Matching results of CAGA and CGA on the dot patterns in Figure 2. The following data have been summarized over 50 runs for each DP.
\\n\\n
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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\n\n\n\n\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"10257",leadTitle:null,fullTitle:"Engineering Applications of Diamond",title:"Engineering Applications of Diamond",subtitle:null,reviewType:"peer-reviewed",abstract:"Diamond offers many advantages over other wide-bandgap materials and thus is a very important material in engineering applications. It can be used in high-speed electronics and response systems as well as high-power laser windows, protective coatings, electrochemical sensors, and more. This book examines the properties, advantages, and potential applications of diamonds in engineering and other fields.",isbn:"978-1-83968-532-3",printIsbn:"978-1-83968-531-6",pdfIsbn:"978-1-83968-533-0",doi:"10.5772/intechopen.91098",price:119,priceEur:129,priceUsd:155,slug:"engineering-applications-of-diamond",numberOfPages:120,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"6062b591f89e8bdbbd8e7f59c0d366a1",bookSignature:"Awadesh Mallik",publishedDate:"August 18th 2021",coverURL:"https://cdn.intechopen.com/books/images_new/10257.jpg",numberOfDownloads:2390,numberOfWosCitations:0,numberOfCrossrefCitations:2,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:5,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:7,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"August 25th 2020",dateEndSecondStepPublish:"September 22nd 2020",dateEndThirdStepPublish:"November 21st 2020",dateEndFourthStepPublish:"February 9th 2021",dateEndFifthStepPublish:"April 10th 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"178218",title:"Dr.",name:"Awadesh",middleName:null,surname:"Mallik",slug:"awadesh-mallik",fullName:"Awadesh Mallik",profilePictureURL:"https://mts.intechopen.com/storage/users/178218/images/system/178218.jpg",biography:"Dr. Awadesh Kumar Mallik is a ceramic engineer from the University of Calcutta, India, who is currently a materials researcher. He started growing CVD diamond at the Indian Institute of Science Bangalore laboratory during his master’s thesis project for the development of vacuum tribology coatings for the Indian Space Research Organisation. He continued his scientific journey of making ceramic components for high-power electron tubes at the Council of Scientific and Industrial Research laboratory CGCRI as part of a national mission for the international fusion energy ITER program. He has executed and delivered several national and international projects. His research interests include diamond materials, thin films and coatings, CVD, PVD, tribology, bioceramics, ceramic membranes, fuel cells and batteries, ceramic powder processing, and materials science in general. He obtained a Ph.D. graduate from Jadavpur University, India, and completed an FWO postdoctoral research fellowship at Hasselt University, Belgium. He has published forty peer-reviewed articles and presented many papers at international conferences.",institutionString:"Hasselt University and IMEC VZW",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"University of Hasselt",institutionURL:null,country:{name:"Belgium"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"14",title:"Materials Science",slug:"materials-science"}],chapters:[{id:"75512",title:"Introductory Chapter: Engineering Applications of Diamond",doi:"10.5772/intechopen.96659",slug:"introductory-chapter-engineering-applications-of-diamond",totalDownloads:745,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:null,signatures:"Awadesh Kumar Mallik",downloadPdfUrl:"/chapter/pdf-download/75512",previewPdfUrl:"/chapter/pdf-preview/75512",authors:[{id:"178218",title:"Dr.",name:"Awadesh",surname:"Mallik",slug:"awadesh-mallik",fullName:"Awadesh Mallik"}],corrections:null},{id:"74938",title:"Novel Magnetic-Sensing Modalities with Nitrogen-Vacancy Centers in Diamond",doi:"10.5772/intechopen.95267",slug:"novel-magnetic-sensing-modalities-with-nitrogen-vacancy-centers-in-diamond",totalDownloads:672,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In modern-day quantum metrology, quantum sensors are widely employed to detect weak magnetic fields or nanoscale signals. Quantum devices, exploiting quantum coherence, are inevitably connected to physical constants and can achieve accuracy, repeatability, and precision approaching fundamental limits. As a result, these sensors have shown utility in a wide range of research domains spanning both science and technology. A rapidly emerging quantum sensing platform employs atomic-scale defects in crystals. In particular, magnetometry using nitrogen-vacancy (NV) color centers in diamond has garnered increasing interest. NV systems possess a combination of remarkable properties, optical addressability, long coherence times, and biocompatibility. Sensors based on NV centers excel in spatial resolution and magnetic sensitivity. These diamond-based sensors promise comparable combination of high spatial resolution and magnetic sensitivity without cryogenic operation. The above properties of NV magnetometers promise increasingly integrated quantum measurement technology, as a result, they have been extensively developed with various protocols and find use in numerous applications spanning materials characterization, nuclear magnetic resonance (NMR), condensed matter physics, paleomagnetism, neuroscience and living systems biology, and industrial vector magnetometry. In this chapter, NV centers are explored for magnetic sensing in a number of contexts. In general, we introduce novel regimes for magnetic-field probes with NV ensembles. Specifically, NV centers are developed for sensitive magnetometers for applications where microwaves (MWs) are prohibitively invasive and operations need to be carried out under zero ambient magnetic field. The primary goal of our discussion is to improve the utility of these NV center-based magnetometers.",signatures:"Huijie Zheng, Arne Wickenbrock, Georgios Chatzidrosos, Lykourgos Bougas, Nathan Leefer, Samer Afach, Andrey Jarmola, Victor M. Acosta, Jingyan Xu, Geoffrey Z. Iwata, Till Lenz, Zhiyin Sun, Chen Zhang, Takeshi Ohshima, Hitoshi Sumiya, Kazuo Nakamura, Junichi Isoya, Jörg Wrachtrup and Dmitry Budker",downloadPdfUrl:"/chapter/pdf-download/74938",previewPdfUrl:"/chapter/pdf-preview/74938",authors:[{id:"335289",title:"Dr.",name:"Huijie",surname:"Zheng",slug:"huijie-zheng",fullName:"Huijie Zheng"},{id:"339213",title:"Dr.",name:"Arne",surname:"Wickenbrock",slug:"arne-wickenbrock",fullName:"Arne Wickenbrock"},{id:"339231",title:"Prof.",name:"Dmitry",surname:"Budker",slug:"dmitry-budker",fullName:"Dmitry Budker"}],corrections:null},{id:"76772",title:"Unique Surface Modifications on Diamond Thin Films",doi:"10.5772/intechopen.98186",slug:"unique-surface-modifications-on-diamond-thin-films",totalDownloads:244,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Diamond thin films are touted to be excellent in surface-sensitive sensing, electro-mechanical systems, and electrochemical applications. However, these applications often entail patterned active surfaces and subtle chemical surface modifications. But due to diamond’s intrinsic hardness and chemical inertness, surface patterning (using micro-machining and ion etching) and chemical surface modifications, respectively, are very difficult. In the case of surface patterning, it is even more challenging to obtain patterns during synthesis. In this chapter, the direct patterning of sub-wavelength features on diamond thin film surface using a femtosecond laser, rapid thermal annealing as a means to prepare the diamond thin film surface as an efficient direct charge transfer SERS substrate (in metal/insulator/semiconductor (MIS) configuration), and implantation of 14N+ ions into the surface and sub-surface regions for enhancing the electrical conductivity of diamond thin film to a certain depth (in MIS configuration) will be discussed encompassing the processing strategies and different post-processing characteristics.",signatures:"Vadali Venkata Satya Siva Srikanth",downloadPdfUrl:"/chapter/pdf-download/76772",previewPdfUrl:"/chapter/pdf-preview/76772",authors:[{id:"345849",title:"Dr.",name:"Vadali",surname:"Venkata Satya Siva Srikanth",slug:"vadali-venkata-satya-siva-srikanth",fullName:"Vadali Venkata Satya Siva Srikanth"}],corrections:null},{id:"75437",title:"Laser Treatment CVD Diamond Coated Punch for Ultra-Fine Piercing of Metallic Sheets",doi:"10.5772/intechopen.96446",slug:"laser-treatment-cvd-diamond-coated-punch-for-ultra-fine-piercing-of-metallic-sheets",totalDownloads:291,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"CVD-diamond coated special tools have been widely utilized to prolong their tool life in practical production lines. WC (Co) punch for fine piercing of metallic sheets required for high wear-toughness to be free from chipping and damages and for high product quality to punch out the holes with sufficient dimensional accuracy. The laser trimming process was developed to reduce the surface roughness of diamond coating down to submicron level and to adjust its diamond layer dimensions with a sharp punch edge for accurate piercing. The pulsed laser irradiation was employed to demonstrate that micro-groove was accurately formed into the diamond coating. Less deterioration in the worked diamond film by this laser treatment was proved by the Raman spectroscopy. The femtosecond laser trimming was proposed to sharpen the punch edge down to 2 μm and to form the nano-textured punch side surfaces with the LIPSS (Laser Induced Periodic Surface Structuring)-period of 300 nm. Fine piercing experiments were performed to demonstrate that punch life was significantly extended to continuous punching in more than 10,000 shots and that mirror-shining hole surfaces were attained in every shot by regularly coining the nanotextures. The sharp punch edge with homogeneous edge profile was responsible for reduction of the induced damages into work sheet by piercing. The punch life was extended by the ejection mechanism of debris particles through the nanotextures on the punch side surface. The present laser treatment was useful in trimming and nanostructuring the complex-shaped punch edge for industrial application.",signatures:"Tatsuhiko Aizawa, Tadahiko Inonara, Tomoaki Yoshino, Tomomi Shiratori and Yohei Suzuki",downloadPdfUrl:"/chapter/pdf-download/75437",previewPdfUrl:"/chapter/pdf-preview/75437",authors:[{id:"251217",title:"Prof.",name:"Tatsuhiko",surname:"Aizawa",slug:"tatsuhiko-aizawa",fullName:"Tatsuhiko Aizawa"},{id:"289331",title:"Mr.",name:"Tadahiko",surname:"Inohara",slug:"tadahiko-inohara",fullName:"Tadahiko