Genetic Search Methods Research Articles

A memetic algorithm for the inventory routing problem

In this article, we study an Inventory Routing Problem with deterministic customer demand in a two-tier supply chain. The supply chain network consists of a supplier using a single vehicle with a given capacity to deliver a single product type to multiple customers. We are interested in population-based algorithms to solve our problem. A Memetic Algorithm (MA) is developed based on the Genetic Algorithm (GA) and Variable Neighborhood Search methods. The proposed meta-heuristics are tested on small and large reference benchmarks. The results of the MA are compared to those of the classical GA and to the optimal solutions in the literature. The comparison shows the efficiency of using MA and its ability to generate high quality solutions in a reasonable computation time.

Comparison of the genetic algorithm and pattern search methods for forecasting optimal flow releases in a multi-storage system for flood control

This paper compares the well-known genetic algorithm (GA) and pattern search (PS) optimization methods for forecasting optimal flow releases in a multi-storage system for flood control. The simulation models used by the optimization models include (a) a batch of scripts for data acquisition of forecasted precipitation and their automated post-processing; (b) a hydrological model for rainfall-runoff conversion, and (c) a hydraulic model for simulating river inundation. This paper focuses on (1) demonstrating the application of the framework by applying it to the operation of a hypothetical eight-wetland system in the Cypress Creek watershed in Houston, Texas; and (2) comparing and discussing the performance of the two optimization methods under consideration. The results show that the GA and PS optimal solutions are very similar; however, the computational time required by PS is significantly shorter than that required by GA. The results also show that optimal dynamic water management can significantly mitigate flooding compared to the case without management.

Utilizing multi-criteria decision to determine the best location for the ecotourism in the east and central of Fars province, Iran

To reduce the negative effects of tourism on the environment, the importance of ecotourism is increasingly considered because this form of tourism helps to protect the environment and sustainable development of an area. So, it is important to determine suitable places for tourism to better manage the study area. The aim of this study is to identify potential ecotourism sites using ordered weight averaging (OWA) and fuzzy quantifier algorithms in the east and central of Fars province, Iran. Required spatial data such as geology, soil, slope land, topographic roughness index (TRI), vegetation, surface water, elevation, protected area, climate, distance to road, and distance to the village were utilized. To prepare ecotourism maps with different confidence levels, eleven ordered weights were applied corresponding to the eleven parameters that were rank-ordered for each parameter after the modified factor weights were applied. Also, the feature selection algorithm (random search and genetic search methods) was used to select the most important parameters to determine the ecotourism map. The results showed that, with decreasing risk (α = 0), almost all of the study area was unsuitable for ecotourism while, with increasing risk (α = 20), all of the study areas were suitable for ecotourism. One of the ecotourism maps prepared with different confidence levels can be suggested based on the different conditions of tourists so that, if the tourist has a limited time, ecotourism maps with a higher degree of confidence levels are recommended and vice versa. This is one of the innovations of the present research. Also, the results of the random search method with the least error show that slope, elevation, climate, distance to river, and distance to road parameters are the most important parameters in preparing the ecotourism map of the region. So, using the results of the research, many economic problems, such as unemployment, will be solved by managers by preparing tourism maps and creating service jobs for tourists. With the OWA method, tourism zoning maps can be controlled by managers in order to provide better services and social and economic justice.

Feasibility study of genetic algorithms application for implementation of block cryptosystem cryptanalysis

Feasibility of genetic search algorithms application for implementation of the cryptanalysis of block cipher methods is considered. A distinctive feature of the bioinspired cryptanalysis methods application (in particular, genetic methods) is the possibility of using the encryption (or decryption) algorithm as an objective function for the suitability evaluation of the key defined by genetic operations. Consequently, when using the bioinspired cryptanalysis methods, the S key definition (for example, when using type 2 cryptanalysis) depends not so much on the complexity of the ciphering transformations, as on the bioinspired method which is to provide a sufficient variety of key generation that shows the significance of the research task of the bioinspired algorithms feasibility (in particular, genetic search methods) for the block cryptosystem cryptanalysis. It is noted also that as the distinctive feature of both block cipher methods, and the genetic algorithms is their internal parallelism, then the task of developing a cryptanalysis algorithm based on the parallel implementation of the constituent stages is relevant. An algorithm of the block methods cryptanalysis on the example of the DES standard on the basis of its parallel version is offered; the experiment results of the quasioptimal key determination obtained at the parallel algorithm implementation on the 8-letter text blocks are given. It is noted that time costs of the algorithm realization do not exceed the time of the known cryptanalysis implementation.

