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Evolutionary Computation - Science topic

Evolutionary computation is a subfield computational intelligence that involves combinatorial optimization problems. Evolutionary computation uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution.
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Publications related to Evolutionary Computation (10,000)
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Computational time and solution precision are two major concerns in evolutionary computation (EC). Although high-performance computing techniques have been applied to reduce the computational time of meta-heuristic algorithms, it does not mean that they can assist meta-heuristic algorithms in finding a high-quality solution. Moreover, most of meta-...
Article
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Harris Hawk Optimization (HHO) algorithm is a new population-based and nature-inspired optimization paradigm, which has strong global search ability, but its diversified local search strategies easily make it fall into local optimum. In order to enhance its search mechanism and speed of convergence, an new improved HHO algorithm based on the invers...
Preprint
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Volume with the Late-Breaking Abstracts submitted to the Evo* 2022 Conference, held in Madrid (Spain), from 20 to 22 of April. These papers present ongoing research and preliminary results investigating on the application of different approaches of Bioinspired Methods (mainly Evolutionary Computation) to different problems, most of them real world...
Preprint
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A novel meta-heuristic algorithm, Egret Swarm Optimization Algorithm (ESOA), is proposed in this paper, which is inspired by two egret species' (Great Egret and Snowy Egret) hunting behavior. ESOA consists of three primary components: Sit-And-Wait Strategy, Aggressive Strategy as well as Discriminant Conditions. The performance of ESOA on 36 benchm...
Chapter
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This book lays down the technological foundation for and introduces key artificial intelligence (AI) concepts and technologies for the digitising industry. While this chapter does not exhaustively cover all types of AI, it comprehensively prioritises the features of AI-based industrial applications and designs and defines the reference terminology...
Chapter
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The effect of the COVID-19 pandemic has prompted a large number of studies targeted at understanding, monitoring, and containing the disease. However, it is still unclear whether the studies performed so far have filled existing knowledge gaps. We used computational intelligence (CI)/Machine Learning (ML) technologies and alliance areas to analyse...
Conference Paper
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Suppose the evolutionary dynamics of a population of individuals is substantially influenced by the relationships between the individuals. Then, the relations between the individuals form a network and a natural mathematical description of their dynamics is by elements of network science, and particularly by evolutionary graph theory. This tutorial...
Preprint
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EC-KitY is a comprehensive Python library for doing evolutionary computation (EC), licensed under GNU General Public License v3.0, and compatible with scikit-learn. Designed with modern software engineering and machine learning integration in mind, EC-KitY can support all popular EC paradigms, including genetic algorithms, genetic programming, coev...
Article
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Image media are used by people to perceive the world’s material reality and spiritual symbols. Traditional folk art images, unlike natural scene images, are characterized by “form to write God.” Their semantic data are more abstract and detailed. As a result, folk art images limit the use of low-level visual feature descriptors in natural images. B...
Article
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Fusion–Fission Optimization (FuFiO) is proposed as a new metaheuristic algorithm that simulates the tendency of nuclei to increase their binding energy and achieve higher levels of stability. In this algorithm, nuclei are divided into two groups, namely stable and unstable. Each nucleus can interact with other nuclei using three different types of...
Preprint
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NeuroEvolution automates the generation of Artificial Neural Networks through the application of techniques from Evolutionary Computation. The main goal of these approaches is to build models that maximize predictive performance, sometimes with an additional objective of minimizing computational complexity. Although the evolved models achieve compe...
Article
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Complex system optimization is an emerging research topic in the field of evolutionary computation, whose goal is to handle complex systems with multiple coupled subsystems, each including multiple objectives and multiple constraints in real-world applications. This paper proposes a multi-system genetic algorithm (MSGA), stemming from implicit para...
Article
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This paper presents a parallel evolutionary metaheuristic which includes different threads aimed at balancing exploration versus exploitation. Exploring different areas of the search space independently, each thread also communicates with other threads, and exploits the search space by improving a common high quality solution. The presented metaheu...
Article
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Traditional frequent itemsets mining (FIM) suffers from the vast memory cost, small processing speed and insufficient disk space requirements. FIM assumes only binary frequency value for items in the dataset and assumes equal importance value for items. In order to target all these limitations of FIM, high-utility itemsets (HUIs) mining has been pr...
Conference Paper
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Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife. Sensors (e.g. drones equipped with cameras) have also begun to play a role in these scenarios by providing real-time information. Incorporating both human and sensor defender resources strategically is the subject of re...
Article
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A number of problems from specifiers for Boolean networks to programs for quantum computers can be encoded as matrices. The paper presents a novel family of linear, generative representations for evolving matrices. The matrices can be general or restricted within special classes of matrices like permutation matrices, Hermitian matrices, or other gr...
Article
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HUIM has been an important issue in recent years, particularly in basket-market analysis, since it identifies useful information or goods for decision-making. Numerous research focused on extracting high-utility itemsets from datasets, revealing a tremendous amount of pattern information. This approach is incapable of providing correct choices in a...