Inohara"},{id:"313724",title:"Prof.",name:"Tomomi",surname:"Shiratori",slug:"tomomi-shiratori",fullName:"Tomomi Shiratori"},{id:"330071",title:"Mr.",name:"Tomoaki",surname:"Yoshino",slug:"tomoaki-yoshino",fullName:"Tomoaki Yoshino"},{id:"330072",title:"Mr.",name:"Yohei",surname:"Suzuki",slug:"yohei-suzuki",fullName:"Yohei Suzuki"}],corrections:null},{id:"74304",title:"Diamond-Like Carbon (DLC) Coatings for Automobile Applications",doi:"10.5772/intechopen.95063",slug:"diamond-like-carbon-dlc-coatings-for-automobile-applications",totalDownloads:438,totalCrossrefCites:1,totalDimensionsCites:4,hasAltmetrics:1,abstract:"Diamond-like carbon (DLC) coatings are amorphous carbon material which exhibits typical properties of diamond such as hardness and low coefficient of friction, characterized based on the sp3 bonded carbon and structure. The proportion of sp2 (graphetically) and sp3 (diamond-like) determines the properties of the DLC. This coating can be applied to automobile engine component in an attempt to provide energy efficiency by reducing friction and wear. However, DLC coatings are faced with issues of thermal instability caused by increasing temperature in the combustion engine of a vehicle. Therefore, it became necessary to seek ways of improving this coating to meetup with all tribological requirements that will be able to resist transformational change of the coating as the temperature increases. This chapter discusses the need for diamond-like carbon coatings for automobile engine applications, due to their ultra-low friction coefficient (<0.1) and excellent wear resistance (wear rate ~ 7 x 10−17 m3/N.m). The importance of DLC coatings deposited using PECVD technique, their mechanical and tribological properties at conditions similar to automobile engines would also be discussed. Non-metallic (hydrogen, boron, nitrogen, phosphorus, fluorine and sulfur) or metals (copper, nickel, tungsten, titanium, molybdenum, silicon, chromium and niobium) has been used to improve the thermal stability of DLC coatings. Recently, incorporation of Ag nanoparticles, TiO2 nanoparticles, WO3 nanoparticles and MoO3 nanoparticles into DLC has been used. The novel fabrication of diamond-like carbon coatings incorporated nanoparticles (WO3/MoO3) using PECVD for automobile applications has shown an improvement in the adhesion properties of the DLC coatings. DLC coatings had a critical load of 25 N, while after incorporating with WO3/MoO3 nanoparticles had critical load at 32 N and 39 N respectively.",signatures:"Funsho Olaitan Kolawole, Shola Kolade Kolawole, Luis Bernardo Varela, Adebayo Felix Owa, Marco Antonio Ramirez and André Paulo Tschiptschin",downloadPdfUrl:"/chapter/pdf-download/74304",previewPdfUrl:"/chapter/pdf-preview/74304",authors:[{id:"253841",title:"Dr.",name:"Shola",surname:"Kolawole",slug:"shola-kolawole",fullName:"Shola Kolawole"},{id:"330793",title:"Dr.",name:"Funsho",surname:"Kolawole",slug:"funsho-kolawole",fullName:"Funsho Kolawole"},{id:"330804",title:"Dr.",name:"Luis Bernardo",surname:"Varela",slug:"luis-bernardo-varela",fullName:"Luis Bernardo Varela"},{id:"330841",title:"Prof.",name:"Marco Antonio",surname:"Ramirez",slug:"marco-antonio-ramirez",fullName:"Marco Antonio Ramirez"},{id:"330842",title:"Dr.",name:"Adebayo",surname:"Owa",slug:"adebayo-owa",fullName:"Adebayo Owa"},{id:"330843",title:"Prof.",name:"André Paulo",surname:"Tschiptschin",slug:"andre-paulo-tschiptschin",fullName:"André Paulo Tschiptschin"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"10411",title:"Materials at the Nanoscale",subtitle:null,isOpenForSubmission:!1,hash:"be29908600b7067c583ac21da1544a2d",slug:"materials-at-the-nanoscale",bookSignature:"Awadesh Kumar Mallik",coverURL:"https://cdn.intechopen.com/books/images_new/10411.jpg",editedByType:"Edited by",editors:[{id:"178218",title:"Dr.",name:"Awadesh",surname:"Mallik",slug:"awadesh-mallik",fullName:"Awadesh Mallik"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6320",title:"Advances in Glass Science and Technology",subtitle:null,isOpenForSubmission:!1,hash:"6d0a32a0cf9806bccd04101a8b6e1b95",slug:"advances-in-glass-science-and-technology",bookSignature:"Vincenzo M. 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In this endeavor, they need a good optimizer to reach their target. Several optimizers are available, which work efficiently for various kinds of problems. Among these, genetic algorithm (GA) is chosen as a functional tool for good and useful optimizer. The algorithm has proven to be a class of effective optimization techniques for many applications in engineering, economics, manufacturing, artificial intelligence, operations research, space science, agriculture, physics, chemistry, bioinformatics, medical science, and many more. However, there are advantages and disadvantages of using genetic algorithms as an optimizer such as other optimization tools. The amount of
This chapter discusses the utilization of GA in various fields with examples and illustrations. It is shown how the GA recognizes a specific pattern from a group of patterns. Genetic-algorithm-based clustering technique is then described for identifying similar group of items among several homogeneous or heterogeneous groups. The discussion is also done on the biological sequence alignment with the GA in achieving accurate alignment.
