A Self-adaptive Fireworks Algorithm for Classification Problems

his work was supported in part by the National Natural Science Foundation of China under Grants 61403206 and 61771258, in part by the Natural Science Foundation of Jiangsu Province under Grants BK20141005 and BK20160910, in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 14KJB520025, in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions, in part by the Open Research Fund of Jiangsu Engineering Research Center of Communication and Network Technology, NJUPT, under Grant JSGCZX17001, and in part by the Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, under Contract SKL2017CP01.Peer reviewedPublisher PD

Swarm Intelligence and Metaphorless Algorithms for Solving Nonlinear Equation Systems

The simplicity, flexibility, and ease of implementation have motivated the use of population-based metaheuristic optimization algorithms. By focusing on two classes of such algorithms, particle swarm optimization (PSO) and the metaphorless Jaya algorithm, this thesis proposes to explore the capacity of these algorithms and their respective variants to solve difficult optimization problems, in particular systems of nonlinear equations converted into nonlinear optimization problems. For a numerical comparison to be made, the algorithms and their respective variants were implemented and tested several times in order to achieve a large sample that could be used to compare these approaches as well as find common methods that increase the effectiveness and efficiency of the algorithms. One of the approaches that was explored was dividing the solution search space into several subspaces, iteratively running an optimization algorithm on each subspace, and comparing those results to a greatly increased initial population. The insights from these previous experiments were then used to create a new hybrid approach to enhance the capabilities of the previous algorithms, which was then compared to preexisting alternatives.A simplicidade, flexibilidade e facilidade de implementa¸c˜ao motivou o uso de algoritmos metaheur´ısticos de optimiza¸c˜ao baseados em popula¸c˜oes. Focando-se em dois destes algoritmos, optimiza¸c˜ao por exame de part´ıculas (PSO) e no algoritmo Jaya, esta tese prop˜oe explorar a capacidade destes algoritmos e respectivas variantes para resolver problemas de optimiza¸c˜ao de dif´ıcil resolu¸c˜ao, em particular sistemas de equa¸c˜oes n˜ao lineares convertidos em problemas de optimiza¸c˜ao n˜ao linear. Para que fosse poss´ıvel fazer uma compara¸c˜ao num´erica, os algoritmos e respectivas variantes foram implementados e testados v´arias vezes, de modo a que fosse obtida uma amostra suficientemente grande de resultados que pudesse ser usada para comparar as diferentes abordagens, assim como encontrar m´etodos que melhorem a efic´acia e a eficiˆencia dos algoritmos. Uma das abordagens exploradas foi a divis˜ao do espa¸co de procura em v´arios subespa¸cos, iterativamente correndo um algoritmo de optimiza¸c˜ao em cada subespa¸co, e comparar esses resultados a um grande aumento da popula¸c˜ao inicial, o que melhora a qualidade da solu¸c˜ao, por´em com um custo computacional acrescido. O conhecimento resultante dessas experiˆencias foi utilizado na cria¸c˜ao de uma nova abordagem hibrida para melhorar as capacidades dos algoritmos anteriores, a qual foi comparada a alternativas pr´e-existentes

Learning and Using Taxonomies For Fast Visual Categorization

The computational complexity of current visual categorization algorithms scales linearly at best with the number of categories. The goal of classifying simultaneously N_(cat) = 10^4 - 10^5 visual categories requires sub-linear classification costs. We explore algorithms for automatically building classification trees which have, in principle, log N_(cat) complexity. We find that a greedy algorithm that recursively splits the set of categories into the two minimally confused subsets achieves 5-20 fold speedups at a small cost in classification performance. Our approach is independent of the specific classification algorithm used. A welcome by-product of our algorithm is a very reasonable taxonomy of the Caltech-256 dataset

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.Open access funding provided by FCT|FCCN (b-on). This work has been supported by FCT— Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/ MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

An Evolutionary Computation Based Feature Selection Method for Intrusion Detection

Data Availability: The data used to support the fndings of this study are available from the corresponding author upon request. This work was supported by the National Natural Science Foundation of China (61403206, 61771258, and 61876089), the Natural Science Foundation of Jiangsu Province (BK20141005 and BK20160910), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (14KJB520025), the Priority Academic Program Development of Jiangsu Higher Education Institutions, the Open Research Fund of Jiangsu Engineering Research Center of Communication and Network Technology, NJUPT (JSGCZX17001), and the Natural Science Foundation of Jiangsu Province of China under Grant BK20140883.Peer reviewedPublisher PD

Application of Analogical Reasoning for Use in Visual Knowledge Extraction

There is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task because of its inability to learn beyond its current comprehension. Analogical reasoning (AR) has been proposed as one method to achieve this goal. Current literature lacks a technical comparison on psychologically-inspired and natural-language-processing-produced AR algorithms with consistent metrics on multiple-choice word-based analogy problems. Assessment is based on “correctness” and “goodness” metrics. There is not a one-size-fits-all algorithm for all textual problems. As contribution in visual AR, a convolutional neural network (CNN) is integrated with the AR vector space model, Global Vectors (GloVe), in the proposed, Image Recognition Through Analogical Reasoning Algorithm (IRTARA). Given images outside of the CNN’s training data, IRTARA produces contextual information by leveraging semantic information from GloVe. IRTARA’s quality of results is measured by definition, AR, and human factors evaluation methods, which saw consistency at the extreme ends. The research shows the potential for AR to facilitate more a human-like AI through its ability to understand concepts beyond its foundational knowledge in both a textual and visual problem space

Introductory Review of Swarm Intelligence Techniques

With the rapid upliftment of technology, there has emerged a dire need to fine-tune or optimize certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods of optimization through experimentation or simulation, for their generic problem-solving abilities and promising efficacy with the least human intervention. In recent times, the inducement of natural phenomena into algorithm design has immensely triggered the efficiency of optimization process for even complex multi-dimensional, non-continuous, non-differentiable and noisy problem search spaces. This chapter deals with the Swarm intelligence (SI) based algorithms or Swarm Optimization Algorithms, which are a subset of the greater Nature Inspired Optimization Algorithms (NIOAs). Swarm intelligence involves the collective study of individuals and their mutual interactions leading to intelligent behavior of the swarm. The chapter presents various population-based SI algorithms, their fundamental structures along with their mathematical models.Comment: Submitted to Springe