Crater detection via genetic search methods to reduce image features

Recent approaches to crater detection have been inspired by face detection’s use of gray-scale texture features. Using gray-scale texture features for supervised machine learning crater detection algorithms provides better classification of craters in planetary images than previous methods. When using Haar features it is typical to generate thousands of numerical values from each candidate crater image. This magnitude of image features to extract and consider can spell disaster when the application is an entire planetary surface. One solution is to reduce the number of features extracted and considered in order to increase accuracy as well as speed. Feature subset selection provides the operational classifiers with a concise and denoised set of features by reducing irrelevant and redundant features. Feature subset selection is known to be NP-hard. To provide an efficient suboptimal solution, four genetic algorithms are proposed to use greedy selection, weighted random selection, and simulated annealing to distinguish discriminate features from indiscriminate features. Inspired by analysis regarding the relationship between subset size and accuracy, a squeezing algorithm is presented to shrink the genetic algorithm’s chromosome cardinality during the genetic iterations. A significant increase in the classification performance of a Bayesian classifier in crater detection using image texture features is observed.

Mixed-Integer Constrained Optimization Based on Memetic Algorithm

Evolutionary algorithms (EAs) are population-based global search methods. They have been successfully applied tomany complex optimization problems. However, EAs are frequently incapable of finding a convergence solution indefault of local search mechanisms. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators withlocal search methods. With global exploration and local exploitation in search space, MAs are capable of obtainingmore high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-basedsearch algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, amemetic algorithm based on MIHDE is developed for solving mixed-integer optimization problems. However, most ofreal-world mixed-integer optimization problems frequently consist of equality and/or inequality constraints. In order toeffectively handle constraints, an evolutionary Lagrange method based on memetic algorithm is developed to solvethe mixed-integer constrained optimization problems. The proposed algorithm is implemented and tested on twobenchmark mixed-integer constrained optimization problems. Experimental results show that the proposed algorithmcan find better optimal solutions compared with some other search algorithms. Therefore, it implies that the proposedmemetic algorithm is a good approach to mixed-integer optimization problems.

PREDICTION FOR TRAFFIC ACCIDENT SEVERITY: COMPARING THE ARTIFICIAL NEURAL NETWORK, GENETIC ALGORITHM, COMBINED GENETIC ALGORITHM AND PATTERN SEARCH METHODS

This paper focuses on predicting the severity of freeway traffic accidents by employing twelve accident-related parameters in a genetic algorithm (GA), pattern search and artificial neural network (ANN) modelling methods. The models were developed using the input parameters of driver's age and gender, the use of a seat belt, the type and safety of a vehicle, weather conditions, road surface, speed ratio, crash time, crash type, collision type and traffic flow. The models were constructed based on 1000 of crashes in total that occurred during 2007 on the Tehran–Ghom Freeway due to the fact that the remaining records were not suitable for this study. The GA evaluated eleven equations to obtain the best one. Then, GA and PS methods were combined using the best GA equation. The neural network used multi-layer perceptron (MLP) architecture that consisted of a multi-layer feed-forward network with hidden sigmoid and linear output neurons that could also fit multi-dimensional mapping problems arbitrarily well. The ANN was applied during training, testing and validation and had 12 inputs, 25 neurons in the hidden layers and 3 neurons in the output layer. The best-fit model was selected according to the R-value, root mean square errors (RMSE), mean absolute errors (MAE) and the sum of square error (SSE). The highest R-value was obtained for the ANN around 0.87, demonstrating that the ANN provided the best prediction. The combination of GA and PS methods allowed for various prediction rankings ranging from linear relationships to complex equations. The advantage of these models is improving themselves adding new data.

Sensitivity analysis and thermo-economic optimisation of Steam Injected Gas Turbine cycle design parameters

The purpose of the present work is to apply thermo-economic concepts to a Steam Injected Gas Turbine (STIG) electricity generation system and optimise it to indicate its economical advantages over a simple gas turbine. The system incorporates a combustion chamber, gas turbine, a compressor and a Heat Recovery Steam Generator (HRSG). A detailed sensitivity analysis was also performed to examine the impacts of the variation of the operating parameters. Genetic algorithm and direct search methods were used to find optimum values. The system is optimised, using thermo-economic concepts. The results demonstrate that the steam injected system shows a better thermodynamic and thermo-economic performance.

