Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Multi-Objective Optimization in Metabolomics/Computational Intelligence

The development of reliable computational models for detecting non-linear patterns encased in throughput datasets and characterizing them into phenotypic classes has been of particular interest and comprises dynamic studies in metabolomics and other disciplines that are encompassed within the omics science. Some of the clinical conditions that have been associated with these studies include metabotypes in cancer, in ammatory bowel disease (IBD), asthma, diabetes, traumatic brain injury (TBI), metabolic syndrome, and Parkinson's disease, just to mention a few. The traction in this domain is attributable to the advancements in the procedures involved in 1H NMR-linked datasets acquisition, which have fuelled the generation of a wide abundance of datasets. Throughput datasets generated by modern 1H NMR spectrometers are often characterized with features that are uninformative, redundant and inherently correlated. This renders it di cult for conventional multivariate analysis techniques to e ciently capture important signals and patterns. Therefore, the work covered in this research thesis provides novel alternative techniques to address the limitations of current analytical pipelines. This work delineates 13 variants of population-based nature inspired metaheuristic optimization algorithms which were further developed in this thesis as wrapper-based feature selection optimizers. The optimizers were then evaluated and benchmarked against each other through numerical experiments. Large-scale 1H NMR-linked datasets emerging from three disease studies were employed for the evaluations. The rst is a study in patients diagnosed with Malan syndrome; an autosomal dominant inherited disorder marked by a distinctive facial appearance, learning disabilities, and gigantism culminating in tall stature and macrocephaly, also referred to as cerebral gigantism. Another study involved Niemann-Pick Type C1 (NP-C1), a rare progressive neurodegenerative condition marked by intracellular accrual of cholesterol and complex lipids including sphingolipids and phospholipids in the endosomal/lysosomal system. The third study involved sore throat investigation in human (also known as `pharyngitis'); an acute infection of the upper respiratory tract that a ects the respiratory mucosa of the throat. In all three cases, samples from pathologically-con rmed cohorts with corresponding controls were acquired, and metabolomics investigations were performed using 1H NMR technique. Thereafter, computational optimizations were conducted on all three high-dimensional datasets that were generated from the disease studies outlined, so that key biomarkers and most e cient optimizers were identi ed in each study. The clinical and biochemical signi cance of the results arising from this work were discussed and highlighted

Improving K-means clustering with enhanced Firefly Algorithms

In this research, we propose two variants of the Firefly Algorithm (FA), namely inward intensified exploration FA (IIEFA) and compound intensified exploration FA (CIEFA), for undertaking the obstinate problems of initialization sensitivity and local optima traps of the K-means clustering model. To enhance the capability of both exploitation and exploration, matrix-based search parameters and dispersing mechanisms are incorporated into the two proposed FA models. We first replace the attractiveness coefficient with a randomized control matrix in the IIEFA model to release the FA from the constraints of biological law, as the exploitation capability in the neighbourhood is elevated from a one-dimensional to multi-dimensional search mechanism with enhanced diversity in search scopes, scales, and directions. Besides that, we employ a dispersing mechanism in the second CIEFA model to dispatch fireflies with high similarities to new positions out of the close neighbourhood to perform global exploration. This dispersing mechanism ensures sufficient variance between fireflies in comparison to increase search efficiency. The ALL-IDB2 database, a skin lesion data set, and a total of 15 UCI data sets are employed to evaluate efficiency of the proposed FA models on clustering tasks. The minimum Redundancy Maximum Relevance (mRMR)-based feature selection method is also adopted to reduce feature dimensionality. The empirical results indicate that the proposed FA models demonstrate statistically significant superiority in both distance and performance measures for clustering tasks in comparison with conventional K-means clustering, five classical search methods, and five advanced FA variants

Evolving machine learning and deep learning models using evolutionary algorithms

Despite the great success in data mining, machine learning and deep learning models are yet subject to material obstacles when tackling real-life challenges, such as feature selection, initialization sensitivity, as well as hyperparameter optimization. The prevalence of these obstacles has severely constrained conventional machine learning and deep learning methods from fulfilling their potentials. In this research, three evolving machine learning and one evolving deep learning models are proposed to eliminate above bottlenecks, i.e. improving model initialization, enhancing feature representation, as well as optimizing model configuration, respectively, through hybridization between the advanced evolutionary algorithms and the conventional ML and DL methods. Specifically, two Firefly Algorithm based evolutionary clustering models are proposed to optimize cluster centroids in K-means and overcome initialization sensitivity as well as local stagnation. Secondly, a Particle Swarm Optimization based evolving feature selection model is developed for automatic identification of the most effective feature subset and reduction of feature dimensionality for tackling classification problems. Lastly, a Grey Wolf Optimizer based evolving Convolutional Neural Network-Long Short-Term Memory method is devised for automatic generation of the optimal topological and learning configurations for Convolutional Neural Network-Long Short-Term Memory networks to undertake multivariate time series prediction problems. Moreover, a variety of tailored search strategies are proposed to eliminate the intrinsic limitations embedded in the search mechanisms of the three employed evolutionary algorithms, i.e. the dictation of the global best signal in Particle Swarm Optimization, the constraint of the diagonal movement in Firefly Algorithm, as well as the acute contraction of search territory in Grey Wolf Optimizer, respectively. The remedy strategies include the diversification of guiding signals, the adaptive nonlinear search parameters, the hybrid position updating mechanisms, as well as the enhancement of population leaders. As such, the enhanced Particle Swarm Optimization, Firefly Algorithm, and Grey Wolf Optimizer variants are more likely to attain global optimality on complex search landscapes embedded in data mining problems, owing to the elevated search diversity as well as the achievement of advanced trade-offs between exploration and exploitation

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

Inductive biases and metaknowledge representations for search-based optimization

"What I do not understand, I can still create."- H. Sayama The following work follows closely the aforementioned bonmot. Guided by questions such as: ``How can evolutionary processes exhibit learning behavior and consolidate knowledge?´´, ``What are cognitive models of problem-solving?´´ and ``How can we harness these altogether as computational techniques?´´, we clarify within this work essentials required to implement them for metaheuristic search and optimization.We therefore look into existing models of computational problem-solvers and compare these with existing methodology in literature. Particularly, we find that the meta-learning model, which frames problem-solving in terms of domain-specific inductive biases and the arbitration thereof through means of high-level abstractions resolves outstanding issues with methodology proposed within the literature. Noteworthy, it can be also related to ongoing research on algorithm selection and configuration frameworks. We therefore look in what it means to implement such a model by first identifying inductive biases in terms of algorithm components and modeling these with density estimation techniques. And secondly, propose methodology to process metadata generated by optimization algorithms in an automated manner through means of deep pattern recognition architectures for spatio-temporal feature extraction. At last we look into an exemplary shape optimization problem which allows us to gain insight into what it means to apply our methodology to application scenarios. We end our work with a discussion on future possible directions to explore and discuss the limitations of such frameworks for system deployment

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Sine Cosine Algorithm for Optimization

This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA