scholarly journals Linkage-Based Distance Metric in the Search Space of Genetic Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Yong-Hyuk Kim ◽  
Yourim Yoon
Keyword(s):  
Genetic Algorithms ◽  
Metric Space ◽  
Gene Rearrangement ◽  
Distance Measure ◽  
Gene Interaction ◽  
Search Space ◽  
Interaction Graph ◽  
Distance Metric ◽  
First Order ◽  
Linkage Of Genes

We propose a new distance metric, based on the linkage of genes, in the search space of genetic algorithms. This second-order distance measure is derived from the gene interaction graph and first-order distance, which is a natural distance in chromosomal spaces. We show that the proposed measure forms a metric space and can be computed efficiently. As an example application, we demonstrate how this measure can be used to estimate the extent to which gene rearrangement improves the performance of genetic algorithms.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

The Nesting of Two-Dimensional Shapes Using Genetic Algorithms

1995 ◽  
Vol 209 (2) ◽  
pp. 115-124 ◽  
Author(s):  
H S Ismail ◽  
K K B Hon
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Process ◽  
Search Space ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Spatial Constraints ◽  
Global Optimal ◽  
Optimum Solution ◽  
Rapid Conversion

The general two-dimensional cutting stock problem is concerned with the optimum layout and arrangement of two-dimensional shapes within the spatial constraints imposed by the cutting stock. The main objective is to maximize the utilization of the cutting stock material. This paper presents some of the results obtained from applying a combination of genetic algorithms and heuristic approaches to the nesting of dissimilar shapes. Genetic algorithms are stochastically based optimization approaches which mimic nature's evolutionary process in finding global optimal solutions in a large search space. The paper discusses the method by which the problem is defined and represented for analysis and introduces a number of new problem-specific genetic algorithm operators that aid in the rapid conversion to an optimum solution.

scholarly journals Analog Circuit Design Automation Tool Using Genetic Algorithms

1970 ◽  
Vol 1 (1) ◽  
Author(s):  
Y. M. A. Khalifa ◽  
D. H. Horrocks
Keyword(s):  
Genetic Algorithms ◽  
Circuit Design ◽  
Analog Circuits ◽  
Computer Aided Design ◽  
Degrees Of Freedom ◽  
Analog Circuit ◽  
Search Space ◽  
Design Stage ◽  
Specific Frequency ◽  
Aided Design

An investigation into the application of Genetic Algorithms (GA) for the design of electronic analog circuits is presented in this paper. In this paper an investigation of the use of genetic algorithms into the problem of analog circuits design is presented. In a single design stage, circuits are produced that satisfy specific frequency response specifications using circuit structures that are unrestricted and with component values that are chosen from a set of preferred values. The extra degrees of freedom resulting from unbounded circuit structures create a huge search space. It is shown in this paper that Genetic Algorithms can be successfully used to search this space. The application chosen is a LC all pass ladder filter circuit design.Key Words: Computer-Aided Design, Analog Circuits, Artificial Intelligence.

scholarly journals Probabilistic Inference for Predicate Constraint Satisfaction

2020 ◽  
Vol 34 (02) ◽  
pp. 1644-1651
Author(s):  
Yuki Satake ◽  
Hiroshi Unno ◽  
Hinata Yanagi
Keyword(s):  
Graphical Models ◽  
Constraint Satisfaction ◽  
Linear Time ◽  
Predicate Logic ◽  
Probabilistic Inference ◽  
Search Space ◽  
Constraint Satisfaction Problems ◽  
Safety Properties ◽  
First Order ◽  
Guided Inductive