Applying Heuristic Search for Distributed Software Performance Enhancement

Software reverse engineering and reengineering techniques are most often applied to reconstruct the software archi-tecture with respect to quality constraints, or non-functional requirements such as maintainability or reusability. In this paper, the performance improvement of distributed software is modeled as a search problem that is solved by heuristic search algorithms such as genetic search methods. To achieve this, firstly, all aspects of the distributed execution of a software is specified by an analytical performance evaluation function that not only evaluates the current deployment of the software from the performance perspective but also can be applied to propose the near-optimal object deploy-ment for that software. This analytical function is applied as the Heuristic search objective function. In this paper a novel statement reordering method is also presented which is used to generate the search objective function such that the best solution in the search space can be found.

A new approach for solving linear bilevel problems using genetic algorithms

Bilevel programming involves two optimization problems where the constraint region of the first level problem is implicitly determined by another optimization problem. This paper develops a genetic algorithm for the linear bilevel problem in which both objective functions are linear and the common constraint region is a polyhedron. Taking into account the existence of an extreme point of the polyhedron which solves the problem, the algorithm aims to combine classical extreme point enumeration techniques with genetic search methods by associating chromosomes with extreme points of the polyhedron. The numerical results show the efficiency of the proposed algorithm. In addition, this genetic algorithm can also be used for solving quasiconcave bilevel problems provided that the second level objective function is linear.

Knowledge-Based Evolution Method for Optimizing Refrigerant Circuitry of Fin-and-Tube Heat Exchangers

Fin-and-tube heat exchangers are widely used in HVAC applications, and the optimization of the refrigerant circuitry (RC) is important to improve their performance. The genetic algorithm (GA) is a commonly used optimization method but cannot be directly used for RC optimization. This paper presents a new optimization method based on the GA, which is simpler and more effective for RC optimization than existing methods. The optimization method for RC optimization presented in this paper is a knowledge-based evolution method (KBEM) that includes an improved genetic algorithm (IGA) and domain knowledge-based RC search methods. The IGA, as the basis of the KBEM, uses a tailor-made RC coding method, RC generation method, and improved genetic operators. The knowledge-based RC search methods increase the optimization efficiency of the IGA by reasonably reducing the search space according to the domain knowledge. Case studies show that the KBEM can generate RC designs better than those generated by the existing RC optimization methods, and the KBEM is suitable for various types of fin-and-tube heat exchangers.

A three-dof robomech: Architecture, optimum synthesis and introduction to compliant robomechs

A robomech is a multi-input multi-output parallel-drive arm equipped with several end effectors to perform tasks that require the simultaneous execution of coordinated multi functions. A three-dof robomech equipped with three end effectors is herein presented. The proposed robomech can perform a three-function task. The robomech consists of three wings that form two cells, each constitute 5R architecture. As an overconstrained linkage, a robomech cannot traverse continuous trajectories. However, it can be synthesized to move its end effectors through any number of desired locations. This article presents the architecture of the proposed robomech, establishes its kinematic relations and constraints, and provides dimensional synthesis scheme based on genetic algorithm, gradient search, and least-square optimization methods. Additionally, a compliant robomech based on the pseudo-rigid-body model (PRBM) is introduced. A case study of an 8-position synthesis task is provided.

Conformation of the exopolysaccharide of Burkholderia cepacia predicted with molecular mechanics (MM3) using genetic algorithm search

We present a computational conformational analysis of the exopolysaccharide of Burkholderia cepacia, which is believed to play a role in colonization and persistence of B. cepacia in the lungs of cystic fibrosis patients. The repeating unit of the exopolysaccharide is a heptasaccharide with three branches, which cause significant steric restraints. Conformational searches using glygal, an in-house developed software using genetic algorithm search methods, were performed on fragments as well as on the complete repeating unit with wrap-over residues. The force field used for the calculations was MM3(96). The search showed four favored conformations for an isolated repeating unit. However, for a sequence of several repeating units, the calculations indicate a single, well-defined linear conformation.

Explanatory optimization of protein mass spectrometry via genetic search.

Optimizing experimental conditions for the effective analysis of intact proteins by mass spectrometry is challenging, as many analytical factors influence the spectral quality, often in very different ways for different proteins and especially with complex protein mixtures. We show that genetic search methods are highly effective in this kind of optimization and that it was possible in 6 generations with a total of <500 experiments out of some 10(14) to find good combinations of experimental variables (electrospray ionization mass spectral settings) that would not have been detected by optimizing each variable alone (i.e., the search space is epistatic). Moreover, by inspecting the evolution of the variables to be optimized using genetic programming, we discovered an important relationship between two of the mass spectrometer settings that accounts for much of this success. Specifically, the conditions that were evolved included very low values of skimmer 1 voltage (the sample cone) and a skimmer 2 voltage (extraction cone) above a threshold that would nevertheless minimize the potential difference between the sample and extraction skimmers. The discovery of this relationship demonstrates the hypothesis-generating ability of genetic search in optimization processes where the size of the search space means that little or no a priori knowledge of the optimal conditions is available.

