A literature review on the application of evolutionary computing to credit scoring

The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees. However, the importance of credit grant decisions for financial institutions has caused growing interest in using a variety of computational intelligence techniques. This paper concentrates on evolutionary computing, which is viewed as one of the most promising paradigms of computational intelligence. Taking into account the synergistic relationship between the communities of Economics and Computer Science, the aim of this paper is to summarize the most recent developments in the application of evolutionary algorithms to credit scoring by means of a thorough review of scientific articles published during the period 2000–2012.This work has partially been supported by the Spanish Ministry of Education and Science under grant TIN2009-14205 and the Generalitat Valenciana under grant PROMETEO/2010/028

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Applications of complex adaptive systems approaches to coastal systems

This thesis investigatesth e application of complex adaptives ystemsa pproaches (e. g. Artificial Neural Networks and Evolutionary Computation) to the study of coastal hydrodynamica nd morphodynamicb ehaviour.T raditionally, nearshorem orphologicalc oastal systems tudiesh ave developeda n understandingo f thosep hysicalp rocesseso ccurringo n both short temporal, and small spatial scales with a large degree of success. The associated approachesa nd conceptsu sedt o study the coastals ystema t theses calesh ave Primarily been linear in nature.H owever,w hent hesea pproachetso studyingt he coastals ystema re extendedto investigating larger temporal and spatial scales,w hich are commensuratew ith the aims of coastal managementr, esults have had less success.T he lack of successi n developing an understandingo f large scalec oastalb ehaviouri s to a large extent attributablet o the complex behavioura ssociatedw ith the coastals ystem.I bis complexity arises as a result of both the stochastic and chaotic nature of the coastal system. This allows small scale system understandingto be acquiredb ut preventst he Largers caleb ehaviourt o be predictede ffectively. This thesis presentsf our hydro-morphodynamicc ase studies to demonstratet he utility of complex adaptives ystema pproachesfo r studying coastals ystems.T he first two demonstrate the application of Artificial Neural Networks, whilst the latter two illustrate the application of EvolutionaryC omputation.C aseS tudy #I considerst he natureo f the discrepancyb etweent he observedl ocation of wave breakingp atternso ver submergeds andbarsa nd the actual sandbar locations.A rtificial Neural Networks were able to quantitativelyc orrectt he observedlo cations to produce reliable estimates of the actual sand bar locations. Case Study #2 considers the developmenot f an approachf or the discriminationo f shorelinel ocation in video imagesf or the productiono f intertidal mapso f the nearshorer egion. In this caset he systemm odelledb y the Artificial Neural Network is the nature of the discrimination model carried out by the eye in delineating a shoreline feature between regions of sand and water. The Artificial Neural Network approachw as shownt o robustly recognisea rangeo f shorelinef eaturesa t a variety of beaches and hydrodynamic settings. Case Study #3 was the only purely hydrodynamic study consideredin the thesis.I t investigatedth e use of Evolutionary Computationt o provide means of developing a parametric description of directional wave spectra in both reflective and nonreflective conditions. It is shown to provide a unifying approach which produces results which surpassedth ose achievedb y traditional analysisa pproachese vent hough this may not strictly have been considered as a fidly complex system. Case Study #4 is the most ambitious applicationa nd addressetsh e needf or data reductiona s a precursorw hen trying to study large scalem orphodynamicd ata sets.I t utilises EvolutionaryC omputationa pproachesto extractt he significant morphodynamic variability evidenced in both directly and remotely sampled nearshorem orphologiesS. ignificantd atar eductioni s achievedw hilst reWning up to 90% of the original variability in the data sets. These case studies clearly demonstrate the ability of complex adaptive systems to be successfidly applied to coastal system studies. This success has been shown to equal and sometimess urpasst he results that may be obtained by traditional approachesT. he strong performance of Complex Adaptive System approaches is closely linked to the level of complexity or non-linearity of the system being studied. Based on a qualitative evaluation, Evolutionary Computation was shown to demonstrate an advantage over Artificial Neural Networks in terms of the level of new insights which may be obtained. However, utility also needs to consider general ease of applicability and ease of implementation of the study approach.I n this sense,A rtificial Neural Networks demonstratem ore utility for the study of coastals ystems.T he qualitative assessmenatp proachu sedt o evaluatet he cases tudiesi n this thesis, may be used as a guide for choosingt he appropriatenesso f either Artificial Neural Networks or Evolutionary Computation for future coastal system studies

