Concurrency is a powerful abstraction that can be used to model and implement multi-deme evolutionary algorithms, opening up additional design questions such as what the different populations in various threads can do...
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This paper proposes an approach to identify relevant parameter combinations within Logical Scenarios for the verification and validation (VV) of automated driving systems (ADS). One approach to potentially reach the g...
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Parallel evolutionary algorithms (PEAs) have been studied for reducing the execution time of evolutionary algorithms by utilizing parallel computing. An asynchronous PEA (APEA) is a scheme of PEAs that increases compu...
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We apply a hybrid evolutionary algorithm to minimize the depth of circuits in quantum computing. More specifically, we evaluate two different variants of the algorithm. In the first approach, we combine the evolutiona...
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It is natural to think of evolutionary algorithms as highly stochastic search methods. This can also make evolutionary algorithms, and particularly recombination, quite difficult to analyze. One way to reduce randomne...
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ISBN:
(纸本)9781450371285
It is natural to think of evolutionary algorithms as highly stochastic search methods. This can also make evolutionary algorithms, and particularly recombination, quite difficult to analyze. One way to reduce randomness involves the quadratization of functions, which is commonly used by modern optimization methods, and also has applications in quantum computing. After a function is made quadratic, random mutation is obsolete and unnecessary;the location of improving moves can be calculated deterministically, on average in O(1) time. Seemingly impossible problems, such as the Needle-in-a-Haystack, becomes trivial to solve in quadratic form. One can also provably tunnel, or jump, between local optima and quasilocal optima in O(n) time using deterministic genetic recombination. The talk also explores how removing randomness from evolutionary algorithms might provide new insights into natural evolution. Finally, a form of evolutionary algorithm is proposed where premature convergence is impossible and the evolutionary potential of the population remains open-ended.
Scania has been working with statistics for a long time but has invested in becoming a data driven company more recently and uses data science in almost all business functions. The algorithms developed by the data sci...
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Scania has been working with statistics for a long time but has invested in becoming a data driven company more recently and uses data science in almost all business functions. The algorithms developed by the data scientists need to be optimized to be fully utilized and traditionally this is a manual and time consuming process. What this thesis investigates is if and how well evolutionary algorithms can be used to automate the optimization process. The evaluation was done by implementing and analyzing four variations of genetic algorithms with different levels of complexity and tuning parameters. The algorithm subject to optimization was XGBoost, a gradient boosted tree model, applied to data that had previously been modelled in a competition. The results show that evolutionary algorithms are applicable in finding good models but also emphasizes the importance of proper data preparation.
The Minimum Spanning Tree problem (abbr. MSTP) is a well-known combinatorial optimization problem that has been extensively studied by the researchers in the field of evolutionary computing to theoretically analyze th...
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Although differential evolution (DE) algorithms perform well on a large variety of complicated optimization problems, only a few theoretical studies are focused on the working principal of DE algorithms. To make the f...
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Many biological processes have been the source of inspiration for heuristic methods that generate high-quality solutions to solve optimization and search problems. This thesis presents an epigenetic technique for Evol...
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evolutionary algorithms have been used recently as an alternative in image registration, especially in cases where the similarity function is non-convex with many local optima. However, their drawback is that they ten...
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evolutionary algorithms have been used recently as an alternative in image registration, especially in cases where the similarity function is non-convex with many local optima. However, their drawback is that they tend to be computationally expensive. Trying to avoid local minima can increase the computational cost. The purpose of authors' research is to minimise the duration of the image registration process. This paper presents a method to minimise the computational cost by introducing a machine learning-based variant of Harmony Search. To this end, a series of machine-learning regression methods are tested in order to find the most appropriate that minimises the cost without degrading the quality of the results. The best regression method is then incorporated in the optimisation process and is compared with two well-known ITK image registration methods. The comparison of authors' image registration method with ITK concerns both the quality of the results and the duration of the registration experiments. The comparison is done on a set of random image pairs of various sources (e.g. medical or satellite images), and the encouraging results strongly indicate that authors' method can be used in a variety of image registration applications producing quality results in significantly less time.
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