Genetic search methods in air traffic control

Of primary importance to the efficient operation and profitability of an airline is adherence to its flight schedule. This paper examines that segment of air traffic control, termed traffic management adviser (TMA), which is charged with the complex task of scheduling arriving aircraft to available runways in a manner that minimizes delays and satisfies safety constraints. In particular, we investigate the effectiveness and efficiency of using genetic search methods to support the scheduling decisions made by TMA. Four different genetic search methods are tested on TMA problems suggested by recent work at the NASA Ames Research Center. For problems of realistic size, optimal or near-optimal assignments of aircraft to runways are achieved in real time. Scope and purpose This paper reports on the application of genetic search algorithms to solve certain complexities associated with air traffic control. Air traffic control is an important practical problem that is difficult to solve by other methods because of non-convex, non-linear, or non-analytic characteristics. Four genetic search algorithms are applied, with consistent advantage being demonstrated by an algorithm based on genetic programming functions. Good results are achieved, with evidence that solutions can be achieved in real time.

Optimization of building thermal design and control by multi-criterion genetic algorithm

The design of buildings is a multi-criterion optimization problem, there always being a trade-off to be made between capital expenditure, operating cost, and occupant thermal comfort. This paper investigates the application of a multi-objective genetic algorithm (MOGA) search method in the identification of the optimum pay-off characteristic between the energy cost of a building and the the occupant thermal discomfort. Results are presented for the pay-off characteristics between energy cost and zone thermal comfort, for three design days and three building weights. Inspection of the solutions indicates that the MOGA is able to find the optimum pay-off characteristic between the daily energy cost and zone thermal comfort. It can be concluded that multi-criterion genetic algorithm search methods offer great potential for the identification of the pay-off between the elements of building thermal design, and as such can help inform the building design process.

Genetic and Random Search Methods in Optimal Shape Design Problems

We describe the application of two global optimization methods, namely of genetic and random search type algorithms in shape optimization. When the so-called fictitious domain approaches are used for the numerical realization of state problems, the resulting minimized function is non-differentiable and stair-wise, in general. Such complicated behaviour excludes the use of classical local methods. Specific modifications of the above-mentioned global methods for our class of problems are described. Numerical results of several model examples computed by different variants of genetic and random search type algorithms are discussed.

Adaptive image segmentation using genetic and hybrid search methods

This paper describes an adaptive approach for the important image processing problem of image segmentation that relies on learning from experience to adapt and improve the segmentation performance. The adaptive image segmentation system incorporates a feedback loop consisting of a machine learning subsystem, an image segmentation algorithm, and an evaluation component which determines segmentation quality. The machine learning component is based on genetic adaptation and uses (separately) a pure genetic algorithm (GA) and a hybrid of GA and hill climbing (HC). When the learning subsystem is based on pure genetics, the corresponding evaluation component is based on a vector of evaluation criteria. For the hybrid case, the system employs a scalar evaluation measure which is a weighted combination of the different criteria. Experimental results for pure genetic and hybrid search methods are presented using a representative database of outdoor TV imagery. The multiobjective optimization demonstrates the ability of the adaptive image segmentation system to provide high quality segmentation results in a minimal number of generations. >

Genetic search methods for assessing geometric tolerances

This paper investigates computational methods for assessing tolerance specifications of geometric features in a context of computer aided inspection. It is concerned with checking the sampled features for containment within tolerance zones specified at the design stage, not with explicit shape measurement. The approach is based on assessing the feasibility of sets of geometric constraints. Genetic search methods are explored as a possible alternative to formal non-linear programming and guidelines for their efficient use for this problem are proposed. These methods are then compared and contrasted with formal methods, particularly generalised reduced gradient (GRG) methods. GRG appears to have the most favourable combination of good and poor features for the simpler test data, but there is some evidence that genetic search might be relatively more efficient for more complex inspection problems.

Genetic search strategies in multicriterion optimal design

The present paper describes an implementation of genetic search methods in multicriterion optimal designs of structural systems with a mix of continuous, integer and discrete design variables. Two distinct strategies to simultaneously generate a family of Pareto optimal designs are presented in the paper. These strategies stem from a consideration of the natural analogue, wherein distinct species of life forms share the available resources of an environment for sustenance. The efficacy of these solution strategies are examined in the context of representative structural optimization problems with multiple objective criteria and with varying dimensionality as determined by the number of design variables and constraints.