Georgios Dounias

• PhD
• Professor (Full) at University of the Aegean

This study addresses the complexity of diagnosing Polyneuropathy (PNP), a group of neurological disorders affecting peripheral nerves. The research introduces a hybrid medical data analysis framework, combining machine learning with computational intelligence methods, to enhance the diagnostic process. The proposed methodology involves data preproc...

This paper explores the crucial role of machine learning in disaster response and understanding human behavior during crises. Natural disasters, such as earthquakes and floods, significantly impact health, the economy, and mental well-being. Effective disaster management involves prevention, preparedness, and addressing psychological challenges thr...

The impact of human errors in imaging interpretation and the fact that decision support systems can improve the reliability and accuracy of radiology reporting have led to the more widespread use of these techniques. Developing decision support systems that assist radiologists in accurate diagnoses and improving the medical decision-making process...

Resource leveling is a highly complex optimization problem corresponding to adjusting a project's timeline (start and end dates) with the aim of matching resource allocation demands. The problem is particularly complex when a project is large and involves hundreds or even thousands of activities. Its successful solution is equivalent to considerabl...

This paper focuses on approaching reliability–redundancy allocation problems, using hybrid schemes that consist of individual nature inspired algorithms. The aim is to investigate if hybridization is an efficient way to approach problems with multiple goals. Therefore, known algorithms that have been successfully applied to reliability and redundan...

The accurate throughput estimation of a production line steadily attracts research interest due to its high impact on their economic operation affecting the world production of goods. Since Artificial Intelligence (AI) is embedded in production processes leading to Industry 4.0, it is as well used for throughput estimation. Neural Networks (NN) are...

In this paper, an application of intelligent techniques for modeling production systems is proposed. Such problems are usually highly complex so for most types of production systems, no accurate general formulas exist in the literature for the calculation of throughput. The specific problem studied in this paper is that of an unreliable two-machine...

The constraints and the vast solution space of operational research optimization problems make them hard to cope with. However, Computational Intelligence, and especially Nature-Inspired Algorithms, has been a useful tool to tackle hard and large space optimization problems. In this paper, a very consistent and effective hybrid optimization scheme...

This paper presents an approach to the problem of breast cancer diagnosis through the data analysis of magnetic mammography observations (MRi Data), developing corresponding hybrid classification models of patient cases into specific classes (e.g. Benign and Malignant). The aim of this work is the contribution of machine learning to the diagnostic...

The analytical evaluation of production system performance measures is a difficult task. Over the years, various methods have been developed to solve specific cases of very short production lines. However, formulae for estimating the mean production rate (throughput) are lacking. Recent developments in artificial intelligence simplify their use in...

In the recent years, extensive discussion takes place in literature, on the effectiveness of meta-heuristics, and especially Nature Inspired Algorithms. Usually, authors state that such an approach should embody a well-balanced exploration and exploitation strategy. Sonar Inspired Optimization (SIO) is a recently presented algorithm, which counts a...

In the last decade, we observe an increasing number of nature-inspired optimization algorithms, with authors often claiming their novelty and their capabilities of acting as powerful optimization techniques. However, a considerable number of these algorithms do not seem to draw inspiration from nature or to incorporate successful tactics, laws, or...

One of the problems that Machine Learning (ML) algorithms face in classification tasks is the Curse of Dimensionality, which refers to the sensitivity of their performance to the data dimensionality. The solution to this problem is to select the features that are of higher importance for the model produced. To improve the performance of a classifie...

Recently, a new Nature Inspired Intelligent scheme has been proposed and presented, named Sonar Inspired Optimization (SIO). This algorithm is inspired by the SONAR mechanism, which is used by Warships to detect targets and avoid mines. In this paper, improvements have been done to the SIO approach in an attempt to increase the performance of the a...

For many decades, Machine Learning made it possible for humans to map the patterns that govern interpolating problems and also, provided methods to cluster and classify big amount of uncharted data. In recent years, optimization problems which can be mathematically formulated and are hard to be solved with simple or naïve heuristic methods brought...

These data contain a comprehensive collection of all Nature-Inspired Algorithms. This collection is a result of two corresponding surveys, where all Nature-Inspired Algorithms that have been published to-date were gathered and preliminary data acquired. The rapidly increasing number of nature-inspired approaches makes it hard for interested researc...

