Advances in computational intelligence

Miguel Atencia, Gonzalo Joya, Francisco García-Lagos
2019 Neural computing & applications (Print)  
The 14th edition of the biennial International Work-Conference on Artificial Neural Networks (IWANN 2017) gathered together more than one hundred significant contributors in the fields of artificial neural networks, machine learning, computational intelligence, and related topics. After the conference, which was held in Cádiz (Spain), in June 2017, a set of outstanding papers were selected and their authors invited to prepare an extended version. This special issue of Neural Computing and
more » ... ations comprises the nine papers that were finally accepted after a thorough reviewing process. The IWANN events were born in 1991, and are currently co-chaired by researchers from the Universidad de Málaga, the Universidad de Granada, and the Universitat Politècnica de Catalunya. Since its inception, all the papers accepted to the conferences have been collected in volumes published by Springer Verlag in the book series Lecture Notes in Computer Science [3-5, 7, 11-20, 24, 26-30, 33]. Besides, a number of special issues of top-tier journals have comprised selected papers from the latest editions, which were revised and substantially extended. These special issues were published in Neurocomputing [2, 6, 21, 22, 31, 32] , Neural Processing Letters [1, 8, 23] , and Soft Computing [25] . Despite the difficult delimitation of the field, the basic ideas of Neural Computation [10] have always somehow been present in all the advances: parallelism and learning. The range of applications has also been more and more enlarged, so nowadays machine learning is pervasive, not only in scientific or engineering disciplines, but also in all aspects of society. Undeniably, the major breakthrough in the last decade is the success of Deep Learning [9] algorithms. Yet the application of computational intelligence techniques to critical tasks requires a continued research effort to provide both rigorous support and explanation to decision making. Some of the papers selected for this special issue focus on principled applications of machine learning to diverse important real-world tasks, including engineering, medical diagnostics, image processing, and bioinformatics. Other contributions use such applications as a proof of concept to design, analyse, and test new architectures or parametersetting methodologies. Therefore, the interaction between theoretical advances and applied results leads to a synergy that is consistent with the aims and scope of Neural Computing and Applications. The nine accepted papers can be roughly classified into three categories: -Biological applications Three contributions place themselves at the frontier of computational intelligence and biomedical disciplines. -The paper Analysis on the potential of an EAsurrogate modelling tandem for deep learning parametrization: an example for cancer classification from medical images by Ruxandra Stoean, introduces a novel methodology to efficiently set the parameters of a convolutional neural network and rank the importance of the involved variables, in the context of medical image interpretation. -The authors
doi:10.1007/s00521-019-04324-4 fatcat:234n6cxenbfkzls3hp42mw7x3e