving as much accuracy as possible. This article presents a method for minimizing the randomization of the objective function and obtaining the highest possible accuracy. A method is presented which minimizes the disadvantages of the largest computation time and the need to generate many samples for typical genetic algorithms (GAs). Optimization results for the classic GA, GEGA, WOA, SMA, and SSA algorithms for the Eggholder and Rastrigin functions were compared. A modification of the genetic algorithm was made to obtain a global extreme with satisfactory accuracy and a sufficiently high probability, while minimizing the number of samples calculated on the basis of the objective function. The developed methodology was used to fulfill the target function for the turbine disc.

Linkage Problem in Location Optimization of Dedicated Bus Lanes on a Network

Methods for identifying optimal decisions for dedicated bus lane locations (DBLs) on a network have been extensively studied in the literature. However, the impacts in relation to changes to car and bus delays of deploying a DBL on a given link largely depend on where other DBLs exist on the network. Therefore, for a network-wide location optimization or a bus lane design problem, linkages exist between decision variables. Typically used metaheuristic methods to optimize DBL locations, such as genetic algorithms (GAs), do not perform well for such problems with linkages between decision variables. To this end, this paper has two novel contributions to the literature by (a) demonstrating that the linkage problem exists, and (b) testing different heuristic algorithms that are more suitable than GAs for optimizing the locations of DBL on a network. The linkage problem in the location optimization of DBLs is demonstrated by enumerating all possible bus lane locations in a small grid network. Next, optimization algorithms that do not enumerate all possible bus lane locations that are capable of learning linkages between decision variables, namely Bayesian algorithm and a population-based incremental learning algorithm, are proposed. These algorithms are compared with two types of GAs in relation to consistency and quality of the solutions, and exploration capability. Results show that algorithms that can learn linkages between decision variables perform better than the GAs.

An iterated greedy matheuristic for scheduling in steelmaking-continuous casting process

Steelmaking-Continuous Casting (SCC) is a bottleneck in the steel production process and its scheduling has become more challenging over time. In this paper, we provide an extensive literature review that highlights challenges in the SCC scheduling and compares existing solution methods. From the literature review, we collect the essential features of an SCC process, such as unrelated parallel machine environments, stage skipping, and maximum waiting time limits in between successive stages. We consider an SCC scheduling problem with as objective the minimisation of the weighted sum of cast break penalties, total waiting time, total earliness, and total tardiness. We formulate the problem as a mixed-integer linear programming model and develop an iterated greedy matheuristic that solves its subproblems to find a near-optimal solution. Through numerical experiments, we show that our algorithm outperforms two types of genetic algorithms when applied to test instances.

How to Assess Different Algorithms Using Intuitionistic Fuzzy Logic

Intuitionistic fuzzy logic is the main tool in the recently developed step-wise “cross-evaluation” procedure that aims at the assessment of different optimization algorithms. In this investigation, the procedure previously applied to compare the effectiveness of two or three algorithms has been significantly upgraded to evaluate the performance of a set of four algorithms. For the first time, the procedure applied here has been tested in the evaluation of the effectiveness of genetic algorithms (GAs), which are proven as very promising and successful optimization techniques for solving hard non-linear optimization tasks. As a case study exemplified with the parameter identification of a S. cerevisiae fed-batch fermentation process model, the cross-evaluation procedure has been executed to compare four different types of GAs, and more specifically, multi-population genetic algorithms (MGAs), which differ in the order of application of the three genetic operators: Selection, crossover and mutation. The results obtained from the implementation of the upgraded intuitionistic fuzzy logic-based procedure for MGA performance assessment have been analyzed, and the standard MGA has been outlined as the fastest and most reliable one among the four investigated algorithms.

Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization

Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods for normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes or entire intersections are affected. This paper presents a novel methodology for optimizing the traffic signal timings in signalized urban intersections, under non-recurrent traffic incidents. With the purpose of producing fast and reliable decisions, we combine the fast running Machine Learning (ML) algorithms and the reliable Genetic Algorithms (GA) into a single optimization framework. Firstly, we deploy a typical GA algorithm by considering the phase duration as the decision variable and the objective function as the total travel time in the network. We fine tune the GA for crossover, mutation, fitness calculation and obtain the optimal parameters. Secondly, we train several regression models to predict the total travel time in the studied traffic network, and select the best performing model which we further hyper-tune. Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree (XGBT), which is the best performing regression model, together in a single optimization framework. Comparison and results are generated by two experiments (one synthetic and one from real urban traffic network) and show that the new BGA-ML is much faster than the original GA algorithm and can reduce the total travel time by almost half when used under incident conditions.