One of the upcoming categories of Computational Intelligence (CI) is meta-heuristic schemes, which derive their intelligence from strategies that are met in nature, namely Nature Inspired Algorithms. These algorithms are used in various optimization problems because of their ability to cope with multi-objective problems and solve difficult constrai...

High Voltage Substations (HVS) are the intermediate step between production of power and successfully transmitting it to clients, making them one of the most important checkpoints in power grids. Nowadays-renewable resources and consequently distributed generation are growing fast, the construction of HVS is of high importance both in terms of qual...

The rapidly growing field of Nature Inspired Intelligent techniques consists of more than 200 algorithms. Thus, it is hard to keep an eye on all these schemes and study them. In this report all nature-inspired algorithms (NIAs) are classified according to the problems on which they were applied in the work that they were initially introduced. This...

This chapter is a typical example of usage of Computational Intelligence Techniques-CI-Techniques (Machine Learning-Artificial Intelligence) in medical data analysis problems, such as optimizing the Pap-Smear or Pap-Test diagnosis. Pap-Smear or Pap-Test is a method for diagnosing Cervical Cancer (4th leading cause of female cancer and 2nd common fe...

This chapter presents various intelligent approaches for modelling, generalization and knowledge extraction from data, which are applied in different electric power engineering domains of the real world. Specifically, the chapter presents: (1) the application of ANNs, inductive ML, genetic programming and wavelet NNs, in the problem of ground resis...

In this chapter, computational intelligence and its major methodologies are introduced, and then hybrid intelligent systems are defined, and the most popular hybrid intelligent approaches are discussed. The increased popularity of hybrid intelligent systems during the last decade is the result of the extensive success of these systems in a wide ran...

In most optimization problems, the solution space is very vast. At the same time, the imposed constraints and limitations provide additional burdens for the underline algorithm. Although hybrid intelligent approaches have proven their potential in demanding problem settings, in most cases, they manage to approximate a good-quality solution, meaning...

Estimating the performance of a production line is a difficult problem because of the enormous number of states that exist when analyzing such systems. In addition to the methods developed to address the problem, it is very useful to have a formula linking the characteristics of the line to its performance. Three cases of sort serial production lin...

Resource Leveling is a constrained problem with a large solution space, especially when the projects have numerous tasks. This makes the problem hard to tackle. In this study, a novel algorithm named sonar inspired optimization (SIO) belonging in Nature Inspired Intelligence is applied in both benchmark and artificial resource-leveling problems to...

The chapter discusses algorithmic trading, which refers to any automated process, consisting of a number of interconnected components, whose main aim is to perform financial transactions of any kind. Its chief advantage lies in the fact that human intervention is minimized to an acceptable extent. This is quite desirable because nowadays numerous f...

In the last decade a new variety of nature inspired optimization algorithms has been appeared. After the swarm based models, researchers turned their inspiration in nature phenomena and laws of science. In this way a new category of algorithms was born, equally effective or even sometimes superior to known algorithms for optimization problems, like...

This paper proposes the use of conventional data analysis and computational intelligence methods for the estimation of critical flashover voltage on polluted Cap & Pin porcelain insulators. Specifically, modeling using Artificial Neural Networks (ANNs) and Multiple Linear Regression (MLR) has been attempted, based on related application data. The d...

In this study, we introduce a new population based optimization algorithm named Sonar Inspired Optimization (SIO). This algorithm is based on the underwater acoustics that war ships use for reckoning targets and obstacles. The advantage of the proposed method is the ability for performing wider range of search during the iterations of the algorithm...

This paper copes with the problem of flashover voltage on polluted insulators, being one of the most important components of electric power systems. Α number of appropriately selected computational intelligence techniques are developed and applied for the modelling of the problem. Some of the applied techniques work as black-box models, but they ar...

This paper addresses the modeling and optimization of resource availability in car parks, serving different priority classes of customers. The authors examine various formulations of the problem concerning two general objectives: a) increasing the availability for high priority customers and b) maximizing the aggregate service level. In the current...

Genetic Programming (GP) has been used in a variety of fields to solve complicated problems. This paper shows that GP can be applied in the domain of serial production systems for acquiring useful measurements and line characteristics such as throughput. Extensive experimentation has been performed in order to set up the genetic programming impleme...

This paper deals with the use of intelligent techniques for resource levelling optimisation. An intelligent approach previously introduced but enriched and improved, is applied in a real world project corresponding to the construction of a power plant boiler. Resource levelling optimisation is related to the optimal handling of available resources...

