Hybrid Genetic Approach Research Articles

Optimization of heterogeneous Bin packing using adaptive genetic algorithm

This research is concentrates on a very interesting work, the bin packing using hybrid genetic approach. The optimal and feasible packing of goods for transportation and distribution to various locations by satisfying the practical constraints are the key points in this project work. As the number of boxes for packing can not be predicted in advance and the boxes may not be of same category always. It also involves many practical constraints that are why the optimal packing makes much importance to the industries. This work presents a combinational of heuristic Genetic Algorithm (HGA) for solving Three Dimensional (3D) Single container arbitrary sized rectangular prismatic bin packing optimization problem by considering most of the practical constraints facing in logistic industries. This goal was achieved in this research by optimizing the empty volume inside the container using genetic approach. Feasible packing pattern was achieved by satisfying various practical constraints like box orientation, stack priority, container stability, weight constraint, overlapping constraint, shipment placement constraint. 3D bin packing problem consists of ‘n’ number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and in-turn profit. Furthermore, Boxes to be packed may be of arbitrary sizes. The user input data are the number of bins, its size, shape, weight, and constraints if any along with standard container dimension. This user input were stored in the database and encoded to string (chromosomes) format which were normally acceptable by GA. GA operators were allowed to act over these encoded strings for finding the best solution.

Hybrid genetic algorithm-decision tree approach for rate constant prediction using structures of reactants and solvent for Diels-Alder reaction

In recent years, Computer-Aided Molecular Design (CAMD) has been extensively used for defining and designing reactions at their maximal potential. In all of these contributions, either the structures of reactants/products have been considered to be unchanging or the solvent structure. Developing a QSPR model which not only captures the influence of reactant structures but also the solvent effect on reaction rate, is essential. Since the structures of reactants and products are related, such QSPR models will serve as a prerequisite for the simultaneous CAMD of reactants, products and solvents. They will also provide a useful tool for predicting the rate constant without relying on experiments. To develop such a QSPR, in our work, the Diels-Alder reaction with different sets of reactants and solvents was investigated. Connectivity indices were used to represent the structures of the members of each set. Principal Component Analysis (PCA) was applied to identify principal components (PCs) corresponding to the structures of reactants and solvent of each set. Linear models expressed in terms of PCs were then generated using a Decision Tree (DT) algorithm such that the R2 value was maximized. These models formed the initial population on which the GA performed operations such as crossover and mutation to obtain model(s) with best rate constant prediction. Thus, the novelty of our approach is that after feature extraction using PCA, a DT algorithm generates an ensemble of linear models, which through the GA is transformed into a model with best fit. Our approach required much lesser generations to provide a model with highest R2ext value as compared to the case where the DT did not initialize the population of models.

A hybrid genetic approach for solving an integrated multi-objective operating room planning and scheduling problem

In this paper, we propose a multi-objective integer linear programming model aiming at efficiently planning and managing hospital operating room suites. By effectively exploiting a novel hybrid genetic solution approach, the devised optimization model is able to determine, in an integrated way, (i) the operating room time assigned to each surgical specialty, (ii) the operating room time assigned to each surgical team, (iii) the surgery admission planning and (iv) the surgery scheduling. The resulting Pareto frontiers provide a set of “optimal” solutions able to support hospital managers in efficiently orchestrating the involved resources and planning surgeons and surgeries. On this basis, the proposed solution framework could represent a suitable engine for the development of advanced and effective health care management decision support systems.

Automated pool detection from satellite images using data mining techniques

A significant part of data mining is image mining, a principle that deals with image data relationship or patterns which are not explicitly found in the images. The basic objective is to gather knowledge from images with some known methods such as image retrieval, image processing and artificial intelligence. Towards this direction, this paper proposes a classification system for identifying pools from satellite area images. For the feature extraction of every photograph, and therefore the creation of the data set, the method of trainable segmentation is applied. In the current approach, genetic algorithms are utilised in an attempt to reduce the feature set to only the informative ones and class imbalance issues were also dealt with by incorporating a hybrid boosting and genetic sub-sampling approach. Results show that the best precision and recall rates are achieved by using a combination of AdaBoost and neural network algorithm.