An FPGA-Based Accelerated Optimization Algorithm for Real-Time Applications

Many modern applications, such as industrial control, image processing and machine learning, are based on optimization algorithms that must solve compute-intensive problems under real-time constraints taking into account real-world parameters that evolve in time. Among others, evolutionary algorithms (EAs) are increasingly used in real-time applications to solve such complex optimization problems. Moreover, Field Programmable Gate Arrays (FPGAs) have been proved an effective platform for the implementation of these algorithms satisfying real-time and low-power requirements. In this paper, we study the FPGA-based acceleration of the Big Bang-Big Crunch (BB-BC) algorithm. BB-BC is an optimization method inspired by the corresponding evolutionary theory of the universe [1]. The BB-BC method is performed in two phases: in the Bing Bang phase, similarly to other Genetic Algorithms (GAs) it generates a random population of candidate solutions, while in the Big Crunch phase it shrinks these candidates around an optimal point. It has been shown that the BB-BC method outperforms classical GA algorithms for several optimization problems in terms of convergence speed. We show that the BB-BC algorithm does not suffer from the design limitations of the classical GAs that impede the performance of their hardware-based accelerators. We propose an efficient fully pipelined design of both BB-BC phases and a parallel scheme which integrates several BB-BC pipelined engines to improve system performance. We implement the proposed FPGA-based accelerator on a Xilinx Virtex-5 development board for three different fitness functions and compare the execution time against its software counterpart (in C language) and a CUDA program running on a massively parallel computing platform (GPU). We also compare the proposed optimized FPGA architecture with an RTL design generated by a high-level synthesis (HLS) design flow. We propose an Adaptive Neuro-fuzzy Inference System (ANFIS) model for fitness function approximation method to reduce the execution latency of complex fitness functions. We also demonstrate the efficiency of the proposed FPGA architecture on an image search problem, finding the darkest pixel of a grey image. The experimental results show that the proposed approach achieves significant speedup compared to the other software versions of the BB-BC algorithm and converges much faster than a typical GA algorithm making it an ideal solution for real-time embedded applications.

Comparison performances of the optimisation models for the multi-objectives reservoir

The reservoir systems require a continuous development tools in the optimal operation for a sustainable and long-term reservoir system planning and management. Insufficient water supply from the reservoir especially during dry season could affect the paddy production, consumers and industrial needs. Thus, the aims of this study is to optimise the water releases from the Pedu-Muda reservoirs and at the same time to maximise the water storage for other purposes. The optimisation model that been used comprises the non-dominated sorting genetic algorithm type II (NSGA-II) and linear programming (LP) to optimise multi-objectives reservoirs. These comparison performances were evaluated by reliability, resiliency, and vulnerability indices. Results revealed both models were capable to provide sufficient water for the irrigation demand without shortage. Unlike LP, NSGA-II was also successfully to maximise the water storage for another purposes. It provided a set of alternative solutions that were presented in the Pareto optimal curves depending on the climatic pattern. The finding was also supported by the reliability (91.7% excessive water and 8.3% meet the demand), resiliency (100%), and vulnerability (null) indices which better than the LP performances.

Enhanced Genetic Algorithm Optimization Models for Vehicular Routing Problems

The Vehicle Routing Problem (VRP) is one of the most studied combinatorial optimization problems because of its practical relevance and complexity. Though there are several techniques have been proposed to solve the VRPs and its variants effectively, each technique has its own tradeoff values in terms of the performance factors. From this perspective, the work presented in this paper proposed an intelligent routing strategy for VRP based on distance values between the cities. The proposed strategy uses an enhanced model of Genetic Algorithm to find the optimal tour paths among the cities under distance based optimized tour path estimation scenarios. For distance-based optimization approach, experiments were performed on the standard benchmark TSP instances obtained from TSPLIB. A set of fine-grained result analyses demonstrated that the proposed model of routing strategies performed comparatively better w.r.t. the existing relevant approaches. By considering this problem as the base, a distinct model was developed as a set of assistive modules for Genetic Algorithms (GA), which are aimed at improving the overall efficiency of the typical GA, particularly for optimization problems. The capability of the proposed optimization models for VRP is demonstrated at various levels, particularly at the population initialization stage, using a set of well-defined experiments.