The objective of this paper is to utilize genetic programming methodologies for the modeling and estimation of ground resistance with the use of field measurements related to weather data. Grounding is important for the safe operation of any electrical installation and protects it against lightning and fault currents. The work utilizes both, conven...

In this paper, the optimization problem of resource leveling is considered. More specifically, the problem at-hand refers to a very large project, i.e. the construction of a ship, which is comprised of numerous activities. The essence of resource leveling is to allocate the project’s resource in an efficient way, and in the same time maintain the r...

This paper proposes an evolutionary computation based approach for solving resource leveling optimization problems in project management. In modern management engineering, problems of this kind refer to the optimal handling of available resources in a candidate project and have emerged, as the result of the even increasing needs of project managers...

This paper deals with the use of intelligent techniques for resource levelling optimisation. An intelligent approach previously introduced but enriched and improved, is applied in a real world project corresponding to the construction of a power plant boiler. Resource levelling optimisation is related to the optimal handling of available resources...

This work demonstrates the application of entropy information based inductive learning techniques for the estimation of the ground resistance of grounding systems, used for the safe operation of electrical installations, substations and power transmission lines. Ground resistance value must be kept in low levels, so that grounding systems are able...

In the domain of project management resource leveling optimization refers to the optimal handling of available resources in a candidate project. The problem of resource leveling optimization becomes highly complex as the size of the project increases. Approaches such as exhaustive or greedy search methodologies often fail to provide nearoptimum sol...

The current work surveys 245 papers and research reports related to algorithms and methods inspired from nature for solving supply chain and logistics optimization problems. Nature Inspired Intelligence (NII) is a challenging new subfield of artificial intelligence (AI) particularly capable of dealing with complex optimization problems. Related app...

The paper deals with a class of problems often met in modern project management under the term “resource leveling optimization problems”. The problems of this kind refer to the optimal allocation of available resources in a candidate project and have emerged, as the result of the even increasing needs of project managers in facing project complexit...

The paper deals with “resource leveling optimization problems”, a class of problems that are often met in modern project management. The problems of this kind refer to the optimal handling of available resources in a candidate project and have emerged, as the result of the even increasing needs of project managers in facing project complexity, cont...

Export Date: 7 November 2015

The current work surveys 245 papers and research reports related to algorithms and methods inspired from nature for solving supply chain and logistics optimization problems. Nature Inspired Intelligence (NII) is a challenging new subfield of artificial intelligence (AI) particularly capable of dealing with complex optimization problems. Related app...

This paper proposes ways for estimating the ground resistance of several grounding systems, embedded in various ground enhancing compounds. Grounding systems are used to divert high fault currents to the earth. The proper estimation of the ground resistance is useful from a technical and also economic viewpoint, for the proper electrical installati...

Nowadays, the increased level of uncertainty in various sectors has posed great burdens in the decision-making process. In the financial domain, a crucial issue is how to properly allocate the available amount of capital, in a number of provided assets, in order to maximize wealth. Automated trading systems assist the aforementioned process to a gr...

Resource Management refers to the effective and efficient deployment of the financial, human, production or information technology resources of an organization, when they are needed. Resource management includes more specialized terms such as: • Resource allocation, which assigns the available resources in an economic way, or corresponds to the sch...

In this study, the main aim is to identify a set of near-optimum solutions for a specific formulation of the portfolio management problem. This set of portfolios forms the efficient frontier (or Pareto optimal set). In this chapter, a hybrid nature-inspired intelligence (NII) algorithm is applied in order to find the efficient frontier. The main co...

The paper proposes the use of evolving intelligent techniques, for effective business decision making related to strategic management. Under the current competitive environment, business plans appraisal arises as an important task for bankers, investors, venture capital fund managers and consultants among others. The process of business plans asses...

In previous studies, nature-inspired algorithms have been implemented in order to tackle hard NP-optimization problems, in the financial domain. Specifically, the task of finding optimal combination of assets with the aim of efficiently allocating your available capital is of major concern. One of the main reasons, which justifies the difficulties...

This paper introduces a new hybrid algorithmic nature inspired approach based on Honey Bees Mating Optimization for successfully solving the Euclidean Traveling Salesman Problem. The proposed algorithm for the solution of the Traveling Salesman Problem, the Honey Bees Mating Optimization (HBMOTSP), combines a Honey Bees Mating Optimization (HBMO) a...

