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4th International Workshop on Hybrid Artificial Intelligence Systems

作者： Carmona, C. J. Gonzalez, P. del Jesus, M. J. Herrera, F. Univ Jaen Dept Comp Sci Jaen Spain AI Univ Granada Dept Comp Sci Granada Spain

ISBN: (纸本)9783642023187

A new multi-objective evolutionary model for Subgroup discovery with fuzzy rules is presented in this paper. The method resolves Subgroup discovery problems based on the hybridization between fuzzy logic and genetic algorithms, with the aim of extracting interesting, novel and interpretable fuzzy rules. To do so, the algorithm includes different mechanisms for improving diversity in the population. This proposal focuses on the classification of individuals in fronts, based on non-dominated sort. A study can be seen for the proposal and other previous methods for different databases. In this study good results are obtained for subgroup discovery by this new evolutionary model in comparison with existing algorithms.

关键词： Data mining Subgroup Discovery multi-objective evolutionary algorithms Fuzzy Rules Genetic Fuzzy Systems

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Enhancing Deep Vein Thrombosis Diagnosis with multi-objective evolutionary Algorithm and Machine Learning 4

Enhancing Deep Vein Thrombosis Diagnosis with Multi-Objectiv...

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4th International Conference on Applied Artificial Intelligence (ICAPAI)

作者： Sorano, Ruslan Ripon, Kazi Shah Nawaz Magnusson, Lars Vidar Halstensen, Thor-David Ghanima, Waleed Ostfold Univ Coll Dept Comp Sci & Commun Halden Norway Oslo Metropolitan Univ Dept Comp Sci Oslo Norway Ostfold Univ Coll Dept Hlth Welf & Org Halden Norway Ostfold Hosp Trust Dept Res Gralum Norway

ISBN: (纸本)9798350349764;9798350349771

Deep vein thrombosis is a serious medical condition requiring prompt and accurate diagnosis. The identification of thrombosis presents a challenging task characterized by conflicting objectives. Maintaining a delicate balance between optimizing overall diagnostic accuracy and averting the misclassification of ill patients as healthy is paramount in the diagnostic process. Our earlier works focused on optimizing the disease prediction accuracy in machine learning models by experimenting with different techniques. We employed single-objective optimization to fine-tune the classification threshold. Additionally, we applied a multi-objective evolutionary algorithm for hyperparameter optimization, both independently and in combination with feature reduction. Expanding on our previous works, this work employs a multi-objective evolutionary algorithm that concurrently tunes hyperparameters, reduces features, and adjusts the classification threshold. By addressing the inherent conflicting objectives in thrombosis diagnostics, the proposed approach generates a set of Pareto-optimal solutions, representing a balance between maximizing overall diagnostic accuracy and minimizing false negatives. Experimental results indicate that this approach enhances the outcomes of the deep vein thrombosis diagnosis prediction, effectively navigating the trade-off in competing objectives for improved clinical efficacy.

关键词： multi-objective evolutionary algorithms healthcare applications decision support deep vein thrombosis hyperparameter tuning feature reduction classification threshold

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Towards a Pareto Front Shape Invariant multi-objective evolutionary Algorithm Using Pair-Potential Functions 1

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20th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Marquez-Vega, Luis A. Guillermo Falcon-Cardona, Jesus Covantes Osuna, Edgar Tecnol Monterrey Sch Engn & Sci Monterrey 64849 Nuevo Leon Mexico UAM Cuajimalpa Dept Appl Math & Syst Mexico City 05348 DF Mexico

ISBN: (数字)9783030898175

ISBN: (纸本)9783030898175;9783030898168

Reference sets generated with uniformly distributed weight vectors on a unit simplex are widely used by several multi-objective evolutionary algorithms (MOEAs). They have been employed to tackle multi-objective optimization problems (MOPs) with four or more objective functions, i.e., the so-called many-objective optimization problems. These MOEAs have shown a good performance on MOPs with regular Pareto front shapes, i.e., simplex-like shapes. However, it has been observed that in many cases, their performance degrades on MOPs with irregular Pareto front shapes. In this paper, we designed a new selection mechanism that aims to promote a Pareto front shape invariant performance of MOEAs that use weight vector-based reference sets. The newly proposed selection mechanism takes advantage of weight vector-based reference sets and seven pair-potential functions. It was embedded into the non-dominated sorting genetic algorithm III (NSGA-III) to increase its performance on MOPs with different Pareto front geometries. We use the DTLZ and DTLZ(-1) test problems to perform an empirical study about the usage of these pair-potential functions for this selection mechanism. Our experimental results show that the pair-potential functions can enhance the distribution of solutions obtained by weight vector-based MOEAs on MOPs with irregular Pareto front shapes. Also, the proposed selection mechanism permits maintaining the good performance of these MOEAs on MOPs with regular Pareto front shapes.

