Genetic algorithms are suitable for optimization problems that possess multiple local optima. The... more Genetic algorithms are suitable for optimization problems that possess multiple local optima. They can provide excellent approximative solutions to a wide range of problems that either takes an infite timeto solve through an exhaustive search or takes requires a huge amount of computational time. In this article, we are going to describe the implementation of genetic algorithms on the unbounded knapsack problem (UKP), which is an optimazation problem NP-hard. Most search papers had dealt only with the simplest version of knapsack problem which is the 0/1 knapsack problem. They provided algorithms that can find near-optimal solutions. However, there is not enough studies that duilt with the version of the unbounded knapsack problem (UKP) with multiple constraints, which is a strongly NP-hard combinatorial optimization problem occurring in many different applications. The goal of this article is to present an to approach to solve this problem with a sophisticated genetic algorithm.
Diagnostic decision support systems are a set of diverse and complex computing tools. Their aim i... more Diagnostic decision support systems are a set of diverse and complex computing tools. Their aim is to process the patient’s characteristics in order to generate a differential diagnosis. This master thesis draws a panorama of computing approaches to support these systems, starting from the planning to the evaluation, following a complete software development life-cyle. Besides involved software and hardware architectures, two classes of decision support approaches are considered : knowledge-based and non-knowledge-based. The former is based on modeling medical knowledge, it led to the expert systems which aim to reproduce an expert reasoning logic. The latter, also called numerical methods, use data mining technics in order to extract knowledge. Our contribution consists of a critical evaluation of the available technological alternatives as well as a synthesis of crucial recommandations, learnt through experts’ works and surveys in the field of diagnostic support.
Genetic algorithms are suitable for optimization problems that possess multiple local optima. The... more Genetic algorithms are suitable for optimization problems that possess multiple local optima. They can provide excellent approximative solutions to a wide range of problems that either takes an infite timeto solve through an exhaustive search or takes requires a huge amount of computational time. In this article, we are going to describe the implementation of genetic algorithms on the unbounded knapsack problem (UKP), which is an optimazation problem NP-hard. Most search papers had dealt only with the simplest version of knapsack problem which is the 0/1 knapsack problem. They provided algorithms that can find near-optimal solutions. However, there is not enough studies that duilt with the version of the unbounded knapsack problem (UKP) with multiple constraints, which is a strongly NP-hard combinatorial optimization problem occurring in many different applications. The goal of this article is to present an to approach to solve this problem with a sophisticated genetic algorithm.
Diagnostic decision support systems are a set of diverse and complex computing tools. Their aim i... more Diagnostic decision support systems are a set of diverse and complex computing tools. Their aim is to process the patient’s characteristics in order to generate a differential diagnosis. This master thesis draws a panorama of computing approaches to support these systems, starting from the planning to the evaluation, following a complete software development life-cyle. Besides involved software and hardware architectures, two classes of decision support approaches are considered : knowledge-based and non-knowledge-based. The former is based on modeling medical knowledge, it led to the expert systems which aim to reproduce an expert reasoning logic. The latter, also called numerical methods, use data mining technics in order to extract knowledge. Our contribution consists of a critical evaluation of the available technological alternatives as well as a synthesis of crucial recommandations, learnt through experts’ works and surveys in the field of diagnostic support.
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Besides involved software and hardware architectures, two classes of decision support approaches are considered : knowledge-based and non-knowledge-based. The former is based on modeling medical
knowledge, it led to the expert systems which aim to reproduce an expert reasoning logic. The latter, also called numerical methods, use data mining technics in order to extract knowledge. Our contribution consists of a critical evaluation of the available technological alternatives as well as a synthesis of crucial recommandations, learnt through experts’ works and surveys in the field of diagnostic support.
Besides involved software and hardware architectures, two classes of decision support approaches are considered : knowledge-based and non-knowledge-based. The former is based on modeling medical
knowledge, it led to the expert systems which aim to reproduce an expert reasoning logic. The latter, also called numerical methods, use data mining technics in order to extract knowledge. Our contribution consists of a critical evaluation of the available technological alternatives as well as a synthesis of crucial recommandations, learnt through experts’ works and surveys in the field of diagnostic support.