The rest of the chapter is organized as follows. Section 2 describes briefly the history of evolutionary algorithm along with the GA. A survey on genetic algorithm is narrated in Section 3. The next three sections elaborate several utilizations of the GA in various scientific fields. Section 4 identifies a pattern among a group of patterns. Genetically guided clustering technique is elaborated in Section 5. Section 6 describes the application of genetic algorithm for detection of polygonal shape of a dot pattern. The biological sequence alignment is discussed in Section 7. Section 8 concludes the chapter.
In the literatures, three primary search methods are identified, namely
For several years since 1950, many computer researchers studied evolutionary systems independently with an idea that in future evolution could be used as an important and essential optimization tool for solving various problems in engineering. The idea was to develop a system with a population of candidate solutions to a given problem with operators influenced by both natural selection and natural genetic variation. Rechenberg [2] introduced “evolution strategies” (
Finally, in 1960s and 1970s, at the University of Michigan, Holland first invented the genetic algorithms and later on improved by himself along with his colleagues [1]. Unlike the previous evolution strategies, Holland’s intention was to formally study the phenomenon of adaptation, which occurs in nature instead of designing algorithms for solving specific problems. He also developed ways by importing the natural adaptation mechanisms in computer systems. The genetic algorithm was presented by Holland as an abstraction of the biological evolution and also gave a theoretical framework for adaptation. His GA was a method for moving from one population of “chromosome” (string of 1’s and 0’s) to a new population with a kind of “natural selection” along with the genetic operators, namely crossover, mutation, and inversion. Each chromosome was a combination of “genes” (bits), and each gene was a particular “allele” (either 0 or 1). In the population the useful chromosomes for reproduction were chosen by the selection operator. The fitter chromosomes thus produced more off-springs compared with less fit ones. In the process of crossover, the subparts of two chromosomes were exchanged. This basically mimicked the biological recombination between two single-chromosome (haploid) organisms. The allele values of some locations of the chromosome were randomly changed and reversed by mutation and inversion processes, respectively.
For last several years, the researchers had been studying and interacting widely on different evolutionary computation methods and ultimately it had broken down to some extent the boundaries between GAs, evolutionary programming, evolution strategies, and other evolutionary approaches. Today the term “genetic algorithm” is used by the researchers to describe something very far from the original conception of Holland. There are at least three overlapping meanings of
Eight-puzzle problem with the initial state and the final state.
Although the genetic algorithm is directed to
The GAs have been employed in a wide variety of problems. Some of the studies involving medical image registration, image segmentation, and contour recognition are available in [12, 13, 14]. In addition, the classification of endothelial cells in tissue section, normalization of Chinese handwriting, and evaluation of earthquake risk for geological structures are examples of some practical applications [15, 16, 17]. GAs have also been used in optimization of feature extraction chain, error-correcting graph isomorphism, and dot pattern matching [18, 19, 20]. Moreover, the techniques to generate fuzzy rules for target tracking, constrained placement problems, process planning for job shop matching and blind channel identification with higher-order cumulation fitting have employed GA [21, 22, 23, 24]. In the field of information retrieval, GA is used to retrieve the dynamic web-based contents [25]. Raidl et al. worked with biased mutation in evolutionary algorithm for solving subset-selection problems on complete graphs [26].
Although the simple genetic algorithm has been used for solving various problems, researchers have always tried to enhance the performance of GA by modifying the algorithm. Among several approaches for the improvement of performance, in 1970, Cavicchio developed a technique to conserve the best individuals by substituting the inferior parents if the offspring’s fitness exceeded that of the inferior parent [27]. The
Some more variants of GA are also found in the literature [1, 32, 33, 34, 35]. However, the implementation of modified genetic algorithm often increases the computation time for solving various complex problems, and the researchers have tried to increase the speed of the algorithm using parallel/distributed GA when the computation time is large for a particular problem. Various parallel implementations of GAs are discussed in [36, 37]. Multiobjective optimization problem is also solved by parallel genetic algorithm. The major parallel multiobjective genetic algorithms are discussed and some observations are included in [38]. Furthermore, two approaches of parallel GAs, namely the
Other than these, the well-known NP-hard Traveling Salesman problem on a cluster of nodes is also solved by the application of parallel genetic algorithm [42]. A
The researchers have also successfully employed GAs in solving various kinds of problems in other scientific fields. Notredome and Higgins [44] developed a popular software for aligning sequences with two objective functions. Several GA-based approaches were developed for solving multisequence alignment [45, 46, 47, 48, 49]. In chemistry, GAs were incorporated in studying water oxidation [50], magnetic storage [51], catalysis [52], stability of boron wheels [53]. Sathya et al. developed a genetic-based algorithm to classify genes for identification of biomaker genes to suggest an individualized treatment [54]. As a robust heuristic search method, the GA helped for mining information from the large datasets. It was applied to discover interesting and useful relations between data elements with abstraction in the domain of association rules [55]. Another GA-based classification technique was used to improve the feature selection with support vector machine (SVM) classifier. Feature selection was used to classify a breast cancer dataset with 699 instances into two classes, namely benign and malignant, each with 11 attributes [56]. A genetic approach was also applied in machine learning for selecting the proper and the best move in playing of chess. The technique helped in deciding the effective move by classifying the set of rules for a particular solution [57]. In addition, several GAs and hybrid GAs were proposed by the medical practitioners as well as the scientists to detect cancer by different approaches such as feature selection, categorization, and classification, registering temperature difference, and many more [58]. A hybrid genetic approach was developed for early diagnosis of breast cancer using thermography. This technique measured the temperature difference between cancerous and healthy tissues with a high success rate [59].