A Systematic Literature Review of Test Case Prioritization Using Genetic Algorithms

Regression testing is the essential process of software maintenance and evolution phase of the software development life cycle for assuring the quality and reliability of updated software. Test case prioritization is the technique of regression testing to reduce the time and effort required for regression testing. Search-based algorithms are used to enhance the efficiency and effectiveness of the method. Among these search-based optimization algorithms, genetic algorithms are becoming more popular among researchers since the last decade. In this paper, we are doing a systematic literature review, i.e., a secondary study of test case prioritization using genetic algorithms. The objective of this review is to examine and classify the current state of use of the genetic algorithm in test case prioritization. In other words, to give a base for the advancement of test case prioritization research using genetic algorithms. With the use of the systematic literature review protocol, we selected the most relevant studies (20 out of 384) from the appropriate repositories by using a set of search keywords, inclusion/exclusion criteria and the quality assessment of studies. The data extraction and synthesis process and the taxonomic classification are used to answer the research questions. We also performed a rigorous analysis of the techniques by comparing them on research methodology, the prioritization method, dataset specification, test suite size, types of genetic algorithms used, performance metrics, and the validation criteria. The whole process took four months for comprehensive analysis and classification of primary studies. We observed that the parameter settings, the type of operators, the probabilistic rate of operators, and fitness function design have a significant impact on the quality of the solutions obtained. This systematic literature review yields that genetic algorithms have great potential in solving test case prioritization problems, and the area is open for further improvements. Future researchers can fill the research gaps by following the suggestions given in the review. From this review, we found that the use of the appropriate approach can make a genetic algorithm based test case prioritization one of the effective methods in regression testing.

Evolutionary Programming for Systematic Evaluation of Aquifers: A Case Study from Dholera, Cambay Basin, Gujarat, India

Joint inversion of different potentials improves subsurface model resolution. In this paper seismic refraction and magnetotelluric data are used to understand near subsurface features of Dholera, Gujarat, India. An extensive seismic and magnetotelluric survey was carried out in Dholera in order to delineate subsurface presence of aquifers. Ray Inversion for Near Surface Estimation (RINSE) is used for inversion of Dholera seismic data. The inversion output of seismic data is used as seed points for resistivity inversion of anomalies. Inversion of resistivity data is done using evolutionary programing method which is also a type of genetic algorithm. Here the optimization is done using four major steps, of evolutionary programing namely population generation, fitness function, crossover and mutation. This paper also compares the similarities between the natural and geophysical optimization. A Low Velocity Layer is identified up to a depth of 11 m from seismic refraction method. Three layers are identified after the interpretation of seismic and resistivity data. The average thicknesses of Layers one and two are calculated as 3.558 and 6.533 respectively.

Fuzzy Adaptive Genetic Algorithm for Improving the Solution of Industrial Optimization Problems

AbstractIn the industrial and manufacturing fields, many problems require tuning of the parameters of complex models by means of exploitation of empirical data. In some cases, the use of analytical methods for the determination of such parameters is not applicable; thus, heuristic methods are employed. One of the main disadvantages of these approaches is the risk of converging to “suboptimal” solutions. In this article, the use of a novel type of genetic algorithm is proposed to overcome this drawback. This approach exploits a fuzzy inference system that controls the search strategies of genetic algorithm on the basis of the real-time status of the optimization process. In this article, this method is tested on classical optimization problems and on three industrial applications that put into evidence the improvement of the capability of avoiding the local minima and the acceleration of the search process.

Evolution or revolution: the critical need in genetic algorithm based testing

Software testing is one of the most inevitable processes in software development. The field of software testing has seen an extensive use of search based techniques in the last decade. Among the search based techniques, it is the metaheuristic techniques such as genetic algorithm that has garnered the major share of attention from researchers. Looking at the large body of work that has happened and is happening in this field, we feel that it is high time someone studied how well genetic algorithm based techniques fare in practical testing process. Method: In this work, we present a roadmap to the future of genetic algorithm based software testing, based on a review of literature. We have mainly reviewed the works which use genetic algorithm for software test data generation. This independent review is designed to direct the attention of future researchers to the deficiencies of genetic algorithm based testing, their possible solutions and the extent to which they are correctable. The observations from the selected primary studies highlight the issues faced when genetic algorithm is applied in software testing. The observations form the review reveal that the type of genetic algorithm used, fitness function design, population initialization and parameter settings does impact the quality of solution obtained in software testing using genetic algorithm. From the review we conclude that, more generalized approaches can make genetic algorithm based software testing one of the strongest methods in practical software testing. We hope that, this review will be a major breakthrough in genetic algorithm based software testing field.

