Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment

Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.

Portfolio Optimization and Asset Allocation With Metaheuristics

Portfolio optimization stands to be an issue of finding an optimal allocation of wealth to place within the obtainable assets. Markowitz stated the problem to be structured as dual-objective mean-risk optimization, pointing the best trade-off solutions within a portfolio between risks which is measured by variance and mean. Thus the major intention was nothing else than hunting for optimum distribution of wealth over a specific amount of assets by diminishing risk and maximizing returns of a portfolio. Value-at-risk, expected shortfall, and semi-variance measures prove to be complex for measuring risk, for maximization of skewness, liquidity, dividends by added objective functions, cardinality constraints, quantity constraints, minimum transaction lots, class constraints in real-world constraints all of which are incorporated in modern portfolio selection models, furnish numerous optimization challenges. The emerging portfolio optimization issue turns out to be extremely tough to be handled with exact approaches because it exhibits nonlinearities, discontinuities and high-dimensional, efficient boundaries. Because of these attributes, a number of researchers got motivated in researching the usage of metaheuristics, which stand to be effective measures for finding near optimal solutions for tough optimization issues in an adequate computational time frame. This review report serves as a short note on portfolio optimization field with the usage of Metaheuristics and finally states that how multi-objective metaheuristics prove to be efficient in dealing with portfolio selection problems with complex measures of risk defining non-convex, non-differential objective functions.

Energy and SLA Efficient Virtual Machine Placement in Cloud Environment Using Non-Dominated Sorting Genetic Algorithm

To increase the availability of the resources and simultaneously to reduce the energy consumption of data centers by providing a good level of the service are one of the major challenges in the cloud environment. With the increasing data centers and their size around the world, the focus of the current research is to save the consumption of energy inside data centers. Thus, this article presents an energy-efficient VM placement algorithm for the mapping of virtual machines over physical machines. The idea of the mapping of virtual machines over physical machines is to lessen the count of physical machines used inside the data center. In the proposed algorithm, the problem of VM placement is formulated using a non-dominated sorting genetic algorithm based multi-objective optimization. The objectives are: optimization of the energy consumption, reduction of the level of SLA violation and the minimization of the migration count.

Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web

This article describes how semantic web data sources follow linked data principles to facilitate efficient information retrieval and knowledge sharing. These data sources may provide complementary, overlapping or contradicting information. In order to integrate these data sources, the authors perform entity linking. Entity linking is an important task of identifying and linking entities across data sources that refer to the same real-world entities. In this work, they have proposed a genetic fuzzy approach to learn linkage rules for entity linking. This method is domain independent, automatic and scalable. Their approach uses fuzzy logic to adapt mutation and crossover rates of genetic programming to ensure guided convergence. The authors' experimental evaluation demonstrates that our approach is competitive and make significant improvements over state of the art methods.

Decision Choice Optimization With Genetic Algorithm in Communication Networks

Optimization is an essential tool in the field of decision support. In this chapter, the authors study an inverse problem applied in the telecommunication networks. Indeed, in the telecommunication networks, service providers have subscription offers to customers. Since competition is strong in this sector, most of these advertising offerings, totally or partially ambiguous, are prepared to attract the attention of consumers. For this reason, customers face problems in making decisions about the choice of the operators that gives them a better report price/QoS. Mathematical modeling of this decision support problem led to the resolution of an inverse problem. More precisely, the inverse problem is to find the function of the QoS real knowing the QoS theoretical or advertising. This model will help customers who seek to know the degree of sincerity of their operators, and it is an opportunity for operators who want to maintain their resources so that they gain the trust of customers.

Mathematical Optimization by Using Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and Its Similarities

To solve the problems of optimization, various methods are provided in different domain. Evolutionary computing (EC) is one of the methods to solve these problems. Mostly used EC techniques are available like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). These techniques have different working structure but the inner working structure is same. Different names and formulae are given for different task but ultimately all do the same. Here we tried to find out the similarities among these techniques and give the working structure in each step. All the steps are provided with proper example and code written in MATLAB, for better understanding. Here we started our discussion with introduction about optimization and solution to optimization problems by PSO, GA and DE. Finally, we have given brief comparison of these.