Preprint
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Evolutionary transfer optimization(ETO) serves as "a new frontier in evolutionary computation research", which will avoid zero reuse of experience and knowledge from solved problems in traditional evolutionary computation. In scheduling applications via ETO, a highly competitive "meeting" framework between them could be constituted towards both int...
Article
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A genetic algorithm and an artificial neural network are deployed for the design of a dynamic multi-layered façade system that adapts in real-time to different weather and occupants’ needs scenarios. The outputs are a set of different performances of the façade insulation cushions, optimized by the previous run of the genetic algorithm. A façade sy...
Preprint
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During "the upgrading of the production process and management modes" [2] in China, knowledge acts as both "the core production factor" [52] and "a new mode of innova-tion"[52] for smart manufacturing scheduling(SMS) towards carbon neutrality. Then disentangling the knowledge plays an important role in SMS, especially when the knowledge is conveyed...
Article
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Evolutionary transfer optimization(ETO) serves as "a new frontier in evolutionary computation research", which will avoid zero reuse of experience and knowledge from solved problems in traditional evolutionary computation. In schedule-ing applications via ETO, a highly competitive "meeting" framework between them could be constituted towards both i...
Preprint
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As "a new frontier in evolutionary computation research", evolutionary transfer optimization(ETO) will overcome the traditional paradigm of zero reuse of related experience and knowledge from solved past problems in researches of evolutionary computation. In scheduling applications via ETO, a quite appealing and highly competitive framework "meetin...
Article
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This study proposes the Fire Hawk Optimizer (FHO) as a novel metaheuristic algorithm based on the foraging behavior of whistling kites, black kites and brown falcons. These birds are termed Fire Hawks considering the specific actions they perform to catch prey in nature, specifically by means of setting fire. Utilizing the proposed algorithm, a num...
Preprint
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Parameter adaptation, that is the capability to automatically adjust an algorithm's hyperparameters depending on the problem being faced, is one of the main trends in evolutionary computation applied to numerical optimization. While several handcrafted adaptation policies have been proposed over the years to address this problem, only few attempts...
Article
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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the...
Article
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Nowadays, the electrical power system has become a more complex, interconnected network that is expanding every day. Hence, the power system faces many problems such as increasing power losses, voltage deviation, line overloads, etc. The optimization of real and reactive power due to the installation of energy resources at appropriate buses can min...
Chapter
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A current boom in the modeling intelligence in algorithm to solve complex applications , this intelligence could be achieved through natural and biological intelligence, resulted a technology known as intelligent systems, these algorithms use soft computing tools. AI aim to make the machines and computers smarter, that make a computer to mimic like...
Article
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Software defect prediction (SDP) is the most fascinating research area in software industry to enhance the quality of software products. SDP classifiers predict the fault-prone modules in early development phases prior to begin testing phase, and thence, the testing efforts can be focused to those predicted fault-prone modules. In this way, the ear...
Article
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The single-objective version of stochastic paint optimizer (SPO) is appropriately changed to solve multi-objective optimization problems described as MOSPO. Color theory, the color wheel, and color combination methods are the main concepts of SPO. The SPO will be able to do excellent exploration and exploitation thanks to four simple color combinat...
Article
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During the signal identification process, massive multiple-input multiple-output (MIMO) systems must manage a high quantity of matrix inversion operations. To prevent exact matrix inversion in huge MIMO systems, several strategies have been presented, which can be loosely classified into similarity measures and evolutionary computation. In the exis...
Article
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Research into automatically searching for an optimal neural network (NN) by optimisation algorithms is a significant research topic in deep learning and artificial intelligence. However, this is still challenging due to two issues: Both the hyperparameter and architecture should be optimised and the optimisation process is computationally expensive...
Article
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The proper management of diversity is essential to the success of Evolutionary Algorithms. Specifically, methods that explicitly relate the amount of diversity maintained in the population to the stopping criterion and elapsed period of execution, with the aim of attaining a gradual shift from exploration to exploitation, have been particularly suc...
Article
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This paper investigates the integration of a surrogate-assisted multi-objective evolutionary algorithm (MOEA) and a parallel computation scheme to reduce the computing time until obtaining the optimal solutions in evolutionary algorithms (EAs). A surrogate-assisted MOEA solves multi-objective optimization problems while estimating the evaluation of...
Article
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Real-time strategy (RTS) games differ as they persist in varying scenarios and states. These games enable an integrated correspondence of non-player characters (NPCs) to appear as an autodidact in a dynamic environment, thereby resulting in a combined attack of NPCs on human-controlled character (HCC) with maximal damage. This research aims to empo...
Article
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Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations have become state of the art in algorithm design for solving real-world optimization problems. Still, it is difficult for practitioners to get an overview that explains their advantages in comparison to a large number of available methods in the scope of optimizat...
