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The term selection problem for selecting query terms in information filtering and routing has been investigated using hill‐climbers of various kinds, largely through the Okapi experiments in the TREC series of conferences. Although these are simple deterministic approaches, which examine the effect of changing the weight of one term at a time, they have been shown to improve the retrieval effectiveness of filtering queries in these TREC experiments. Hill‐climbers are, however, likely to get trapped in local optima, and the use of more sophisticated local search techniques for this problem that attempt to break out of these optima are worth investigating. To this end, this paper aims to apply a genetic algorithm (GA) to the same problem.

A standard TREC test collection is used from the TREC‐8 filtering track, recording mean average precision and recall measures to allow comparison between the hill‐climber and GAs. It also varies elements of the GA, such as probability of a word being included, probability of mutation and population size in order to measure the effect of these variables. Different strategies such as elitist and non‐elitist methods are used, as well as roulette wheel and rank selection GAs.

The results of tests suggest that both techniques are, on average, better than the baseline, but, the implemented GA does not match the overall performance of a hill‐climber. The Rank selection algorithm does better on average than the Roulette Wheel algorithm. There is no evidence in this study that varying word inclusion probability, mutation probability or Elitist method make much difference to the overall results. Small population sizes do not appear to be as effective as larger population sizes.

The evidence provided here would suggest that being stuck in a local optima for the term selection optimization problem does not appear to be detrimental to the overall success of the hill‐climber. The evidence from term rank order would appear to provide extra useful evidence, which hill climbers can use efficiently, and effectively, to narrow the search space.

The paper represents the first attempt to compare hill‐climbers with GAs on a problem of this type.

Value-at-risk (VaR) is a risk measure of potential loss on a specific portfolio. The main uses of VaR are in risk management and financial reporting. Researchers are continuously looking for new and efficient ways to evaluate VaR, and the 2008 financial crisis has given further impetus to finding new and reliable ways of evaluating and using VaR. In this study, the authors use genetic algorithm (GA) to evaluate VaR and compare the results with conventional VaR techniques.

In essence, the authors propose two modifications to the standard GA: normalized population selection and strict population selection. For a typical set of simulation, eight chromosomes were used each with eight stored values, and the authors get eight values for VaR.

The experiments using data from four different market indices show that by adjusting the volatility, the VaR computed using GA is more conservative as compared to those computed using Monte Carlo simulation.

The proposed methodology is designed for VaR computation only. This could be generalized for other applications.

This is achieved with much less cost of computation, and hence, the proposed methodology could be a viable practical approach for computing VaR.

The proposed methodology is simple and, at the same time, novel that could have far-reaching impact on practitioners.

The purpose of this paper is to gain trajectory-tracking controllers for autonomous aircraft are optimized using a modified evolutionary, or genetic algorithm (GA).

The GA design utilizes real representation for the individual consisting of the collection of all controller gains subject to tuning. The initial population is generated randomly over pre-specified ranges. Alternatively, initial individuals are produced as random variations from a heuristically tuned set of gains to increase convergence time. A two-point crossover mechanism and a probabilistic mutation mechanism represent the genetic alterations performed on the population. The environment is represented by a performance index (PI) composed of a set of metrics based on tracking error and control activity in response to a commanded trajectory. Roulette-wheel selection with elitist strategy are implemented. A PI normalization scheme is also implemented to increase the speed of convergence. A flexible control laws design environment is developed, which can be used to easily optimize the gains for a variety of unmanned aerial vehicle (UAV) control laws architectures.

The performance of the aircraft trajectory-tracking controllers was shown to improve significantly through the GA optimization. Additionally, the novel normalization modification was shown to encourage more rapid convergence to an optimal solution.

The GA paradigm shows much promise in the optimization of highly non-linear aircraft trajectory-tracking controllers. The proposed optimization tool facilitates the investigation of novel control architectures regardless of complexity and dimensionality.

The addition of the evolutionary optimization to the WVU UAV simulation environment enhances significantly its capabilities for autonomous flight algorithm development, testing, and evaluation. The normalization methodology proposed in this paper has been shown to appreciably speed up the convergence of GAs.

The paper provides a flexible generalized framework for UAV control system evolutionary optimization. It includes specific novel structural elements and mechanisms for improved convergence as well as a comprehensive PI for trajectory tracking.

Majority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling.

Cloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption.

The energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.

This paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.

This research paper reports a novel genetic algorithm (GA)‐based approach for reconfiguration of radial distribution networks for real loss minimization and power quality improvement.

A fuzzy controlled GA has been used for efficient reconfiguration of radial distribution systems for loss minimization and power quality improvement. The special features of the proposed algorithm are: an improved chromosome coding/decoding for network representation so as to preserve the radial property without islanding any load after reconfiguration and an efficient convergence characteristics attributed to fuzzy controlled mutation.

The proposed network reconfiguration algorithm is very much effective in arriving at the global optimal solution (minimum loss network structure) because of efficient search of the solution space. Also, no invalid chromosomes are generated in the genetic evolution because of appropriate coding/decoding. The algorithm is found to be very much suitable for real time implementations.

This research paper provides the power distribution engineers with a computationally efficient approach for optimal operation of distribution systems.