In this paper, we present a novel constraint solving method for a class of predicate Constraint Satisfaction Problems (pCSP) where each constraint is represented by an arbitrary clause of first-order predicate logic over predicate variables. The class of pCSP properly subsumes the well-studied class of Constrained Horn Clauses (CHCs) where each constraint is restricted to a Horn clause. The class of CHCs has been widely applied to verification of linear-time safety properties of programs in different paradigms. In this paper, we show that pCSP further widens the applicability to verification of branching-time safety properties of programs that exhibit finitely-branching non-determinism. Solving pCSP (and CHCs) however is challenging because the search space of solutions is often very large (or unbounded), high-dimensional, and non-smooth. To address these challenges, our method naturally combines techniques studied separately in different literatures: counterexample guided inductive synthesis (CEGIS) and probabilistic inference in graphical models. We have implemented the presented method and obtained promising results on existing benchmarks as well as new ones that are beyond the scope of existing CHC solvers.

Deep learning for extracting water body from Sentinel-2 MSI imagery

2021 ◽  
Author(s):  
Shuren Chou
Keyword(s):  
Deep Learning ◽  
Distance Measure ◽  
Accuracy Assessment ◽  
Feature Learning ◽  
Search Space ◽  
Water Bodies ◽  
Detection Accuracy ◽  
Simple Linear Iterative Clustering ◽  
Clustering Center ◽  
Sentinel 2

<p>Deep learning has a good capacity of hierarchical feature learning from unlabeled remote sensing images. In this study, the simple linear iterative clustering (SLIC) method was improved to segment the image into good quality super-pixels. Then, we used the convolutional neural network (CNN) to extract of water bodies from Sentinel-2 MSI data using deep learning technique. In the proposed framework, the improved SLIC method obtained the correct water bodies boundary by optimizing the initial clustering center, designing a dynamic distance measure, and expanding the search space. In addition, it is different from traditional extraction of water bodies methods that cannot achieve multi-level water bodies detection. Experimental results showed that this method had higher detection accuracy and robustness than other methods. This study was able to extract water bodies from remotely sensed images with deep learning and to conduct accuracy assessment.</p>

Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm

2021 ◽  
Author(s):  
Che-Hang Cliff Chan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Parameters Identification ◽  
Experimental Results ◽  
Adaptive Search ◽  
Real Coded Genetic Algorithm ◽  
Hysteresis Modeling ◽  
Convergence Performance ◽  
Solution Accuracy

The thesis presents a Genetic Algorithm with Adaptive Search Space (GAASS) proposed to improve both convergence performance and solution accuracy of traditional Genetic Algorithms(GAs). The propsed GAASS method has bee hybridized to a real-coded genetic algorithm to perform hysteresis parameters identification and hystereis invers compensation of an electromechanical-valve acuator installed on a pneumatic system. The experimental results have demonstrated the supreme performance of the proposed GAASS in the search of optimum solutions.

Genetic Algorithms for Decision-Making in Cognitive Radio Networks

2013 ◽  
pp. 247-263
Author(s):  
Tommy Hult ◽  
Abbas Mohammed
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Cognitive Radio ◽  
Search Space ◽  
Radio Spectrum ◽  
Dynamic Decision Making ◽  
Optimal Point ◽  
Optimization Search

Efficient use of the available licensed radio spectrum is becoming increasingly difficult as the demand and usage of the radio spectrum increases. This usage of the spectrum is not uniform within the licensed band but concentrated in certain frequencies of the spectrum while other parts of the spectrum are inefficiently utilized. In cognitive radio environments, the primary users are allocated licensed frequency bands while secondary cognitive users dynamically allocate the empty frequencies within the licensed frequency band according to their requested QoS (Quality of Service) specifications. This dynamic decision-making is a multi-criteria optimization problem, which the authors propose to solve using a genetic algorithm. Genetic algorithms traverse the optimization search space using a multitude of parallel solutions and choosing the solution that has the best overall fit to the criteria. Due to this parallelism, the genetic algorithm is less likely than traditional algorithms to get caught at a local optimal point.