Hybrid Genetic Optimization to Mitigate Starvation in Wireless Mesh Networks

Background/Objectives: The objective of this work is to mitigate starvation in Wireless Mesh Networks (WMNs) being deployed in today’s LAN, WAN and Internet topologies by employing a novel optimization method. Methods/Analysis: The QoS performance of WMNs is severely affected by a problem called starvation where nodes that are one-hop away from the gateway monopolize the channel so that far away nodes get starved of channel access. We propose a hybrid genetic algorithmic approach to mitigate starvation in WMNs by dynamic adjustment of contention window of mesh nodes optimally. In this approach, Genetic Algorithm incorporated with Gravitational Search Algorithm is used. Findings: Simulations are conducted using the proposed method for multimedia traffic with AODV as the routing protocol. The performance of the proposed method is compared with priority-based method, pure GA optimization, Fair Binary Exponential Back-off algorithm (FBEB) and IEEE 802.11. The local search capability of GSA incorporated in our proposed method improves the throughput by 24.64% than priority-based method and by 3.56% than pure GA optimization. In our approach, we observed a significant decrease in end-to-end delay compared to pure GA optimization. Improvement in fairness is found along one-hop, two-hop and three-hop nodes when compared with FBEB. The FBEB algorithm adjusts the CW size by indirectly estimating the traffic in communication medium leading to lesser throughput whereas our proposed method changes the CW size dynamically based on QoS parameters of network nodes leading to improvement in throughput. The proposed method increases throughput by 5.10% than IEEE 802.11 and by 1.25% than FBEB at one-hop. The proposed method increases throughput by 66.67% than IEEE 802.11 and by 22.61% than FBEB at two-hop. Application/Improvement: Our hybrid Genetic optimization method improves QoS performance of Wireless Mesh networks by avoiding throughput imbalances among users and reducing end-to-end delay effectively. Keywords: Contention Window, Genetic Algorithm, Gravitational Search Algorithm, Starvation, Wireless Mesh Networks

Knowledge Extraction in Fuzzy Relational Systems Based on Genetic and Neural Approach

In this paper, a problem of MIMO object identification expressed mathematically in terms of fuzzy relational equations is considered. We use the multivariable relational structure based on the modular fuzzy relational equations with the multilevel composition law. The identification problem consists of extraction of an unknown relational matrix and also of parameters of membership functions included in the fuzzy knowledge base, which can be translated as a set of fuzzy IF-THEN rules. In fuzzy relational calculus this type of the problem relates to inverse problem and requires resolution for the composite fuzzy relational equations. The search for solution amounts to solving an optimization problem using the hybrid genetic and neural approach. The genetic algorithm uses all the available experimental information for the optimization, i.e., operates off-line. The essence of the approach is in constructing and training a special neuro-fuzzy network, which allows on-line correction of the extracted relations if the new experimental data is obtained. The resulting solution is linguistically interpreted as a set of possible rules bases. The approach proposed is illustrated by the computer experiment and the example from medical diagnosis.

A hybrid genetic algorithm for job sequencing and worker allocation in parallel unrelated machines with sequence-dependent setup times

In this paper, the unrelated parallel machine scheduling problem with sequence-dependent setup times and limited human resources is addressed with reference to the makespan minimisation objective. Workers needed for setup operations are supposed to be a critical resource as their number is assumed to be lower than the number of workstations. In addition, each worker is characterised by a specific skill level, which affects setup times. Firstly, a mathematical model able to optimally solve small instances of the problem in hand is illustrated. Then, to deal with large-sized test cases, three different optimisation procedures equipped by different encoding methods are proposed: a permutation encoding-based genetic algorithm (GA), a multi-encoding GA and a hybrid GA that properly moves from a permutation encoding to a multi-encoding once a given threshold on the number of generations is achieved. In particular, three different hybrid GAs featured by different encoding switch thresholds were implemented. An extensive benchmark including both small- and large-sized instances was generated with the aim of both calibrating the genetic parameters and comparing the alternative GAs through distinct ANOVA analyses. Numerical results confirm the effectiveness of the hybrid genetic approach whose encoding switch threshold is fixed to 25 % of the overall generations. Finally, a further analysis concerning the impact of multi-skilled workforce on the performance of both production system and optimisation strategy is presented.

Customer churn prediction using a hybrid genetic programming approach

A churn consumer can be defined as a customer who transfers from one service provider to another service provider. Recently, business operators have investigated many techniques that identify the customer churn since churn rates leads to serious business loss. In this paper, a hybrid technique has been used which combines K-means clustering with Genetic Programming to predict churners in telecommunication companies. First, K-means clustering is used to filter the training dataset from outliers and non representative customer behaviors then Genetic Programming is applied in order to build classification trees that are able to classify customers into churners and non churners. The proposed approach is evaluated and compared with other common classification approaches. Experimental results show that K-means clustering with Genetic Programming has promising

A hybrid genetic approach for multi-objective and multi-platform large volume surveillance problem

Efficient management of surveillance assets and successful scheduling of surveillance tasks are complex decision-making problems for the execution of large volume surveillance missions in order to improve security and safety. A mission can be seen as a defined set of logical ordered tasks with time and space constraints. The resources to task assignment rules require that available assets should be allocated to each task. A combination of assets might be required to execute a given task. Finding efficient management solutions should be investigated to optimise assets-resources allocation and tasks scheduling. In this paper, we propose to model this optimisation problem as a multi-objective, multi-platform assignment and scheduling problem. Resources are to be assigned to accomplish different tasks. Surveillance tasks should be scheduled into successive periods. The problem is designed to consider two conflicting objective functions: minimising the makespan and minimising the total cost. As the problem is NP-hard, a hybrid genetic algorithm HGA is proposed. The empirical validation is performed using a simulation environment called Inform Lab, and a comparison to two state-of-the-art multi-objective approaches based on selected performance metrics. The experimental results show that HGA performs consistently well for high dimensional problems.