The algorithm proposed in this paper is computationally much faster compared to most of the present day mathematical programming approaches for distribution system operation. This makes it very much attractive for online implementations in any radial distribution network.

This paper has proposed a novel chromosome coding/decoding technique for radial distribution system and a fuzzy logic‐based mutation probability controller for efficient search of global solution space to be used in GA‐based optimal operation of radial distribution systems.

Genetic algorithms are a class of non‐deterministic algorithms that derive from Darwinian evolution and that provide good, though not necessarily optimal, solutions to combinatorial problems. We describe their application to the identification of characteristics that occur approximately equifrequently in a database, using two different methods for the creation of the chromosome data structures that lie at the heart of a genetic algorithm. Experiments with files of English and Turkish text suggest that the genetic algorithm developed here can produce results superior to those produced by existing non‐deterministic algorithms; however, the results are inferior to those produced by an existing deterministic algorithm.

This paper provides an introduction to genetic algorithms, a new approach to the investigation of computationally‐intensive problems that may be insoluble using conventional, deterministic approaches. A genetic algorithm takes an initial set of possible starting solutions and then iteratively improves these solutions using operators that are analogous to those involved in Darwinian evolution. The approach is illustrated by reference to several problems in information retrieval.

Medians of a graph have many applications in engineering. Optimal locations for facility centers, distribution of centers and domain decomposition for parallel computation are a few examples of such applications. In this paper, a new ant system (AS) algorithm based on the idea of using two sets of ants, named active and passive ants is proposed for the problem of finding k‐medians of a weighted graph or the facility location problem on a network.

The structure of the algorithm is derived from two known heuristics; namely, rank‐based AS and max‐min ant system with some adjustments in pheromone updating and locating the ants on the graph nodes. The algorithms are designed with and without a local search.

An efficient algorithm for location finding, and the novel application of an ant colony system can be considered as the main contribution of this paper.

Combining two different tools; namely, graph theory and AS algorithm results in an efficient and accurate method for location finding. The results are compared to those of another algorithm based on the theory of graphs.

The purpose of this paper is of two‐fold. First, the authors propose the application of genetic algorithm (GA)‐based heuristic for solving a distribution allocation problem for a three‐stage supply chain with fixed cost. Second, a methodology for parameter design in GA is discussed which can lead to better performance of the algorithm.

A mathematical model is formulated as an integer‐programming problem. The model is solved using GA‐based heuristic and illustrated with a numerical example. An investigation is made for determining the best combination of the parameters of GA using factorial design procedure.

The optimum population size for the selected problem size is found to be 100. The mutation probability for a better solution is 0.30. The objective function value at the above mentioned levels is better than that obtained at the other combinations.

This work provides a good insight about the fixed cost transportation problem (FCTP) in a three‐stage supply chain and design of numerical parameters for GA. The model developed assumes a single product environment in a single period. Hence, the present study can be extended to a multi‐product, multi‐period, and varying demand environment. In the parameter design, three distinct numerical parameters are considered. The parameters, population size and mutation probability are set at four levels and the parameter, crossover probability is set at three levels. More levels can be selected so that more combinations can be experimented.

The paper presents the formulation and solution of a distribution‐allocation problem in a three‐stage supply chain with fixed cost for a transportation route.

The purpose of this paper is to provide an improved genetic algorithm to solve 0/1 multidimensional knapsack problem (0/1 MKP), by proposing new selection and crossover operators that cooperate to explore the search space.

The authors first present a new sexual selection strategy that significantly improves the one proposed by (Varnamkhasti and Lee, 2012), while working in phenotype space. Then they propose two variants of the two-stage recombination operator of (Aghezzaf and Naimi, 2009), while they adapt the latter in the context of 0/1 MKP. The authors evaluate the efficiency of both proposed operators on a large set of 0/1 MKP benchmark instances. The obtained results are compared against that of conventional selection and crossover operators, in terms of solution quality and computing time.

The paper shows that the proposed selection respects the two major factors of any metaheuristic: exploration and exploitation aspects. Furthermore, the first variant of the two-stage recombination operator pushes the search space towards exploitation, while the second variant increases the genetic diversity. The paper then demonstrates that the improved genetic algorithm combining the two proposed operators is a competitive method for solving the 0/1 MKP.

Although only 0/1 MKP standard instances were tested in the empirical experiments in this paper, the improved genetic algorithm can be used as a powerful tool to solve many real-world applications of 0/1 MKP, as the latter models several industrial and investment issues. Moreover, the proposed selection and crossover operators can be incorporated into other bio-inspired algorithms to improve their performance. Furthermore, the two proposed operators can be adapted to solve other binary combinatorial optimization problems.

This research study provides an effective solution for a well-known non-deterministic polynomial-time (NP)-hard combinatorial optimization problem; that is 0/1 MKP, by tackling it with an improved genetic algorithm. The proposed evolutionary mechanism is based on two new genetic operators. The first proposed operator is a new and deeply different variant of the so-called sexual selection that has been rarely addressed in the literature. The second proposed operator is an adaptation of the two-stage recombination operator in the 0/1 MKP context. This adaptation results in two variants of the two-stage recombination operator that aim to improve the quality of encountered solutions, while taking advantage of the sexual selection criteria to prevent the classical issue of genetic algorithm that is premature convergence.