Article
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Yin–Yang-pair Optimization (YYPO) is a recently developed philosophy-inspired meta-heuristic algorithm, which works with two main points for exploitation and exploration, respectively, and then generates more points via splitting to search the global optimum. However, it suffers from low quality of candidate solutions in its exploration process owi...
Article
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Runtime analysis aims at contributing to our understanding of evolutionary algorithms through mathematical analyses of their runtimes. In the context of discrete optimization problems, runtime analysis classically studies the time needed to find an optimal solution. However, both from a practical and from a theoretical viewpoint, more fine-grained...
Article
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One hope when using non-elitism in evolutionary computation is that the ability to abandon the current-best solution aids leaving local optima. To improve our understanding of this mechanism, we perform a rigorous runtime analysis of a basic non-elitist evolutionary algorithm (EA), the $$(\mu ,\lambda )$$ EA, on the most basic benchmark function wi...
Article
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This paper presents an art-inspired optimization algorithm, which is called Stochastic Paint Optimizer (SPO). The SPO is a population-based optimizer inspired by the art of painting and the beauty of colors plays the main role in this algorithm. The SPO, as an optimization algorithm, simulates the search space as a painting canvas and applies a dif...
Article
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While the concept of swarm intelligence was introduced in 1980s, the first swarm optimisation algorithm was introduced a decade later, in 1992. In this paper, nineteen representative original swarm optimisation algorithms are analysed to extract their common features and design a taxonomy for swarm optimisation. We use twenty-nine benchmark problem...
Article
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Learning Classifier Systems (LCSs) are a group of rule-based evolutionary computation techniques, which have been frequently applied to data mining tasks. The LCSs’ rules are designed to be human-readable to enable the underlying knowledge to be investigated. However, the models for the majority of domains with high feature interaction contain a la...
Article
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The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the...
Article
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Engineering design can be regarded as an iterative optimization process. This process is difficult because of two main problems: the first is that computer-aided engineering (CAE) is time-consuming in terms of evaluating design solutions, while the second is the high dimensionality of design solutions. In the research community, a surrogate model i...
Article
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Jump functions are the most-studied non-unimodal benchmark in the theory of randomized search heuristics, in particular, evolutionary algorithms (EAs). They have significantly improved our understanding of how EAs escape from local optima. However, their particular structure—to leave the local optimum one can only jump directly to the global optimu...
Article
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The future pedagogical systems need anthropocentric inclusive educational programs in which the goal should be adjustable according to the knowledge requirements, intelligence, and learning objective of each student. Prioritizing these needs, innovative AI methods are required to assist and ensure the making of conscious educational decisions, in t...
Article
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Multifactorial evolution algorithm (MFEA) is a powerful search paradigm with the purpose of addressing multiple optimization tasks simultaneously in the field of evolutionary computation. The assortative mating of MFEA is a key component to make it outperform traditional single-task optimization algorithms. However, the optimal solution of each gen...
Article
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Neuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications. Contributions of this study to the neuroevolutionary machine learning area are twofold: firstly, this study presents an evolutionary field theorem of search agents and sugge...
Article
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Among the evolutionary methods, one that is quite prominent is genetic programming. In recent years, a variant called geometric semantic genetic programming (GSGP) was successfully applied to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all its evolutionary history, i.e., all populations from the first o...
Presentation
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Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a stochastic manner. They can offer a reliable and effective approach to address complex problems in real-world applications. EC algorithms have recently been used to improve the performance of Machine Learning (ML) models and the quality of their res...
Article
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Many crucial tasks in weather prediction require large-scale and long-term spatio-temporal predictions. However, these tasks usually face three challenges: high feature redundancy, dependence of long-term prediction, and complexity in spatial relations of geographical location. To overcome these challenges, the Graph Evolution-based Spatio-Temporal...
Article
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Real-world problems often contain complex structures and various variables, and classical optimization techniques may face difficulties finding optimal solutions. Hence, it is essential to develop efficient and robust techniques to solve these problems. Computational intelligence (CI) optimization methods, such as swarm intelligence (SI) and evolut...
Article
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With the continuous development of evolutionary computing, many excellent algorithms have emerged, which are applied in all walks of life to solve various practical problems. In this paper, two hybrid fish, bird and insect algorithms based on different architectures are proposed to solve the optimal coverage problem in wireless sensor networks. The...
Article
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In this work, we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The principal goal when dealing with this scenario is to dynamically exploit the existing complementarities among the problems (tasks) being optimized, helping each other through the exchange of valua...
Article
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Several organizations are implementing large-scale Internet of Things (IoT)-based ecosystems, such as Industrial IoT (IIoT). Such systems are vulnerable to new threats and intrusions because of the nature of their networks. It is necessary to secure such systems by developing feature selection integrated with robust machine learning models. In this...
Article
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The solving of large-scale multi-objective optimization problem (LSMOP) has become a hot research topic in evolutionary computation. To better solve this problem, this paper proposes a self-organizing weighted optimization based framework, denoted S-WOF, for addressing LSMOPs. Compared to the original framework, there are two main improvements in o...