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Multidimensional Knapsack Problem: A Fitness Landscape Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008Fitness landscape analysis techniques are used to better understand the influence of genetic representations and associated variation operators when solving a combinatorial optimization problem. Five representations are investigated for the multidimensional knapsack problem. Common mutation operators, such as bit-flip mutation, are employed to generate
Jorge Tavares +2 more
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Distributed random walks for fitness landscape analysis
Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 2020Fitness landscape analysis is used to mathematically characterize optimization problems. In order to perform fitness landscape analysis on continuous-valued optimization problems, a sample of the fitness landscape needs to be taken. A common way to perform this sampling is to use random walk algorithms.
Ryan Dieter Lang, Andries P. Engelbrecht
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Local Landscape Patterns for Fitness Landscape Analysis
2014Almost all problems targeted by evolutionary computation are black-box or heavily complex, and their fitness landscapes usually are unknown. Selection of the appropriate search algorithm and parameters is a crucial topic when the landscape of a given target problem could be unknown in advance.
Shinichi Shirakawa, Tomoharu Nagao
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On the Robustness of Random Walks for Fitness Landscape Analysis
2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019Fitness landscape analysis is used to characterize search landscapes of optimization problems. The objective or fitness function of the optimization problem is extended into a search landscape. Random walk algorithms are the most common methods used to sample these search landscapes and various analyses are performed on the obtained samples in order to
Ryan Dieter Lang, Andries P. Engelbrecht
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A fitness landscape analysis of the travelling thief problem
Proceedings of the Genetic and Evolutionary Computation Conference, 2018Local Optima Networks are models proposed to understand the structure and properties of combinatorial landscapes. The fitness landscape is explored as a graph whose nodes represent the local optima (or basins of attraction) and edges represent the connectivity between them. In this paper, we use this representation to study a combinatorial optimisation
Mohamed El Yafrani +6 more
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Fitness Landscape Analysis of a Simulation Optimisation Problems with HeuristicLab
2011 UKSim 5th European Symposium on Computer Modeling and Simulation, 2011In this paper the fitness landscape of a simulation optimisation problem is analysed within the metaheuristic optimisation framework Heuristic Lab. Computational experiments are performed within an application prototype of a link between the model of a vehicle scheduling problem and the optimisation framework.
Vitaly Bolshakov +2 more
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Fractal Analysis of Fitness Landscapes
2014Complex optimization problems may have fitness landscapes with fractal characteristics. This chapter reviews landscapes obtained from basic artificial test functions as well as cost functions of real application problems which have the property to be fractal. We will discuss the description, structure and complexity of these fractal fitness landscapes.
Ivan Zelinka +2 more
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Fitness Landscape Analysis and Optimization of Coupled Oscillators
Complex Systems, 2006Synchronization in chaotic oscillatory systems has a wide array of applications in biology, physics, and communications systems. Over the past 10 years there has been considerable interest in the synchronization properties of small-world and scale-free networks.
Newth, David, Brede, Markus
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A Fitness Landscape Analysis of the LeNet-5 Loss Function
2022 IEEE Symposium Series on Computational Intelligence (SSCI), 2022Fitness landscape analysis (FLA) refers to a set of techniques that allow for the characterisation, visualisation and comprehension of the trends of objective functions within their decision spaces. Two of the important features estimated through FLA are ruggedness, i.e. the number and distribution of optima within the decision space, and neutrality, i.
Yuyang Zhou, Ferrante Neri
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Meta-learning on flowshop using fitness landscape analysis
Proceedings of the Genetic and Evolutionary Computation Conference, 2019In the context of recommendation methods, meta-learning considers the use of previous knowledge regarding problems solution and performance to indicate the best strategy, whenever it faces a new similar problem. This paper studies the use of meta-learning to recommend local search strategies to solve several instances of permutation flowshop problems ...
Pavelski, Lucas Marcondes +2 more
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