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There are three difficult problems in the application of genetic algorithms, namely, the parameter con- trol, the premature convergence and the deception problem. Based on genetic algorithm with varying population size, a self-ada...
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There are three difficult problems in the application of genetic algorithms, namely, the parameter con- trol, the premature convergence and the deception problem. Based on genetic algorithm with varying population size, a self-adaptive genetic algorithm called natural genetic algorithm (NGA) is proposed. This algorithm introduces the population size threshold and the immigrant concepts, and adopts dynamically changing parameters. The design and structure of NGA are discussed in this paper. The performance of NGA is also analyzed.
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During the last two decades, the water resources planning and management profession has seen a dramatic increase in the development and application of various types of evolutionary algorithms (EAs). This observation is especially ...
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During the last two decades, the water resources planning and management profession has seen a dramatic increase in the development and application of various types of evolutionary algorithms (EAs). This observation is especially true for application of genetic algorithms, arguably the most popular of the several types of EAs. Generally speaking, EAs repeatedly prove to be flexible and powerful tools in solving an array of complex water resources problems. This paper provides a comprehensive review of state-of-the-art methods and their applications in the field of water resources planning and management. A primary goal in this ASCE Task Committee effort is to identify in an organized fashion some of the seminal contributions of EAs in the areas of water distribution systems, urban drainage and sewer systems, water supply and wastewater treatment, hydrologic and fluvial modeling, groundwater systems, and parameter identification. The paper also identifies major challenges and opportunities for the future, including a call to address larger-scale problems that are wrought with uncertainty and an expanded need for cross fertilization and collaboration among our field's subdisci-plines. Evolutionary computation will continue to evolve in the future as we encounter increased problem complexities and uncertainty and as the societal pressure for more innovative and efficient solutions rises.
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GAs and their relations, which fall under the umbrella term evolutionary computing, are being harnessed to optimize designs of all sorts. GAs mimics the mechanisms of biological evolution. Populations of individuals evolve by mean...
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GAs and their relations, which fall under the umbrella term evolutionary computing, are being harnessed to optimize designs of all sorts. GAs mimics the mechanisms of biological evolution. Populations of individuals evolve by means of reproduction, inheritance, mutation, natural selection, and recombination or crossover (two organisms swap a portion of their genetic code). The result is computational methods that build a population of individuals or designs based on a set of criteria and constraints.
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This paper aims to provide an introduction to genetic algorithms and their three main components, i.e., the representation of solutions and their modification through mutation and crossover operators. It has been specifically desi...
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This paper aims to provide an introduction to genetic algorithms and their three main components, i.e., the representation of solutions and their modification through mutation and crossover operators. It has been specifically designed as introduction for newcomers to this exciting research area. This short paper represents a summary of the full paper found online in IEEE Xplore. The latter provides interactive components for a hands-on exploration of the covered material.
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Purpose - This article is going to introduce a modified variant of the imperialist competitive algorithm (ICA). The paper aims to discuss these issues. Design/methodology/approach - ICA is a meta-heuristic algorithm that is introd...
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Purpose - This article is going to introduce a modified variant of the imperialist competitive algorithm (ICA). The paper aims to discuss these issues. Design/methodology/approach - ICA is a meta-heuristic algorithm that is introduced based on a socio-politically motivated global search strategy. It is a population-based stochastic algorithm to control more countries. The most powerful countries are imperialists and the weakest countries are colonies. Colonies movement toward their relevant imperialist, and making a competition among all empires to posses the weakest colonies of the weakest empires, form the basis of the ICA. This fact that the imperialists also need to model and they move towards top imperialist state is the most common type of political rules from around the world. This paper exploits these new ideas. The modification is the empire movement toward the superior empire for balancing the exploration and exploitation abilities of the ICA. Findings - The algorithms are used for optimization that have shortcoming to deal with accuracy rate and local optimum trap and they need complex tuning procedures. MICA is proposed a way for optimizing convex function with high accuracy and avoiding to trap in local optima rather than using original ICA algorithm by implementing some modification on it. Originality/value - Therefore, several solution procedures, including ICA, modified ICA, and genetic algorithm and particle swarm optimization algorithm are proposed. Finally, numerical experiments are carried out to evaluate the effectiveness of models as well as solution procedures. Test results present the suitability of the proposed modified ICA for convex functions with little fluctuations.
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Genetic algorithms are well suited to hybridization. Problem division between components of the hybrid varies by problem type and by the mechanisms included in the hybrid. We propose a hybridization technique for combining genetic...