Intelligent Computing in Medical Imaging

Biomedical imaging is considered main procedure to acquire valuable physical information about the human body and some other biological species. It produces specialized images of different parts of the biological species for clinical analysis. It assimilates various specialized domains including nuclear medicine, radiological imaging, Positron emission tomography (PET), and microscopy. From the early discovery of X-rays, progress in biomedical imaging continued resulting in highly sophisticated medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound, Computed Tomography (CT), and lungs monitoring. These biomedical imaging techniques assist physicians for faster and accurate analysis and treatment. The present chapter discussed the impact of intelligent computing methods for biomedical image analysis and healthcare. Different Artificial Intelligence (AI) based automated biomedical image analysis are considered. Different approaches are discussed including the AI ability to resolve various medical imaging problems. It also introduced the popular AI procedures that employed to solve some special problems in medicine. Artificial Neural Network (ANN) and support vector machine (SVM) are active to classify different types of images from various imaging modalities. Different diagnostic analysis, such as mammogram analysis, MRI brain image analysis, CT images, PET images, and bone/retinal analysis using ANN, feed-forward back propagation ANN, probabilistic ANN, and extreme learning machine continuously. Various optimization techniques of ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO) and other bio-inspired procedures are also frequently conducted for feature extraction/selection and classification. The advantages and disadvantages of some AI approaches are discussed in the present chapter along with some suggested future research perspectives.

A Multiobjective Genetic-Algorithm-Based Optimization of Micro-Electrical Discharge Drilling

Inconel 718 superalloy finds wide range of applications in various industries due to its superior mechanical properties including high strength, high hardness, resistance to corrosion, etc. Though poor machinability especially in micro-domain by conventional machining processes makes it one of the “difficult-to-cut” material. The micro-electrical discharge machining (µ-EDM) is appropriate process for machining any conductive material, although selection of machining parameters for higher machining rate and accuracy is difficult task. The present study attempts to optimize parameters in micro-electrical discharge drilling (µ-EDD) of Inconel 718. The material removal rate, electrode wear ratio, overcut, and taper angle have been selected as performance measures while gap voltage, capacitance, electrode rotational speed, and feed rate have been selected as process parameters. The optimum setting of process parameters has been obtained using Genetic Algorithm based multi-objective optimization and verified experimentally.

An Efficient Batch Scheduling Model for Hospital Sterilization Services Using Genetic Algorithm

A major challenge faced by hospitals is to provide efficient medical services. The problem studied in this article is motivated by the hospital sterilization services where the washing step generally constitutes a bottleneck in the sterilization services. Therefore, an efficient scheduling of the washing operations to reduce flow time and work-in-process inventories is of great concern to management. In the washing step, different sets of reusable medical devices may be washed together as long as the washer capacity is not exceeded. Thus, the washing step is modeled as a batch scheduling problem where washers have nonidentical capacities and reusable medical device sets have different sizes and different ready times. The objective is to minimize the sum of completion times for washing operations. The problem is first formulated as a nonlinear integer programming model. Given that this problem is NP-hard, a genetic algorithm is then proposed to heuristically solve the problem. Computational experiments show that the proposed algorithm is capable of consistently obtaining high-quality solutions in short computation times.

Genetic Algorithm Influenced Top-N Recommender System to Alleviate New User Cold Start Problem

Most recommender systems are based on the familiar collaborative filtering algorithm to suggest items. Quite often, collaborative filtering algorithm fails in generating recommendations due to the lack of adequate user information resulting in new user cold start problem. The cold start problem is one among the prevailing issue in recommendation system where the system fails to render recommendations. To overcome the new user cold start issue, demographical information of the user is utilised as the user information source. Among the demographical information, the impact of the user gender is less explored when compared with other information like age, profession, region, etc. In this work, a genetic algorithm-influenced gender-based top-n recommender algorithm is proposed to address the new user cold start problem. The algorithm utilises the evolution concepts of the genetic algorithm to render top-n recommendations to a new user. The evaluation of the proposed algorithm using real world datasets proved that the algorithm has a better efficiency than the state of art approaches.