A web scraping framework for stock price modelling using deep learning methods

Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2018-2019, Tutor: Salvador Torra Porras(eng) This work aims to shed light to the process of webs craping,emphasizing its im- portance in th enew ’BigData’ era with an illustrative application of such methods in financial markets. The work essentially focuses on differents craping methodolo- gies that can be used to obtain large quantities of heterogenous data in realtime. Automatization of data extraction systems is one of the main objectives pursuedin this work, immediately followed by the development of a framework for predic- tive modelling. Applying neural networks and deep learning methods to the data obtained through webscraping. The goal pursued is toprovide the reader with some remarkable notes on how these models work while allowing room for further research and improvements on the models presented

Structural optimisation via genetic algorithms

Thesis (MScEng)--Stellenbosch University, 2012.ENGLISH ABSTRACT: The design of steel structures needs to incorporate some optimisation procedure that evolves the initial design into a more economic nal design, where this nal design must still satisfy all the initial design criteria. A candidate optimisation technique suggested by this research is the genetic algorithm. The genetic algorithm (GA) is an optimisation technique that was inspired by evolutionary principles, such as the survival of the ttest (also known as natural selection). The GA operates by generating a population of individuals which 'compete' with one another in order to survive, or di erently stated, in order to make it into the next generation. Each individual presents a solution to the problem. Surviving solutions which propagate through to the next generation are typically 'better' or ' tter' than the ones that had died o , hence suggesting a process of optimisation. This process continues until a de ned convergence criteria is met (e.g. speci ed maximum number of generations is reached), where after the best individual in the population serves as the ultimate solution to the problem. This study thoroughly investigates the inner workings that drive the algorithm, after which an algorithm is presented to face the challenges of structural optimisation. This algorithm will be concerned only with sizing optimisation; geometry, topology and shape optimisation is outside the scope of this research. The objective of this optimising problem will be to minimise the weight of the structure, it is assumed that the weight is inversely propotional to the cost of the structure. The motive behind using a genetic algorithm in this study is largely due to its ability to handle discrete search spaces; classical search methods are typically limited to some form of gradient search technique for which the search space must be continuous. The algorithm is also preferred due to its ability to e ciently search through vast search spaces, which is typically the case for a structural optimisation problem. The genetic algorithm's performance will be examined through the use of bench-marking problems. Benchmarking is done for both planar and space trusses; the 10 - and 25 bar truss problems. Such problems are typically analysed with stress and displacement constraints. After the performance of the algorithm is validated, the study commences towards solving real life practical problems. The rst step towards solving such problems would be to investigate the 160 bar truss benchmarking problem. This problem will be slightly adapted by applying South African design standards to the design, SANS (2005). This approach is more realistic, when compared to simply specifying stress and displacement constraints due to the fact that an element cannot simply be assigned the same stress constraint for tension and compression; slenderness and buckling e ects need to be taken into account. For this case, the search space will no longer simply be some sample search space, but will consist of real sections taken from the Southern African Steel Construction Handbook, SAISC (2008). Finally, the research will investigate what is needed to optimise a proper real life structure, the Eskom Self-Supporting Suspension 518H Tower. It will address a wide variety of topics, such as modelling the structure as realistically as possible, to investigating key aspects that might make the problem di erent from standard benchmarking problems and what kind of steps can be taken to over-come possible issues and errors. The algorithm runs in parallel with a nite element method program, provided by Dr G.C. van Rooyen, which analyses the solutions obtained from the algorithm and ensures structural feasibility.AFRIKAANSE OPSOMMING: Die ontwerp van staal strukture moet 'n sekere optimalisasie proses in sluit wat die aanvanklike ontwerp ontwikkel na 'n meer ekonomiese nale ontwerp, terwyl die nuwe ontwerp nog steeds aan al die aanvanklike ontwerp kriteria voldoen. 