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Genetic algorithms are well suited to hybridization. Problem division between components of the hybrid varies by problem type and by the mechanisms included in the hybrid. We propose a hybridization technique for combining genetic algorithms (GAs) and deterministic algorithms based on solution candidate partitioning. We conduct a set of experiments to evaluate several instances of this hybridization scheme and demonstrate the efficiency of this hybridization within certain partition ranges for these problems. These results indicate that performance of this hybrid approach is superior to a GA or a deterministic algorithm alone for the problem instances examined, a result that may hold for problems of the same classes as those examined. Finally, we propose topics for future research.
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Optimization and its related solving methods are becoming increasingly important in most academic and industrial fields. The goal of the optimization process is to make a system or a design as effective and functional as possible....
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Optimization and its related solving methods are becoming increasingly important in most academic and industrial fields. The goal of the optimization process is to make a system or a design as effective and functional as possible. This is achieved by optimizing a set of objectives while meeting the system requirements. Optimization techniques are classified into exact and approximate algorithms. Nature-inspired (NI) methods, a sub-class of approximate techniques, are widely recognized for providing efficient approaches for solving a wide variety of real-world optimization problems. In this paper, we discuss many scenarios where we can or cannot use different NI methods in tackling real-world optimization problems. We also enrich our survey with many studies for the reader to prove the efficiency and efficacy of using NI methods to tackle many real-world applications. Therefore, NI methods should be considered as alternative reliable approaches in the absence of exact methods to provide satisfactory solutions.
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This paper introduces a new evolutionary algorithm with the support of an actual quantum processor, a computing device which uses phenomena from quantum mechanics to enable a considerable speed-up in computation. In particular, th...
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This paper introduces a new evolutionary algorithm with the support of an actual quantum processor, a computing device which uses phenomena from quantum mechanics to enable a considerable speed-up in computation. In particular, the proposed approach uses quantum superposition and entanglement to implement quantum evolutionary concepts such as quantum chromosome, entangled crossover, rotation mutation, and quantum elitism, to efficiently perform genetic evolution on quantum devices, and converge towards proper sub-optimal solutions of a given optimization problem. The proposed quantum genetic algorithm has been implemented by using a hybrid hardware architecture, where classical processors interact with the family of quantum processors provided by the IBM Q Experience (R) initiative. As shown in the experimental section, the proposed quantum genetic algorithm's performance highlights that the synergy between quantum and evolutionary computation results in a new and promising bio-inspired optimization strategy. (C) 2021 Elsevier Inc. All rights reserved.
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It is known that the (1 + 1)-EA with mutation rate $c/n$ optimizes every monotone function efficiently if $c < 1$ , and needs exponential time on some monotone functions (HotTopic functions) if $c\geq 2.2$ . We study the same quest...
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It is known that the (1 + 1)-EA with mutation rate $c/n$ optimizes every monotone function efficiently if $c < 1$ , and needs exponential time on some monotone functions (HotTopic functions) if $c\geq 2.2$ . We study the same question for a large variety of algorithms, particularly for the $(1 + \lambda)$ -EA, $(\mu + 1)$ -EA, $(\mu + 1)$ -GA, their “fast” counterparts, and for the $(1 + (\lambda,\lambda))$ -GA. We find that all considered mutation-based algorithms show a similar dichotomy for HotTopic functions, or even for all monotone functions. For the $(1 + (\lambda,\lambda))$ -GA, this dichotomy is in the parameter $c\gamma $ , which is the expected number of bit flips in an individual after mutation and crossover, neglecting selection. For the fast algorithms, the dichotomy is in $m_{2}/m_{1}$ , where $m_{1}$ and $m_{2}$ are the first and second falling moment of the number of bit flips. Surprisingly, the range of efficient parameters is not affected by either population size $\mu $ nor by the offspring population size $\lambda $ . The picture changes completely if crossover is allowed. The genetic algorithms $(\mu + 1)$ -GA and $(\mu + 1)$ -fGA are efficient for arbitrary mutations strengths if $\mu $ is large enough.
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Evolutionary Computation (EC) is field of computer science that borrows concepts such as natural selection and the genotype- phenotype distinction from biology in order to solve a wide range of complex problems, such as robot cont...
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Evolutionary Computation (EC) is field of computer science that borrows concepts such as natural selection and the genotype- phenotype distinction from biology in order to solve a wide range of complex problems, such as robot controller design, job-shop schedule optimization, pattern recognition, electronic circuit design and many more. In addition, EC techniques in combination with individual -based modelling can be applied in their domain of origin, biology, to investigate the emergence and evolution of natural phenomena.
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