In the following sections, some applications of genetic algorithm with several illustrations are discussed. The parameters of experimental setup are provided to help implementation of the GAs in solving similar or different kinds of problems. Some exceptional results are also elaborated with illustrations to understand the strength of GA for finding solution in numerous scientific fields.
In a 2-D or 3-D feature space, a dot pattern represents some physical objects or class of objects with a set of dots or points. Such dot patterns are used in geographic and cartographic data, spatial information system, astronomy and astrophysics, remote sensing, image texture analysis, biomedical imaging, and some other areas [60, 61, 62, 63]. The involvement of such studies with dot patterns includes the set estimation and shape identification, identification of point process parameter, and clustering and classification.
The dot pattern matching problem is defined as follows.
The matching score is now evaluated as follows for two dot patterns
where
In pattern recognition problem, the performance of CGA (conventional genetic algorithm) [1] has been tested with the sequential and distribution of two GA-based modified methods, namely, cascaded genetic algorithm (CAGA) [64] and distributed CAGA (DCAGA) [65].
In dot pattern or object matching problem,
A scene of multiple dot patterns in 2-D space [
A scene of multiple objects with edge map in 2-D space [
Dot pattern | CAGA | CGA | ||||
---|---|---|---|---|---|---|
Successful matching | Unsuccessful matching | Successful matching | Unsuccessful matching | |||
Perfectly matched | Visually matched | Not matched | Perfectly matched | Visually matched | Not matched | |
DP1 | 22% | 53% | 25% | 0% | 10% | 90% |
DP2 | 30% | 40% | 30% | 0% | 7% | 93% |
DP3 | 73% | 23% | 4% | 3% | 53% | 44% |
DP4 | 17% | 80% | 3% | 0% | 50% | 50% |
Matching results of CAGA and CGA on the dot patterns in Figure 2. The following data have been summarized over 50 runs for each DP.
Dot pattern | DCAGA | DCGA | ||||
---|---|---|---|---|---|---|
Successful matching | Unsuccessful matching | Successful matching | Unsuccessful matching | |||
Perfectly matched | Visually matched | Not matched | Perfectly matched | Visually matched | Not matched | |
DP1 | 57% | 43% | 0% | 14% | 73% | 13% |
DP2 | 40% | 60% | 0% | 10% | 70% | 20% |
DP3 | 83% | 17% | 0% | 10% | 67% | 23% |
DP4 | 40% | 60% | 0% | 10% | 90% | 0% |
Matching results of DCAGA and DCGA on the dot patterns in Figure 2. The following data have been summarized over 50 runs for each DP.
Dot pattern | DCAGA | DCGA | ||||
---|---|---|---|---|---|---|
Successful matching | Unsuccessful matching | Successful matching | Unsuccessful matching | |||
Perfectly matched | Visually matched | Not matched | Perfectly matched | Visually matched | Not matched | |
P1 | 33% | 67% | 0% | 0% | 30% | 70% |
P2 | 76% | 24% | 0% | 10% | 63% | 27% |
P3 | 27% | 67% | 6% | 3% | 10% | 87% |
P4 | 17% | 80% | 3% | 0% | 27% | 73% |
Matching results of DCAGA and DCGA on the objects with edge map in Figure 3. The following data have been summarized over 50 runs for each DP.
The dot/point patterns depicted in Figure 3 are basically the
We consider the results of GAs for Figure 2 in the experiment. From Table 1, the success rate of CAGA for the best case is 96% for DP3 where 73% of times the pattern is perfectly matched and 23% of times it is visually matched. The success rate of CAGA for DP4 is 97%, but in this case the pattern is perfectly matched for only 17% of times. The performance of CAGA is worst for DP2 with failure rate of 30%. On the other hand, the best performance of CGA is achieved for DP3 where the pattern is perfectly matched for 3% of times and visually matched for 53% of times. In CGA, the successful matching score of DP2 is only 7% (visually matched), which is worst among all four patterns.
From Table 2, it is noted that for the DCAGA, the success rate is 100% for all four patterns considering perfect and visual matching. The DCAGA achieves the best performance for DP3. On the contrary, in the worst case, the failure rate of the DCGA is at most 23%. However, for matching of the pattern DP4, a success rate of 100% is reached by the DCGA.