Novel Hybrid Genetic Approach for Two Dimensional Guillotinable Cutting Problems

The paper deals with the purpose of one hybrid approach for solving the constrained two-dimensional cutting (2DC) problem. The authors study this hybrid approach that combines the genetic algorithm and the Tabu search method. For this problem, they assume a packing of a whole number of rectangular pieces to cut, and that all cuts are of guillotine type in one sheet of a fixed width and an infinite height. Finally, the authors undertake an extensive experimental study with a large number of problem instances extracted from the literature by the Burke Benchmarks and the Beasley Benchmarks in order to support and to prove their approach and to evaluate the performance.

Nonlinear constrained optimal control problem: a PSO–GA-based discrete augmented Lagrangian approach

This work deals with the optimal control problem which has been proposed to solve using the discrete augmented Lagrangian-based nonlinear programming approach. It is shown that this technique guarantees a satisfactory performance by minimizing the energy and maximizing the output. Further, a hybrid particle swarm and genetic algorithm-based approach is used to achieve the optimal value of Lagrange multipliers and required parameters. The designed scheme has been successfully tested through extensive simulation. The successful use of the proposed scheme encourages to other physical systems. The proposed scheme is evaluated extensively on a laboratory-scale coupled-tank system.

A hybrid genetic algorithmic approach to the maximally diverse grouping problem

The maximally diverse grouping problem (MDGP) is a NP-complete problem. For such NP-complete problems, heuristics play a major role in searching for solutions. Most of the heuristics for MDGP focus on the equal group-size situation. In this paper, we develop a genetic algorithm (GA)-based hybrid heuristic to solve this problem considering not only the equal group-size situation but also the different group-size situation. The performance of the algorithm is compared with the established Lotfi–Cerveny–Weitz algorithm and the non-hybrid GA. Computational experience indicates that the proposed GA-based hybrid algorithm is a good tool for solving MDGP. Moreover, it can be easily modified to solve other equivalent problems.

Hybrid genetic approach for 1-D bin packing problem

This paper deals with the one-dimensional Bin Packing Problem (1-D BPP). Exact solution methods can only be used for very small instances, hence for real-world problems we have to focus on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to this problem, including Genetic Algorithms and Evolutionary Programming. In this paper, we propose a Hybrid Genetic Algorithm (HGA) to solve 1-D BPP. We compare our approach with algorithms given by Scholl et al., Alvim et al. and Kok-Hua et al. Then we discuss the performance of the approach. We show that giving at least the same performance on term of quality solution, our HGA approach outperforms these algorithms on term of computational time. This performance is due to new mechanisms of hybridisation of genetic algorithms and local search.

A Hybrid Genetic Algorithm for Optimization of Two-dimensional Cutting-Stock Problem

In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this method, the genetic algorithm is used to select a set of cutting patterns while the linear programming model permits one to create the lengths to produce with each cutting pattern to fulfil the customer orders with minimal production cost. The effectiveness of the hybrid genetic approach has been evaluated through a set of instances which are both randomly generated and collected from the literature.

Local‐genetic slicing of point clouds for rapid prototyping

PurposeThe purpose of this paper is to propose to overcome the limitations of polygonization of point clouds for rapid prototyping purposes, by using a direct slicing approach, based on a hybrid local‐genetic algorithm to achieve a robust direct slicing system.Design/methodology/approachAt first a volume analysis is performed on the point cloud and a space decomposition is realized using elementary voxels. Then, considering each Z level of the voxelized point cloud, the external non‐void voxels are linked togheter using an hybrid local and genetic approach, to generate the contour of the object with an automatic process. The contour of the object is finally converted into slice files suitable for the rapid prototyping machine.FindingsThe genetic algorithm (GA) is very effective in detecting those slices where an optimal solution is not achieved with the local approach, and in finding the minimum path that connects the points belonging to the slice contour.Research limitations/implicationsFurther studies must be conducted to improve the efficiency of the approach to the travelling salesman problem (TSP) and to the relation between the cell dimension and the point cloud density. In this context, the use of adaptive slicing will be considered, in order to improve time performances.Practical implicationsThe approach is fully automated and enables the direct creation of layered manufactured copies of 3D scanned products directly from the point clouds, avoiding the tessellation phase that is often time consuming and characterized by errors in the STL file.Originality/valueThe use of TSP problem to solve the direct slicing of point clouds is more effective than simple spline fitting techniques, avoiding self‐crossing curves. This approach solves the TSP problem for each slice, exploiting the volumetric space decomposition to fasten the achievement of the solution. In fact the local knowledge is used by the nearest neighbours local search and the partial solution achieved is the starting point of the GA. The GA is effective in finding global minima and results to be fastened by the local approach.