Genetic Algorithms and Multimodal Search

2011 ◽  
pp. 231-249
Author(s):  
Marcos Gestal ◽  
José Manuel Vázquez Naya ◽  
Norberto Ezquerra
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Computation ◽  
Unique Solution ◽  
Real World ◽  
Multiple Solutions ◽  
Global Solutions ◽  
Search Space ◽  
The Real ◽  
Local Solutions ◽  
Multimodal Problems

Traditionally, the Evolutionary Computation (EC) techniques, and more specifically the Genetic Algorithms (GAs), have proved to be efficient when solving various problems; however, as a possible lack, the GAs tend to provide a unique solution for the problem on which they are applied. Some non global solutions discarded during the search of the best one could be acceptable under certain circumstances. Most of the problems at the real world involve a search space with one or more global solutions and multiple local solutions; this means that they are multimodal problems and therefore, if it is desired to obtain multiple solutions by using GAs, it would be necessary to modify their classic functioning outline for adapting them correctly to the multimodality of such problems. The present chapter tries to establish, firstly, the characterisation of the multimodal problems will be attempted. A global view of some of the several approaches proposed for adapting the classic functioning of the GAs to the search of mu ltiple solutions will be also offered. Lastly, the contributions of the authors and a brief description of several practical cases of their performance at the real world will be also showed.

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

2021 ◽  
pp. 1074-1115
Author(s):  
Victer Paul ◽  
Ganeshkumar C ◽  
Jayakumar L
Keyword(s):  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Computation Time ◽  
Search Space ◽  
Population Based ◽  
Vital Role ◽  
Traveling Salesman ◽  
Performance Criteria

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.

Evolutionary Modeling of Routes: The Case of Road Design

2001 ◽  
Author(s):  
Ângela Guimarães Pereira
Keyword(s):  
Genetic Algorithms ◽  
Physical Space ◽  
Linear Structure ◽  
Search Space ◽  
Evaluation Methodology ◽  
Mitigation Measures ◽  
Evolutionary Modeling ◽  
Technical Parameters ◽  
The Road ◽  
Road Design

In this study a route is defined as the path that a linear structure or facility follows in the terrain. Linear structures comprise facilities such as roads, motorways, railways, pipelines, electrical power lines, and telephone cables, each of these structures requiring specific technical parameters in what concerns the geometry of the path and having different effects on the terrain they traverse. Amongst these structures, roads and motorways are the group that creates the greatest overall impact; accordingly Portuguese legislation requires an environmental impact assessment (EIA) process as part of the necessary licensing approval. Usually the alternative (or alternatives) that undergo the EIA process is justified in terms of technical and economical issues. The result is that if major environmental impacts are identified by the EIA study, a myriad of mitigation measures are proposed, very seldom the redesign of the path being carried out (Guimarães Pereira & Antunes, 1996). Preliminary studies that precede the implementation of these types of projects are technically detailed and often come together with economical feasibility studies, shelving environmental issues for later assessment. In the methodology proposed in this chapter a multidimensional evaluation methodology, multicriteria evaluation, will be combined with the robustness of a search methodology, genetic algorithms (GAs) to generate alternative road routes that take into consideration environmental, economical, technical, and social criteria. These criteria are referenced to the physical space where the road is to be placed and therefore this methodology is embedded into a geographic information system (GIS). Genetic algorithms are particularly attractive to apply to multi-modal problems, allowing the exploration of spatial features to eventually find “best compromise” alternatives because these algorithms proceed their search by maintaining a population of solutions, that they can simultaneously exploit for their efficiency.1 Moreover, the particular mixing mechanism provides the means to recombine solutions and explore the search space. The remainder of this chapter describes evolutionary modeling of road routes, in particular the coding onto a GA of the geometric algorithm that accounts for the technical aspects of motorway siting. The details of the implementation of the MCDA-GA methodology, running within the GIS GRASS 4.1 (Geographic Resources Analysis Support System) and its application to generate and evaluate alternative routes of a section of a Portuguese complementary itinerary (IC7) will be presented.

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