Table 3 tabulates the experimental results of object matching for the GA-based distributed approaches. It is observed that the DCAGA cannot always achieve the success rate of 100% as noticed for the earlier experiment of dot pattern matching in Table 2. The success rate of the DCAGA is 100% for P1 and P2, and the failure rate is a maximum 6% for P3 and P4. It is noted that both techniques perform best for the isolated object P2. The DCGA does not perform well for the objects, which are close to each other.
In general, the clustering problem can be presented as follows:
For the set of
Clustering approaches are semi-optimal ways of arriving at the grouping problem. The number of ways of sorting
For 25 objects with five clusters, this is in the order of
Since similarity is fundamental to the definition of a cluster, a measure of similarity between two objects/patterns/entities drawn from the same feature space is essential to most clustering procedures. The proximity of individuals (i.e., objects) is usually expressed as a distance during the clustering of data units. The clustering of variables generally involves a correlation or other such measure of association. The smaller the distance between the objects, the higher is the similarity. Because of the variety of feature types and scales, the distance measure (or measures) must be chosen carefully. Several distance measures are employed for clustering [67, 68]. The most popular and commonly used one is the Euclidean distance,
which is the line of sight distance between two points representing the objects.
Let us consider a set consisting of
We have discussed data clustering with a GA-based algorithm. This algorithm is dependent on the splitting and merging techniques [69]. Initially the data are split into several sub-clusters, and in the next step those sub-clusters are merged to find out the required clusters. We have considered various types of datasets to show how the data are grouped to isolate properly the required number of clusters.
We have studied seven datasets in
(a) The original dataset with three clusters before the application of GA-based algorithm. (b) Isolated three clusters after completion of the algorithm [
(a) The original dataset with five clusters before the application of algorithm. (b) Isolated five clusters after the completion of algorithm [
(a) The original dataset with three clusters before the application of algorithm. (b) Isolated three clusters after the completion of algorithm [
(a) The original dataset with two clusters before the application of algorithm. (b) Isolated two clusters after the completion of algorithm [
(a) The original dataset with two clusters before the application of algorithm. (b) Isolated two clusters after the completion of algorithm [
(a) The original dataset with six clusters before the application of algorithm. (b) Isolated six clusters after the completion of algorithm [
(a) The original dataset with two clusters and noise before the application of algorithm. (b) Isolated two genuine clusters and separated noise after the completion of algorithm [
The dataset in Figure 5(a) comprises five clusters. All of them are denser near the center and lighter toward the boundary. Three clusters are equal in size, and two of them are close to each other. The five clusters have been correctly isolated using the algorithm, as shown in Figure 5(b).
On the contrary, Figure 6 consists of two closely located clusters of different densities. One cluster has uniformly dense data and the other has density tapering away from the center. The data density of the third cluster is also uniform. However, all of them are equal in size. Figure 6(a)
Figure 7 depicts two almost overlapping clusters of equal size and density. The data points distribution of both the clusters is interesting since they follow Gaussian distribution in
The dataset of Figure 8 is different and interesting from previous four datasets. Here, one cluster is completely enclosed by the other. The data point density of both clusters is almost uniform. In the dataset, a special feature of the algorithm is used. It is to check the adjacency condition of sub-clusters, which are to be merged to isolate the clusters ultimately as depicted in 8(b).
Figures 9
The data shown in Figure 10(a) are quite different from all other datasets. It is also a special dataset in nature because it consists of two identical shaped clusters with different density in the presence of random noise scattered all over the 2-D feature space. Two clusters are perfectly identified and also the noise, which is identified as the third cluster as shown in Figure 10(b). After application of the algorithm it is seen that the noise is clustered locally in smaller arbitrary shape in the space due to inherent characteristics of the random noise. The third cluster is then formed by merging all smaller clusters of noisy data as depicted in Figure 10(b).
We have now discussed the cluster identification of a dataset of more than two features. Iris data is a set of four features [70], which is one of the most popular databases in the pattern recognition literature. The dataset contains three classes named as Iris Setosa (Class A), Iris Versicolor (Class B), and Iris Virginica (Class C). Each class has 50 instances and refers to a kind of Iris plant. The four feature attributes of the data are
Classified as class A | Classified as class B | Classified as class C | |
---|---|---|---|
Actual class A | 50 | 0 | 0 |
Actual class B | 0 | 40 | 10 |
Actual class C | 0 | 0 | 50 |
Confusion matrix for Iris flower classification.
One cluster has been separated clearly from other two after invoking the algorithm. However, other two clusters are not perfectly separable (see Table 4).
The processing of dot patterns (DPs) or point sets in a plane is useful and important in pattern recognition problems. Dot patterns are encountered in various problems including points in feature space [71], pixels in digital image [72], physical objects such as stars in the galaxy [73], or spatial data [74, 75, 76].