A Hybrid Genetic Approach for the Dynamic Vehicle Routing Problem with Time Windows

SYNOPTIC ABSTRACTRecent progress in information technology brings new challenges in dealing with the dynamic vehicle routing problem with time windows (DVRPTW). However, few alternate methods to the well-known Tabu Search –based techniques have been proposed so far to solve the DVRPTW efficiently. In this paper, a new hybrid genetic approach (HGA-LCS) to address the DVRPTW is presented. The basic scheme consists in concurrently evolving two populations of solutions to minimize customer service denial, lateness or temporal constraint violation, and total traveled distance. Combining variations of key concepts inspired from routing techniques and search strategies to define new hybrid genetic operators, the proposed approach also exploits a least commitment routing policy in servicing scheduled customers to potentially improve solution quality. The strategy consists in delaying customer visits and therefore premature route construction as long as possible to deal with a larger number of customers all at once when building a solution. Comparative results from a computational experiment over a common set of benchmark problems show the proposed procedure to match or outperform some of the best heuristic routing methods while being fast and highly competitive.

A hybrid genetic algorithm for the two-dimensional single large object placement problem

In the two-dimensional single large object placement problem, we are given a rectangular master surface which has to be cut into a set of smaller rectangular items, with the aim of maximizing the total value of the pieces cut. We consider the special case in which the items cannot be rotated and must be cut with their edges always parallel to the edges of the surface. We present new greedy algorithms and a hybrid genetic approach with elitist theory, immigration rate, heuristics on-line and tailored crossover operators. Extensive computational results for a large number of small and large benchmark test problems are presented. The results show that our approach outperforms existing heuristic algorithms.

Hybrid genetic algorithm for dual selection

In this paper, a hybrid genetic approach is proposed to solve the problem of designing a subdatabase of the original one with the highest classification performances, the lowest number of features and the highest number of patterns. The method can simultaneously treat the double problem of editing instance patterns and selecting features as a single optimization problem, and therefore aims at providing a better level of information. The search is optimized by dividing the algorithm into self-controlled phases managed by a combination of pure genetic process and dedicated local approaches. Different heuristics such as an adapted chromosome structure and evolutionary memory are introduced to promote diversity and elitism in the genetic population. They particularly facilitate the resolution of real applications in the chemometric field presenting databases with large feature sizes and medium cardinalities. The study focuses on the double objective of enhancing the reliability of results while reducing the time consumed by combining genetic exploration and a local approach in such a way that excessive computational CPU costs are avoided. The usefulness of the method is demonstrated with artificial and real data and its performance is compared to other approaches.

A hybrid genetic approach for airborne sensor vehicle routing in real-time reconnaissance missions

Past initiatives to address surveillance and reconnaissance mission planning mainly focused on low-level control aspects such as real-time path planning and collision avoidance algorithms in limited environment. However, few efforts have been spent on high-level real-time task allocation. It is believed that automated decision capabilities supporting real-time resource allocation for sensor control and interactions might significantly reduce user workload, focusing attention on alternate tasks and objectives while assigning hard computational tasks to artificial agents. In this paper, we propose a new hybrid genetic algorithm to solve the dynamic vehicle routing problem with time windows, in which a group of airborne sensors are engaged in a reconnaissance mission evolving in a dynamic uncertain environment involving known and unknown targets/threats. In that context, visiting a target may consist in carrying out a collection of subtasks such as search, detect, recognize and confirm suspected targets, discover and confirm new ones. The approach consists in concurrently evolving two populations of solutions to minimize total travel time and temporal constraint violation using genetic operators combining variations of key concepts inspired from routing techniques and search strategies. A least commitment principle in servicing scheduled customers is also exploited to potentially improve solution quality.

Hybrid genetic approach for solving large-scale capacitated cell formation problems with multiple routings

We present a comprehensive model for designing a cellular manufacturing system. The model bridges several known problems in that it integrates the cell formation problem, the machine allocation problem, and the part routing problem. Multiple process plans for each part and multiple routing alternatives for each of those process plans are considered. The part demands can be satisfied from internal production or through outsourcing. Machines have limited capacities. We propose a solution methodology based on a combination of a genetic algorithm and large-scale optimization techniques. A computational study is conducted to evaluate the viability of our approach for solving large scale problems. A limited computational experiment involving smaller problems, that are special cases of our model, is also conducted to compare our solution approach with existing models.