Hybrid intelligent algorithms, especially those who combine nature-inspired techniques, are well known for their searching abilities in complex problem domains and their performance. One of their main characteristic is that they manage to escape getting trapped in local optima. In this study, two hybrid intelligent schemes are compared both in term...

The purpose of this project was to calculate the ROI of the middle cerebral artery (MCA) in patients with acute stroke to establish if this measurement could be used as another primary sign of infarction even if other established primary signs of infarction are absent. CT brain scans of 465 patients, who presented in the emergency room in the first...

In this research, an application of a computational intelligence approach for effort estimation in software projects is presented. More specifically, the authors examine a genetic programming system for symbolic regression; the main goal is to derive equations for estimating the development effort that are highly accurate. These mathematical formul...

Appointment-based logistics systems, such as special courier services, or repair / maintenance services, face ever increasing competitive pressures for efficiency and on-time performance. For example, in addition to typical (core) operations, courier service providers lately deal with micrologistics activities, such as bulk product deliveries. The...

Estimating customer demand in a multi-level supply chain structure is crucial for companies seeking to maintain their competitive advantage within an uncertain business environment. This work explores the potential of computational intelligence approaches as forecasting mechanisms for predicting customer demand at the first level of organization of...

Logistics area is often recognized as one of the key elements in achieving effective disaster preparedness and response efforts. This chapter presents modeling and solution approaches for both the problem of prepositioning emergency supplies prior to a disaster as well as the problem of their distribution after the disaster onset. Depending on whet...

As the practices of offshoring and outsourcing force the supply chain networks to keep on expanding geographically in the globalised environment, the logistics processes are becoming more exposed to risk and disruptions. Thus, modern supply chains seem to be more vulnerable than ever. It is clear that efficient logistics risk and security managemen...

Contributions to a supply chain’s overall cost function (such as the bullwhip effect) are sensitive to the different players’ ordering policies. This chapter addresses the problem of developing ordering policies which minimise the overall supply chain cost. Evolutionary Algorithms have been used to evolve such ordering policies. The authors of this...

This chapter considers a detailed mathematical formulation for the problem of designing supply chain networks comprising multiproduct production facilities with shared production resources, warehouses, distribution centers and customer zones and operating under time varying demand uncertainty. Uncertainty is captured in terms of a number of likely...

The increase of transactions by electronic commerce (e-commerce) in Business to Business applications has a constant trend during last years. Many research reports have focused on negotiation and auction mechanisms in this context, but a smaller number of related research attempts, has chosen to develop coalition approaches This research attempt tr...

Supply chain management is a vital process for the competitiveness and profitability of companies. Supply chain consists of a large and complex network of components such as suppliers, warehouses, customers etc. which are connected in almost every possible way. Companies’ main aim is to optimize the components of these complex networks to their ben...

One of the most important, complicated and expensive processes in a warehouse is order-picking. The cost associated with order preparation and picking typically varies between 40% and 60% of the total cost of all the processes in a warehouse; therefore, improving the productivity in order picking would result directly in cost reduction. In any atte...

The cooperation among firms allows them to focus on their core products, improving efficiency and competiveness. The emerging paradigm of co-opetition, considering at the same time cooperative and competitive aspects, seems to be the most promising approach both in traditional and electronic network. This chapter investigates the excess capacity is...

This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problem is the optimization of a class of JIT manufacturing systems. The approach investigated here is to interface a simulation model of the system with a hybrid optimization technique which combines...

Effective travel time prediction is of great importance for efficient real-time management of freight deliveries, especially in urban networks. This is due to the need for dynamic handling of unexpected events, which is an important factor for successful completion of a delivery schedule in a predefined time period. This chapter discusses the predi...

An integral part of econometric practice is to test the adequacy of model specifications. If a model is adequately specified, it should not leave interesting features of the data-generating process in the errors. Despite the common tradition, the importance of diagnostic checking as a safeguard against mis-specification has only recently been recog...

This paper introduces a new hybrid algorithmic nature inspired approach based on particle swarm optimization, for successfully solving one of the most popular supply chain management problems, the vehicle routing problem. The vehicle routing problem is considered one of the most well studied problems in operations research. The proposed algorithm f...

Hybrid intelligent schemes have proven their efficiency in solving NP-hard optimization problems. Portfolio optimization refers to the problem of finding the optimal combination of assets and their corresponding weights which satisfies a specific investment goal and various constraints. In this study, a hybrid intelligent metaheuristic, which combi...