An attribute of dot patterns is the visual shape generated by it. One simple way of defining the external shape is the convex hull [77]. But in most cases, the underlying shape from which the points emerge is not convex. To detect the nonconvex shape, the GA-based split and merge procedure [78] starts with the initial convex hull polygon. The
(a) Edge (represented by dashed line) marked for splitting process (edge AB before splitting; edges AC and BC created by splitting). (b) Edge marked for merging process over region XYZ and region PQRS). (c) Edges (represented by dashed lines) marked for isolation process [
A DP may also be the union of more than one disjoint smaller DP region as shown in Figure 11(c). The isolation process separates such regions by removing two lines of the resulting polygon simultaneously, which act as the bridge between two disjoint regions. The removal of lines is possible if the region within the lines does not contain any dot.
Now, consider a set
The algorithm is studied on different dot pattern sets in
(a) A spiral shaped DP and its convex hull. (b) Resulting perceptual border of the DP [
(a) A star-shaped DP and its convex hull. (b) Resulting perceptual border of the DP with the GA-based merging process [
(a) A DP with nonuniform density and its convex hull. (b) Resulting perceptual border of the DP [
(a) A DP with two Gaussian distributions and its convex hull. (b) Resulting perceptual border of the DP applying GA-based merging process. [
(a) A multicomponent chromosome-like dot pattern and its convex hull. (b) Resulting perceptual border of the components [
In the group of datasets with nearly uniform density data points, Figures 12
The dot patterns of Figures 14
The ultimate polygons with smother border as in Figure 17(b) and (c) are generated from the starting polygon of Figure 17(a) if the user specifies the number of sides. Figure 17(a) shows a
(a) A C-shaped DP and its convex hull. (b) Perceptual border of the DP with 36 edges (user specified). (c) Perceptual border of the DP with 23 edges (user-specified) [
Figure 16 is a special dot pattern of three components where each one contains nearly uniformly dense data points. The convex hull of the DP is a 17-side polygon. The splitting process followed by the isolation process separates the DP into three distinct regions with zigzag polygonal boundaries. Finally, for each isolated polygon, the merging process generates three separated polygons with a smoother border as in Figure 16(b).
An alignment at sequence level represents the primary structure of biological macromolecules, which is represented as a linear sequential chain of residues. Residues are the building blocks of macromolecules in which DNAs and RNAs are made of nucleotide residues and proteins are of amino acid residues. The building blocks encode the history of molecular evolution. During the process of evolution, residues accumulate many random changes and diverge over time. Some of the changes are accepted and selected by natural selections, and some of them are not. Residues, which are involved in important functional and/or structural roles, tend to be preserved by natural selection and do not accumulate random changes [79]. These parts of sequences help to infer the evolutionary history and identify the common ancestor of two or more related sequences. Therefore, the sequences that share the common evolutionary origin are called homologous sequences. Homology is the conclusion that can be drawn by looking at the degree of similarities between two or more sequences of different species.
The similarity between two or multiple sequences is quantified by measuring the alignment score. To choose the best alignment among a set of sequences, quantification of alignment is required. It identifies the evolutionary distances between two or multiple sequences. The best alignment always associated with the maximum shared similarity or identity and the lowest number of mutational events between the sequences. Therefore, the scoring systems always score positive when there are identical or similar residues in the alignment.
On the other hand, the mutational events are always associated with zero or negative scores. Different scoring schemes are used in sequence alignment to know the level of identity or similarity. The process of score evaluation for a pairwise sequence alignment is more straightforward and simple compared with scoring the alignment of multiple sequences together. In two aligned sequences, the total number of identical residues yields a percentage identity between them. This identity count is used mostly for nucleotide sequence alignment, as the nucleotides A, T/U, G, and C play equivalent roles in the structure and function of the DNA or RNA molecule. Therefore, the nucleotides are either identical, which is a match in alignment, or nonidentical, which is a mismatch in an alignment. In contrast, for protein sequences, a similarity score is calculated along with the identity score denoting the amino acids having similar physicochemical properties. The substitution matrices are normally consulted for protein sequence alignment.
The GA-based sequence alignment [80] is a simple method to align a pair of sequences (a query sequence and a known database sequence) without gaps for finding similarity. When a biological sequence (DNA/RNA/protein), called the query sequence is given, one usually performs a similarity measure within the databases that consist of all available genomic sequences and the known protein sequences. Eventually, the search yields many sequences with varying degree of similarities. It then depends on the user to identify those that are associated with the higher scores and homologous. In the alignment technique, we consider one sequence as a database sequence denoted by D and the other as a query sequence denoted by Q.
In the initialization step, an initial population of size
In this approach, all three operators of the conventional GA, namely tournament selection, one-point crossover, and bit-flip mutation are used [1]. In each iteration, the algorithm advances the search toward a better solution by producing a better population. After random selection of two chromosomes from the population pool, the crossover and the mutation operations are performed on them.
The fitness score of a chromosome is evaluated by the following fitness function F.
where
For protein sequence alignment, the BLOSUM62 matrix is consulted and for DNA sequence, an identical match gets +1 score, and a mismatch gets 0. The fitness score determines the similarity or identity level between two sequences D and Q. The fitness score is now calculated for pairwise alignment between two sequences D and Q. It is evaluated in three ways. The scoring is for the straight alignment and the alignments with a left or right shift.