Painting with light: An interactive evolutionary system for daylighting design

Painting with Light is a user-guided interactive evolutionary system for daylighting design. It allows architects to use color to specify desired light levels in spaces, and searches for solutions that bring building geometry and materials close to performance targets. The proposed interface addresses a main limitation of generative design systems based on building performance metrics, by allowing the user to specify daylight spatial patterns with a high degree of granularity. This development poses new challenges for both objective function and penalty function definitions. Painting with Light automatically computes and displays statistical indexes that inform the user on the deviation error between the performance of the solution found by the system and the desired targets. The system was implemented in Python on top of a popular Computer-Aided Design software, Rhinoceros, as an add-on to its Visual Programming Language, Grasshopper. This choice of implementation allows access to Grasshopper's built-in functions and methods for 3D parametric modeling, to tools that provide direct access to Radiance, a lighting simulation software, and to different types of genetic algorithms. Five experiments were conducted on a freeform parametric model for progressive system calibration, which encompassed four steps: 1) adjustment of painted targets to fit the problem feasible solution space; 2) devise appropriate weights and penalty factors for the fitness function; 3) test two different evolutionary solvers; 4) test the system's capability to find a predefined solution where the optimal values were known. After calibration, the system was able to produce solutions that closely approximate the painted goals.

A new genetic algorithm based on modified Physarum network model for bandwidth-delay constrained least-cost multicast routing

A mobile ad hoc network is a kind of popular self-configuring network, in which multicast routing under the quality of service constraints, is a significant challenge. Many researchers have proved that such problem can be formulated as a NP-complete problem and proposed some swarm-based intelligent algorithms to solve the optimal solution, such as the genetic algorithm (GA), bees algorithm. However, a lower efficiency of local search ability and weak robustness still limit the computational effectiveness. Aiming to those shortcomings, a new hybrid algorithm inspired by the self-organization of Physarum, is proposed in this paper. In our algorithm, an updating scheme based on Physarum network model (PM) is used for improving the crossover operator of traditional GAs, in which the same parts of parent chromosomes are reserved and the new offspring by the PM is generated. In order to estimate the effectiveness of our proposed optimized scheme, some typical genetic algorithms and their updating algorithms (PMGAs) are compared for solving the multicast routing on four different datasets. The simulation experiments show that PMGAs are more efficient than original GAs. More importantly, the PMGAs are more robustness that is very important for solving the multicast routing problem. Moreover, a series of parameter analyses is used to find a set of better setting for realizing the maximal efficiency of our algorithm.

Fuzzy Adaptive Genetic Algorithm for Improving the Solution of Industrial Optimization Problems

In the industrial and manufacturing field, many problems require the tuning of the parameters of complex empirical or theoretical models by means of the exploitation of data. In some cases the use of analytical methods for the determination of such parameters is not applicable, thus heuristic methods are employed. One of the main disadvantages of the use of these latter approaches is the risk of converging to a sub-optimal solution due to characteristics of the search surface which is determined both by the model and the data available for the tuning. In this paper the use of a novel type of genetic algorithms is proposed to overcome this drawback. This approach exploits a fuzzy inference system that controls the search strategies of the genetic algorithm on the basis of the real-time status of the optimization process. In this paper this method is tested on three problems drawn from the steel-making industry that put into evidence the improvement of the capability of avoiding the local minima of the optimization problem and the acceleration of the search process.

Genetic Algorithms for Feature Selection for Brain–Computer Interface

The crucial problem that has to be solved when designing an effective brain–computer interface (BCI) is: how to reduce the huge space of features extracted from raw electroencephalography (EEG) signals. One of the strategies for feature selection that is often applied by BCI researchers is based on genetic algorithms (GAs). The two types of GAs that are most commonly used in BCI research are the classic algorithm and the Culling algorithm. This paper presents both algorithms and their application for selecting features crucial for the correct classification of EEG signals recorded during imagery movements of the left and right hand. The results returned by both algorithms are compared to those returned by an algorithm with aggressive mutation and an algorithm with melting individuals, both of which have been proposed by the author of this paper. While the aggressive mutation algorithm has been published previously, the melting individuals algorithm is presented here for the first time.