Honey Bees Mating Optimization algorithm is a relatively new nature inspired algorithm. In this paper, this nature inspired algorithm is used in a hybrid scheme with other metaheuristic algorithms for successfully solving the Vehicle Routing Problem. More precisely, the proposed algorithm for the solution of the Vehicle Routing Problem, the Honey B...

The classification problem consists of using some known objects, usually described by a large vector of features, to induce a model that classifies others into known classes. Feature selection is widely used as the first stage of the classification task to reduce the dimension of the problem, decrease noise and improve speed by the elimination of i...

This paper considers the task of forming a portfolio of assets that outperforms a benchmark index, while imposing a constraint on the tracking error volatility. We examine three alternative formulations of active portfolio management. The first one is a typical setup in which the fund manager myopically maximizes excess return. The second formulati...

The successful handling of numerous real–world complex problems has increased the popularity of nature–inspired intelligent (NII) algorithms and techniques. Their successful implementation primarily on difficult and complicated optimization problems, stresses their upcoming importance in the broader area of artificial intelligence. NII techniques t...

We present a hybrid intelligent trading system that combines artificial neural networks (ANN) and particle swarm optimisation (PSO) to generate optimal trading decisions. A PSO algorithm is used to train ANNs using objective functions that are directly linked to the performance of the trading strategy rather than statistical measures of forecast e...

The term pap-smear refers to samples of human cells stained by the so-called Papanicolaou method. The purpose of the Papanicolaou method is to diagnose pre-cancerous cell changes before they progress to invasive carcinoma. In this paper a metaheuristic algorithm is proposed in order to classify the cells. Two databases are used, constructed in diff...

This paper presents the application of a computational intelligence methodology in effort estimation for software projects. Namely, we apply a genetic programming model for symbolic regression; aiming to produce mathematical expressions that (1) are highly accurate and (2) can be used for estimating the development effort by revealing relationships...

We present a hybrid intelligent system for financial forecasting that combines neural networks with econometric GARCH models for volatility. We show how this flexible modelling framework can accommodate most of the statistical features observed in financial time-series (nonlinearities in mean, asymmetric GARCH effects and non-gaussian errors). We a...

The term pap-smear refers to samples of human cells stained by the so-called Papanicolaou method. The purpose of the Papanicolaou method is to diagnose pre-cancerous cell changes before they progress to invasive carcinoma. In this paper, a metaheuristic algorithm is proposed in order to classify the cells. Two databases are used, constructed in dif...

In this paper, we apply a basic Bee Colony Optimization algorithm in order to find a high-quality solution for the constrained portfolio optimization problem. Moreover, we use a basic Ant Colony Optimization algorithm and a Tabu Search metaheuristic approach as a benchmark. Our findings indicate that nature-inspired methodologies are able to find f...

The knowledge of the software quality can allow an organization to allocate the needed resources for the code maintenance. Maintaining the software is considered as a high cost factor for most organizations. Consequently, there is need to assess software modules in respect of defects that will arise. Addressing the prediction of software defects by...

The purpose of this work is to test the efficiency of specific intelligent classification algorithms when dealing with the domain of stroke medical diagnosis. The dataset consists of patient records of the ”Acute Stroke Unit”, Alexandra Hospital, Athens, Greece, describing patients suffering one of 5 different stroke types diagnosed by 127 diagnost...

This paper introduces a new hybrid algorithmic nature inspired approach based on Honey Bees Mating Optimization, for successfully solving the Vehicle Routing Problem. The proposed algorithm for the solution of the Vehicle Routing Problem, the Honey Bees Mating Optimization (HBMOVRP), combines a Honey Bees Mating Optimization (HBMO) algorithm and th...

During the last years nature inspired intelligent techniques have become attractive for analyzing large data sets and solving complex optimization problems. In this paper, one of the most interesting of them, the Ant Colony Optimization (ACO), is used for the construction of a hybrid algorithmic scheme which effectively handles the Pap Smear Cell c...

This paper compares a number of neural network model selection approaches on the basis of pricing S&P 500 stock index options. For the choice of the optimal architecture of the neural network, we experiment with a “top-down” pruning technique as well as two “bottom-up” strategies that start with simple models and gradually complicate the architectu...

The OLMAM algorithm (optimized Levenberg-Marquardt with adaptive momentum) is a variant of the Levenberg-Marquardt algorithm for training multilayer feedforward neural networks. OLMAM has been shown to obtain excellent solutions in difficult classification problems where other computational intelligence techniques usually achieve inferior performan...