For a straight alignment, let us consider the following two nucleotide sequences,
Let us now consider the following binary coded chromosome from the population of an intermediate generation as a probable solution of query sequence
The equivalent decoded value of
where “_” denotes the presence of 0 in
Therefore, the alignment score or the fitness of the chromosome would be 4 and the alignment structure looks like:
However, the alignment by shifting is necessary for a different representation of Q to achieve the best alignment score. For example, if Q is ACTTAGTAAC, then the shifting to the right is required to obtain the optimum alignment with alignment score 6, which is illustrated below,
Similarly, for Q: GCGTTGCTTAGA, the left shift alignment is necessary to obtain the best alignment with score 7, which is shown below,
For the evaluation of the final fitness score of an individual, the scores of all three alignments (straight, shift right, and shift left) techniques are considered. However, the selection of position to start alignment depends on the randomly chosen position number. Since minimum 30% matching between two sequences is necessary to evaluate the homologous relationship [81, 82], the random numbers between 1 and 70% of the length of the sequence is generated. For example, if a sequence length is of 100 residues, we shall identify a position randomly between
This chapter describes the application of genetic algorithm in various scientific fields as a reliable optimizer. It is narrated how the simple/conventional genetic algorithm has been advanced with time. Apart from these some real applications of the modified genetic algorithm with illustrations have been elaborated in brief (references are provided for detailed description) on the synthetic data. It is shown how the GA identifies a pattern (in single/multiple dot(s) or edge map(s) scene) in 2-D space. The reasoning is given to explain the differences in results of matching scores of two genetic sequential and distributed schemes (CGA and CAGA or DCGA and DCAGA). A genetic-based clustering approach with results provides the efficient use of GA for grouping synthetic as well as real data. The function of GA in complex shape detection and biological sequence alignment on synthetic data further shows the application of GA as a useful and essential optimizer.
GA | Genetic Algorithm |
SVM | Support Vector Machine |
CGA | Conventional Genetic Algorithm |
CAGA | CAscaded Genetic Algorithm |
DCAGA | Distributed CAscaded Genetic Algorithm |
2-D | Two-Dimensional |
3-D | Three-Dimensional |
R2 | Two-Dimensional space |
DP | Dot Pattern |
DNA | Deoxyribonucleic acid |
RNA | Ribonucleic acid |
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\\n\\nDr Alex Lazinica
\\n\\nAlex Lazinica is co-founder and Board member of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his Ph.D. in Robotics at the Vienna University of Technology. There, he worked as a robotics researcher with the university's Intelligent Manufacturing Systems Group, as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and, most importantly, co-founded and built the International Journal of Advanced Robotic Systems, the world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career since it proved to be the pathway to the foundation of IntechOpen with its focus on addressing academic researchers’ needs. Alex personifies many of IntechOpen´s key values, including the commitment to developing mutual trust, openness, and a spirit of entrepreneurialism. Today, his focus is on defining the growth and development strategy for the company.
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\n\nCo-founded by Alex Lazinica and Vedran Kordic: “We are passionate about the advancement of science. As Ph.D. researchers in Vienna, we found it difficult to access the scholarly research we needed. We created IntechOpen with the specific aim of putting the academic needs of the global research community before the business interests of publishers. Our Team is now a global one and includes highly-renowned scientists and publishers, as well as experts in disseminating your research.”
\n\nBut, one thing we have in common is -- we are all scientists at heart!
\n\nSara Uhac, COO
\n\nSara Uhac was appointed Managing Director of IntechOpen at the beginning of 2014. She directs and controls the company’s operations. Sara joined IntechOpen in 2010 as Head of Journal Publishing, a new strategically underdeveloped department at that time. After obtaining a Master's degree in Media Management, she completed her Ph.D. at the University of Lugano, Switzerland. She holds a BA in Financial Market Management from the Bocconi University in Milan, Italy, where she started her career in the American publishing house Condé Nast and further collaborated with the UK-based publishing company Time Out. Sara was awarded a professional degree in Publishing from Yale University (2012). She is a member of the professional branch association of "Publishers, Designers and Graphic Artists" at the Croatian Chamber of Commerce.
\n\nAdrian Assad De Marco
\n\nAdrian Assad De Marco joined the company as a Director in 2017. With his extensive experience in management, acquired while working for regional and global leaders, he took over direction and control of all the company's publishing processes. Adrian holds a degree in Economy and Management from the University of Zagreb, School of Economics, Croatia. A former sportsman, he continually strives to develop his skills through professional courses and specializations such as NLP (Neuro-linguistic programming).
\n\nDr Alex Lazinica
\n\nAlex Lazinica is co-founder and Board member of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his Ph.D. in Robotics at the Vienna University of Technology. There, he worked as a robotics researcher with the university's Intelligent Manufacturing Systems Group, as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and, most importantly, co-founded and built the International Journal of Advanced Robotic Systems, the world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career since it proved to be the pathway to the foundation of IntechOpen with its focus on addressing academic researchers’ needs. Alex personifies many of IntechOpen´s key values, including the commitment to developing mutual trust, openness, and a spirit of entrepreneurialism. Today, his focus is on defining the growth and development strategy for the company.
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