'n Kandidaat optimeringstegniek wat voorgestel word deur hierdie navorsing is die genetiese algoritme. Die genetiese algoritme (GA) is 'n optimaliserings tegniek wat ge- ïnspireer was deur evolusionêre beginsels soos die oorlewing van die sterkste (ook bekend as natuurlike seleksie). Dit werk deur die skep van 'n bevolking van individue wat 'kompeteer' met mekaar om dit te maak na die volgende generasie. Elke individu bied 'n oplossing vir die probleem. Oorlewende oplossings wat voortplant deur middel van die volgende generasie is tipies 'beter' of ' kser' as die individue wat uitgesterf het, dus word 'n proses van optimalisering word saamgestel. Hierdie proses gaan voort totdat 'n bepaalde konvergensie kriteria voldoen is (bv. 'n gespesi seerde aantal generasies), waar na die beste individu in die bevolking dien as die uiteindelike oplossing vir die probleem. Hierdie studie ondersoek die genetiese algoritme, waarna 'n algoritme aangebied word om die uitdagings van strukturele optimalisering aan te spreek. Hierdie algoritme het alleenlik te doen met snit optimalisering; meetkunde, topologie en vorm optimalisering is buite die bestek van hierdie navorsing. Die motief agter die gebruik van 'n genetiese algoritme in hierdie studie is grootliks te danke aan sy vermoë om diskrete soek ruimtes te hanteer; klassieke soek metodes word gewoonlik beperk tot 'n vorm van 'n helling tegniek waarvoor die soektog ruimte deurlopende moet wees. Die algoritme is ook gekies as gevolg van sy vermoë om doeltre end deur groot soektog ruimtes te soek, wat gewoonlik die geval vir 'n strukturele probleem met optimering is. Die genetiese algoritme se prestasie sal ondersoek word deur die gebruik van standaarde toetse. Standarde toetse word gedoen vir beide vlak en ruimte kappe, die 10 - en 25 element vakwerk. Sulke probleme word tipies met spanning en verplasing beperkings ontleed. Na a oop van die bekragtiging van die algoritme, word praktiese probleme hanteer. Die eerste stap in die rigting sou wees om die 160 element vakwerk toets probleem te ondersoek. Hierdie probleem sal e ens aangepas word deur die toepassing van die Suid-Afrikaanse ontwerp standaarde, SANS (2005) aan die ontwerp. Dit is 'n meer realistiese benadering in vergelyking met net gespesi seerde spanning en verplasing beperkings as gevolg van die feit dat 'n element nie net eenvoudig dieselfde spanning beperking vir spanning en druk toegeken kan word nie; slankheid en knik e ekte moet ook in ag geneem word. In hierdie geval sal die soek ruimte nie meer net meer eenvoudig 'n sekere teoretiese soek ruimte wees nie, maar sal bestaan uit ware snitte wat uit die Suid Afrikaanse Konstruksie Handboek kom, SAISC (2008). Ten slotte sal die navorsing ondersoek instel na 'n standaard Eskom Transmissie toring en dit sal 'n wye verskeidenheid van onderwerpe aanspreek, soos om die modellering van die struktuur so realisties as moontlik te maak, tot die ondersoek van sleutelaspekte wat die probleem verskillend van standaard toets probleme maak en ook watter soort stappe geneem kan word om moontlike probleme te oor-kom. Die algoritme werk in parallel met 'n eindige element metode program, wat deur Dr GC van Rooyen verskaf is, wat die oplossings ontleed van die algoritme en verseker dat die struktuur lewensvatbaar is

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

A course-oriented intelligent tutoring system with probability assessment

Most Intelligent Tutoring Systems (ITSs) in the past have concentrated on small domains and have been topic-oriented. They have tended to be non-extendable prototypes and have neglected the expertise of human teachers. It is argued here that a promising approach at this time is to design course-oriented ITS shells which are based on the human teacher. Courses using such shells could be used to take some of the load of first-time delivery and assessment from teachers and lecturers, and leave them more time for individual tutoring. [Continues.