关键词： Reference sets multi-objective evolutionary algorithms Pareto front shape invariant performance Pair-potential functions

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Machine scheduling in custom furniture industry through neuro-evolutionary hybridization

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APPLIED SOFT COMPUTING 2011年第2期11卷 1600-1613页

作者： Vidal, Juan C. Mucientes, Manuel Bugarin, Alberto Lama, Manuel Univ Santiago de Compostela Dept Elect & Comp Sci Santiago De Compostela 15782 Spain

Machine scheduling is a critical problem in industries where products are custom-designed. The wide range of products, the lack of previous experiences in manufacturing, and the several conflicting criteria used to evaluate the quality of the schedules define a huge search space. Furthermore, production complexity and human influence in each manufacturing step make time estimations difficult to obtain thus reducing accuracy of schedules. The solution described in this paper combines evolutionary computing and neural networks to reduce the impact of (i) the huge search space that the multi-objective optimization must deal with and (ii) the inherent problem of computing the processing times in a domain like custom manufacturing. Our hybrid approach obtains near optimal schedules through the Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with time estimations based on multilayer perceptron neural networks. (C) 2010 Elsevier B. V. All rights reserved.

关键词： Machine scheduling optimization multi-objective evolutionary algorithms NSGA-II Neural networks Hybrid approach

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multi-objective analysis of a component-based representation within an interactive evolutionary design system

Multi-objective analysis of a component-based representation...

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7th International Conference on Adaptive Computing in Design and Manufacture (ACDM)

作者： Machwe, A. T. Parmee, I. C. Univ W England ACDDM Grp Fac Comp Engn & Math Sci Bristol BS16 1QY Avon England

This article describes research relating to a user-centered evolutionary design system that evaluates both engineering and aesthetic aspects of design solutions during early-stage conceptual design. The experimental system comprises several components relating to user interaction, problem representation, evolutionary search and exploration and online learning. The main focus of the article is the evolutionary aspect of the system when using a single quantitative objective function plus subjective judgment of the user. Additionally, the manner in which the user-interaction aspect affects system output is assessed by comparing Pareto frontiers generated with and without user interaction via a multi-objective evolutionary algorithm (MOEA). A solution clustering component is also introduced and it is shown how this can improve the level of support to the designer when dealing with a complex design problem involving multiple objectives. Supporting results are from the application of the system to the design of urban furniture which, in this case, largely relates to seating design.

关键词： multi-objective evolutionary algorithms interactive evolutionary design systems representation clustering

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Designing the Optical Network of Haiti Using a multiobjective evolutionary Approach

Designing the Optical Network of Haiti Using a Multiobjectiv...

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3rd IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Dupleix, Vital Araujo, Danilo R. B. Bastos-Filho, Carmelo J. A. Univ Pernambuco BR-50720001 Recife PE Brazil Univ Fed Rural Pernambuco BR-52171900 Recife PE Brazil

ISBN: (纸本)9781509051052

The Haitian Republic currently does not have a suitable telecommunication network to support the novel information technologies in all the principal cities of the country. Therefore, it is a real concern to design an appropriate telecommunication infrastructure to cover all the national territory aiming to support the offer of these services in the country. The optical networks of Digicel and Natcom were the first two highspeed networks designed in the metropolitan area of Haiti, but these systems are not sufficient to satisfy the population needs regarding access to a high-quality broadband Internet services. This paper aims to design a suitable optical backbone network for Haiti based on the recent advances in multi-objective evolutionary algorithms. According to our results, it is possible to create suitable physical topologies successfully by using socio-economics data from the cities and the geographical locations of the terminal nodes.

关键词： Optical networks physical topology design multi-objective evolutionary algorithms graph theory geographic graph models

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On the Performance of multiple objective evolutionary algorithms for Software Architecture Discovery 14

On the Performance of Multiple Objective Evolutionary Algori...

