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Handbook of Heuristics, 2018 Evolutionary algorithms (EAs) are population-based metaheuristics, originally inspired by aspects of natural evolution. Modern varieties incorporate a broad mixture of search mechanisms, and tend to blend inspiration from nature with pragmatic ...
Corne, David W., Lones, Michael A.
core +4 more sources
Algorithmicity of Evolutionary Algorithms [PDF]
Studies in Logic, Grammar and Rhetoric, 2020 In the first part of our article we will refer the penetration of scientific terms into colloquial language, focusing on the sense in which the concept of an algorithm currently functions outside its original scope.
Leciejewski Sławomir +1 more
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Evaluating evolutionary algorithms
Artificial Intelligence, 1996 AbstractTest functions are commonly used to evaluate the effectiveness of different search algorithms. However, the results of evaluation are as dependent on the test problems as they are on the algorithms that are the subject of comparison. Unfortunately, developing a test suite for evaluating competing search algorithms is difficult without clearly ...
Darrell Whitley +3 more
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arXiv, 2015 This manuscript contains an outline of lectures course "Evolutionary Algorithms" read by the author. The course covers Canonic Genetic Algorithm and various other genetic algorithms as well as evolutionary strategies, genetic programming, tabu search and the class of evolutionary algorithms in general.
arxiv +3 more sources
Parameter Control in Evolutionary Algorithms [PDF]
Parameter Setting in Evolutionary Algorithms, 2007 The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed during evolution. We provide a classification of different approaches based on a number of complementary
Eiben, Aguston +3 more
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On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling [PDF]
arXiv, 2015 Evolutionary algorithms have been frequently used for dynamic optimization problems. With this paper, we contribute to the theoretical understanding of this research area. We present the first computational complexity analysis of evolutionary algorithms for a dynamic variant of a classical combinatorial optimization problem, namely makespan scheduling.
Neumann, Frank, Witt, Carsten
arxiv +4 more sources
A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization [PDF]
IEEE Transactions on Evolutionary Computation, 2022 Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable
Songbai Liu +3 more
semanticscholar +1 more source
Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods. [PDF]
Sci Rep, 2018 Bui DT +10 more
europepmc +2 more sources
Load Frequency Control for Multi-Area Power Plants with Integrated Wind Resources
Applied Sciences, 2021 To provide a more practical method of controlling the frequency and tie-line power flow of a multi-area interconnected power system (MAIPS), a state observer based on sliding mode control (SOboSMC) acting under a second-order time derivative is proposed.
Van Van Huynh +7 more
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Journal of Information and Telecommunication, 2023 With the continuous development of a society in which construction works are ubiquitous, investors often accelerate the progress of construction projects and may neglect to follow the proposed construction processes.
Tran Anh Khoa +2 more
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