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16th Genetic and evolutionary Computation Conference (GECCO)

作者： Ramirez, Aurora Raul Romero, Jose Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

ISBN: (纸本)9781450326629

During the design of complex systems, software architects have to deal with a tangle of abstract artefacts, measures and ideas to discover the most fitting underlying architecture. A common way to structure these systems is in terms of their interacting software components, whose composition and connections need to be properly adjusted. Its abstract and highly combinatorial nature increases the complexity of the problem. In this scenario, Search-based Software Engineering (SBSE) may serve to support this decision making process from initial analysis models, since the discovery of component-based architectures can be formulated as a challenging multiple optimisation problem, where different metrics and configurations can be applied depending on the design requirements and its specific domain. Many-objective optimisation evolutionary algorithms can provide an interesting alternative to classical multi-objective approaches. This paper presents a comparative study of five different algorithms, including an empirical analysis of their behaviour in terms of quality and variety of the returned solutions. Results are also discussed considering those aspects of concern to the expert in the decision making process, like the number and type of architectures found. The analysis of many-objectives algorithms constitutes an important challenge, since some of them have never been explored before in SBSE.

关键词： Search-based Software Engineering component-based software architecture multi-objective evolutionary algorithms many-objective evolutionary algorithms

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Scheduling for the National Hockey League Using a multi-objective evolutionary Algorithm

Scheduling for the National Hockey League Using a Multi-obje...

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22nd Australian Joint Conference on Artificial Intelligence

作者： Craig, Sam While, Lyndon Barone, Luigi Univ Western Australia Nedlands WA 6009 Australia

ISBN: (纸本)9783642104381

We describe a multi-objective evolutionary algorithm that derives schedules for the National Hockey League according to three objectives: minimising the teams' total travel, promoting equity in rest time between games, and minimising long streaks of home or away games. Experiments show that the system is able to derive schedules that beat the 2008-9 NHL schedule in all objectives simultaneously, and that it returns a set of schedules that offer a range of trade-offs across the objectives.

关键词： Sports scheduling multi-objective evolutionary algorithms

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Utilization of evolutionary algorithms for making the environmental game much more exciting

Utilization of evolutionary algorithms for making the enviro...

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International Conference on Control, Automation and Systems

作者： Baba, Norio Handa, Hisashi Information Science Osaka Kyoiku University Osaka Prefecture Japan Natural Science and Technology Okayama University Okayama City Japan

ISBN: (纸本)9788995003879

In this paper, we suggest that EAs could be utilized for making the Environmental Game become much more exciting. In particular, in order attain this objective, we try to utilize evolutionary algorithms in the following steps: (1) First, we consider a new way for producing a return for each player. (2) Second, we utilize multi-objective evolutionary algorithms (MOEAs) to generate various skilled players whose decision making concerning the investment to the chemical goods (and consideration toward environmental protection) can strongly affect the environmental state of the sea. (3) Further, we utilize evolutionary Programming (EP) to derive appropriate rules which could be used to help players fully enjoy game playing. Several game playing (utilizing the new rule) by our students suggest the effectiveness of our approach.

关键词： environmental game gaming neural networks multi-objective evolutionary algorithms fast evolutionary programming educational gaming player's satisfaction

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A multi-objective Neuro-evolutionary Algorithm to Obtain Interpretable Fuzzy Models

A Multi-objective Neuro-evolutionary Algorithm to Obtain Int...

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13th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA 2009)

作者： Sanchez, Gracia Jimenez, Fernando Sanchez, Jose F. Alcaraz, Jose M. Univ Murcia Dept Ingn Informac & Comunicac E-30001 Murcia Spain

ISBN: (纸本)9783642142635

In this paper, a multi-objective constrained optimization model is proposed to improve interpretability of TSK fuzzy models. This approach allows a linguistic approximation of the fuzzy models. A multi-objective evolutionary algorithm is implemented with three different selection and generational replacements schemata (Niched Preselection, NSGA-II and ENORA) to generate fuzzy models in the proposed optimization context. The results clearly show a real ability and effectiveness of the proposed approach to find accurate and interpretable TSK fuzzy models. These schemata have been compared in terms of accuracy, interpretability and compactness by using three test problems studied in literature. Statistical tests have also been used with optimality and diversity multi-objective metrics to compare the schemata.

关键词： TSK Fuzzy Models multi-objective evolutionary algorithms RBF Neural Networks

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